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A state of the art review of intelligent scheduling

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Abstract

Intelligent scheduling covers various tools and techniques for successfully and efficiently solving the scheduling problems. In this paper, we provide a survey of intelligent scheduling systems by categorizing them into five major techniques containing fuzzy logic, expert systems, machine learning, stochastic local search optimization algorithms and constraint programming. We also review the application case studies of these techniques.

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References

  • Abd KK (2015) Intelligent scheduling of robotic flexible assembly cells. Springer, Berlin

    MATH  Google Scholar 

  • Abdennadher S, Schlenker H (1999) Nurse scheduling using constraint logic programming. In: AAAI/IAAI

  • Abdullah S, Abdolrazzagh-Nezhad M (2014) Fuzzy job shop scheduling problems: a review. Inf Sci 278:380–407

    Article  MathSciNet  MATH  Google Scholar 

  • Abdullah M, Othman M (2014) Simulated annealing approach to cost-based multi-quality of service job scheduling in cloud computing enviroment. Am J Appl Sci 11(6):872

    Article  Google Scholar 

  • Adamopoulos GI, Pappis CP (1996) A fuzzy-linguistic approach to a multi-criteria sequencing problem. Eur J Oper Res 92(3):628–636

    Article  MATH  Google Scholar 

  • Agarwal A, Jacob VS, Pirkul H (2006) An improved augmented neural-network approach for scheduling problems. INFORMS J Comput 18(1):119–128

    Article  MathSciNet  MATH  Google Scholar 

  • Aggoun A, Vazacopoulos A (2004) Solving sports scheduling and timetabling problems with constraint programming. In: Economics, management and optimization in sports. Springer, Berlin, pp 243–264

    Chapter  MATH  Google Scholar 

  • Ahani G, Asyabani M (2014) A tabu search algorithm for no-wait job shop scheduling problem. Int J Oper Res 19(2):246–258

    Article  MathSciNet  MATH  Google Scholar 

  • Ahmadizar F, Hosseini L (2011) Single-machine scheduling with a position-based learning effect and fuzzy processing times. Int J Adv Manuf Technol 56(5–8):693–698

    Article  Google Scholar 

  • Ahmadizar F, Hosseini L (2013) Minimizing makespan in a single-machine scheduling problem with a learning effect and fuzzy processing times. Int J Adv Manuf Technol 65(1–4):581–587

    Article  Google Scholar 

  • AitZai A, Benmedjdoub B, Boudhar M (2016) Branch-and-bound and PSO algorithms for no-wait job shop scheduling. J Intell Manuf 27(3):679–688

    Article  MATH  Google Scholar 

  • Akyol DE, Bayhan GM (2007) A review on evolution of production scheduling with neural networks. Comput Ind Eng 53(1):95–122

    Article  Google Scholar 

  • Akyol DE, Mirac Bayhan G (2008) Multi-machine earliness and tardiness scheduling problem: an interconnected neural network approach. Int J Adv Manuf Technol 37(5-6):576–588

    Article  Google Scholar 

  • Alcan P, BaşLıGil H (2012) A genetic algorithm application using fuzzy processing times in non-identical parallel machine scheduling problem. Adv Eng Softw 45(1):272–280

    Article  Google Scholar 

  • Al-Turki U, Fedjki C, Andijani A (2001) Tabu search for a class of single-machine scheduling problems. Comput Oper Res 28(12):1223–1230

    Article  MathSciNet  MATH  Google Scholar 

  • Alvarez-Valdes R, Crespo E, Tamarit JM (2002) Design and implementation of a course scheduling system using Tabu search. Eur J Oper Res 137(3):512–523

    Article  MATH  Google Scholar 

  • Ambika G, Uthra G (2014) Branch and bound technique in flow shop scheduling using fuzzy processing times. Ann Pure Appl Math 8:37–42

    Google Scholar 

  • Anderson JA (1995) An introduction to neural networks. MIT press, Cambridge

    Book  MATH  Google Scholar 

  • Anghinolfi D, Paolucci M (2009) A new discrete particle swarm optimization approach for the single-machine total weighted tardiness scheduling problem with sequence-dependent setup times. Eur J Oper Res 193(1):73–85

    Article  MATH  Google Scholar 

  • Anglani A, Grieco A, Guerriero E, Musmanno R (2005) Robust scheduling of parallel machines with sequence-dependent set-up costs. Eur J Oper Res 161(3):704–720

    Article  MathSciNet  MATH  Google Scholar 

  • Arizono I, Yamamoto A, Ohta H (1992) Scheduling for minimizing total actual flow time by neural networks. Int J Prod Res 30(3):503–511

    Article  MATH  Google Scholar 

  • Armentano VA, Ronconi DP (1999) Tabu search for total tardiness minimization in flow shop scheduling problems. Comput Oper Res 26(3):219–235

    Article  MathSciNet  MATH  Google Scholar 

  • Asokan P, Jerald J, Arunachalam S, Page T (2008) Application of adaptive genetic algorithm and particle swarm optimisation in scheduling of jobs and AS/RS in FMS. Int J Manuf Res 3(4):393–405

    Article  Google Scholar 

  • Aydin ME, Fogarty TC (2004) A simulated annealing algorithm for multi-agent systems: a job shop scheduling application. J Intell Manuf 15(6):805–814

    Article  Google Scholar 

  • Azadeh A et al (2010) A flexible artificial neural network–fuzzy simulation algorithm for scheduling a flow shop with multiple processors. Int J Adv Manuf Technol 50(5–8):699–715

    Article  Google Scholar 

  • Azadeh A, Negahban A, Moghaddam M (2012a) A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems. Int J Prod Res 50(2):551–566

    Article  Google Scholar 

  • Azadeh A et al (2012b) An integrated neural network–simulation algorithm for performance optimisation of the bi-criteria two-stage assembly flow shop scheduling problem with stochastic activities. Int J Prod Res 50(24):7271–7284

    Article  Google Scholar 

  • Azadeh A et al (2015) A hybrid computer simulation-adaptive neuro-fuzzy inference system algorithm for optimization of dispatching rule selection in job shop scheduling problems under uncertainty. Int J Adv Manuf Technol 79(1–4):135–145

    Article  Google Scholar 

  • Bagherpour M, Noghondarian K, Noori S (2007) Applying fuzzy logic to estimate setup times in sequence dependent single machine scheduling problems. Int J Comput Sci Netw Secur 7:111–118

    Google Scholar 

  • Balci HH, Valenzuela JF (2004) Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method. Int J Appl Math Comput Sci 14(3):411–422

    MathSciNet  MATH  Google Scholar 

  • Balin S (2011) Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation. Inf Sci 181(17):3551–3569

    Article  Google Scholar 

  • Bank M, Ghomi SF, Jolai F, Behnamian J (2012) Application of particle swarm optimization and simulated annealing algorithms in flow shop scheduling problem under linear deterioration. Adv Eng Softw 47(1):1–6

    Article  Google Scholar 

  • Baptiste P, Le Pape C, Nuijten W (2012) Constraint-based scheduling: applying constraint programming to scheduling problems, vol 39. Springer, Berlin

    MATH  Google Scholar 

  • Barán B, von Lücken C, Sotelo A (2005) Multi-objective pump scheduling optimisation using evolutionary strategies. Adv Eng Softw 36(1):39–47

    Article  MATH  Google Scholar 

  • Baykasoğlu A, Sönmez AI (2004) Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling problems. J Intell Manuf 15(6):777–785

    Article  Google Scholar 

  • Baykasoğlu A, Göçken M, Özbakır L, Kulluk S (2008) Composite dispatching rule generation through data mining in a simulated job shop. In: Modelling, computation and optimization in information systems and management sciences, Springer, Berlin, pp 389–398

    MATH  Google Scholar 

  • Beck Jc, Feng TK, Watson jp (2011) Combining constraint programming and local search for job-shop scheduling. INFORMS J Comput 23(1):1–14

    Article  MathSciNet  MATH  Google Scholar 

  • Behnamian J, Ghomi SMTF (2014) Multi-objective fuzzy multiprocessor flow shop scheduling. Appl Soft Comput 21:139–148

    Article  Google Scholar 

  • Bell J (2014) Machine learning: hands-on for developers and technical professionals. Wiley, New York

    Book  Google Scholar 

  • Benini L et al (2008) A constraint programming approach for allocation and scheduling on the cell broadband engine. In: International conference on principles and practice of constraint programming, Springer, Berlin

  • Berral JL, Goiri Í, Nou R, Julià F, Guitart J, Gavaldà R, Torres J (2010) Towards energy-aware scheduling in data centers using machine learning. In: Proceedings of the 1st international conference on energy-efficient computing and networking, ACM, pp 215–224

  • Berrichi A et al (2010) Bi-objective ant colony optimization approach to optimize production and maintenance scheduling. Comput Oper Res 37(9):1584–1596

    Article  MathSciNet  MATH  Google Scholar 

  • Berthold T et al (2010) A constraint integer programming approach for resource-constrained project scheduling. In: International conference on integration of artificial intelligence (AI) and operations research (OR) techniques in constraint programming, Springer, Berlin

    Chapter  MATH  Google Scholar 

  • Bezirgan A (1992) A case-based reasoning approach to dynamic job shop scheduling. In: Ai’92-proceedings of the 5th Australian joint conference on artificial intelligence, World Scientific, p 233

  • Biegel JE, Davern JJ (1990) Genetic algorithms and job shop scheduling. Comput Ind Eng 19(1–4):81–91

    Article  Google Scholar 

  • Bilkay O, Anlagan O, Kilic SE (2004) Job shop scheduling using fuzzy logic. Int J Adv Manuf Technol 23(7–8):606–619

    Article  Google Scholar 

  • Bilolikar VS, Jain K, Sharma M (2016) An adaptive crossover genetic algorithm with simulated annealing for multi mode resource constrained project scheduling with discounted cash flows. Int J Oper Res 25(1):28–46

    Article  MathSciNet  MATH  Google Scholar 

  • Blum C (2005) Beam-ACO—hybridizing ant colony optimization with beam search: an application to open shop scheduling. Comput Oper Res 32(6):1565–1591

    Article  MATH  Google Scholar 

  • Blum C, Sampels M (2004) An ant colony optimization algorithm for shop scheduling problems. J Math Model Algorithms 3(3):285–308

    Article  MathSciNet  MATH  Google Scholar 

  • Bochtis DD (2010) Machinery management in bio-production systems: planning and scheduling aspects. Agric Eng Int CIGR J 12(2):55–63

    Google Scholar 

  • Booth KEC, Nejat G, Christopher Beck J (2016) A constraint programming approach to multi-robot task allocation and scheduling in retirement homes. In: International conference on principles and practice of constraint programming, Springer, Cham

    Google Scholar 

  • Bourdais S, Galinier P, Pesant G (2003) “HIBISCUS: a constraint programming application to staff scheduling in health care. In: International conference on principles and practice of constraint programming, Springer, Berlin

    Chapter  Google Scholar 

  • Bourenane M, Mellouk A (2014) Inductive approaches for packet scheduling in communication networks. Real Time Syst Sched 2:151–193

    Google Scholar 

  • Bożejko W, Pempera J, Wodecki M (2015) Parallel simulated annealing algorithm for cyclic flexible job shop scheduling problem. In: International conference on artificial intelligence and soft computing, Springer International Publishing, pp 603–612

  • Brandao J, Mercer A (1997) A tabu search algorithm for the multi-trip vehicle routing and scheduling problem. Eur J Oper Res 100(1):180–191

    Article  MATH  Google Scholar 

  • Brown DE, Marin JA, Scherer WT (1995a) A survey of intelligent scheduling systems. In: Intelligent scheduling systems, Springer, New York, pp 1–40

    Chapter  Google Scholar 

  • Brown DE, John AM, William TS (1995b) A survey of intelligent scheduling systems. In: Intelligent scheduling systems, Springer, Boston, pp 1–40

    Chapter  Google Scholar 

  • Brusco MJ, Jacobs LW (1993) A simulated annealing approach to the cyclic staff-scheduling problem. Naval Res Log (NRL) 40(1):69–84

    Article  MATH  Google Scholar 

  • Cai X, Li KN (2000) A genetic algorithm for scheduling staff of mixed skills under multi-criteria. Eur J Oper Res 125(2):359–369

    Article  MATH  Google Scholar 

  • Carlsson M, Johansson M, Larson J (2017) Scheduling double round-robin tournaments with divisional play using constraint programming. Eur J Oper Res 259(3):1180–1190

    Article  MathSciNet  MATH  Google Scholar 

  • Chan WT, Hao H (2002) Constraint programming approach to precast production scheduling. J Constr Eng Manag 128(6):513–521

    Article  Google Scholar 

  • Chan FT, Chung SH, Chan PLY (2005) An adaptive genetic algorithm with dominated genes for distributed scheduling problems. Expert Syst Appl 29(2):364–371

    Article  Google Scholar 

  • Chan FT, Prakash A, Ma HL, Wong CS (2013) A hybrid Tabu sample-sort simulated annealing approach for solving distributed scheduling problem. Int J Prod Res 51(9):2602–2619

    Article  Google Scholar 

  • Chanas S, Kasperski A (2001) Minimizing maximum lateness in a single machine scheduling problem with fuzzy processing times and fuzzy due dates. Eng Appl Artif Intell 14(3):377–386

    Article  Google Scholar 

  • Chanas S, Kasperski A (2004) Possible and necessary optimality of solutions in the single machine scheduling problem with fuzzy parameters. Fuzzy Sets Syst 142(3):359–371

    Article  MathSciNet  MATH  Google Scholar 

  • Chang P-C, Hsieh J-C, Wang Y-W (2005) Genetic algorithm and case-based reasoning applied in production scheduling. In: Knowledge incorporation in evolutionary computation, Springer, Berlin, pp. 215–236

    Chapter  Google Scholar 

  • Chang PC, Huang WH, Wu JL, Cheng TCE (2013) A block mining and re-combination enhanced genetic algorithm for the permutation flow shop scheduling problem. Int J Prod Econ 141(1):45–55

    Article  Google Scholar 

  • Chang FS, Wu JS, Lee CN, Shen HC (2014) Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling. Expert Syst Appl 41(6):2947–2956

    Article  Google Scholar 

  • Chen R-M (2011a) Reducing network and computation complexities in neural based real-time scheduling scheme. Appl Math Comput 217(13):6379–6389

    MathSciNet  MATH  Google Scholar 

  • Chen R-M (2011b) Particle swarm optimization with justification and designed mechanisms for resource-constrained project scheduling problem. Expert Syst Appl 38(6):7102–7111

    Article  Google Scholar 

  • Chen RM, Huang YM (1998) Multiconstraint task scheduling in multi-processor system by neural network. In: Proceedings of tenth IEEE international conference on tools with artificial intelligence, IEEE, pp 288–294

  • Chen LS, Su CT (2008) Using granular computing model to induce scheduling knowledge in dynamic manufacturing environments. Int J Comput Integr Manuf 21(5):569–583

    Article  Google Scholar 

  • Chen W-N, Zhang J (2013) Ant colony optimization for software project scheduling and staffing with an event-based scheduler. IEEE Trans Softw Eng 39(1):1–17

    Article  Google Scholar 

  • Chen N, Li C, Qin P (1998) KDPAG expert system applied to materials design and manufacture. Eng Appl Artif Intell 11(5):669–674

    Article  Google Scholar 

  • Chen CC, Yih Y, Wu YC (1999) Auto-bias selection for developing learning-based scheduling systems. Int J Prod Res 37(9):1987–2002

    Article  MATH  Google Scholar 

  • Chen L et al (2007) A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal. Eur J Oper Res 181(1):40–58

    Article  MathSciNet  MATH  Google Scholar 

  • Chen J-p et al (2008) Study on application of CBR in steelmaking and continuous casting dynamic scheduling system. Metall Ind Autom 2:011

    Google Scholar 

  • Chen JC, Wu CC, Chen CW, Chen KH (2012) Flexible job shop scheduling with parallel machines using genetic algorithm and grouping genetic algorithm. Expert Syst Appl 39(11):10016–10021

    Article  Google Scholar 

  • Chen B et al (2014a) Adaptive immune-genetic algorithm for fuzzy job shop scheduling problems. In: International conference in swarm intelligence, Springer International Publishing

  • Chen C-L et al (2014b) A revised discrete particle swarm optimization algorithm for permutation flow shop scheduling problem. Soft Comput 18(11):2271–2282

    Article  Google Scholar 

  • Chen JC, Chen YY, Chen TL, Lin JZ (2016) Comparison of simulated annealing and tabu-search algorithms in advanced planning and scheduling systems for TFT-LCD colour filter fabs. Int J Comput Integr Manuf 30:1–19

    Google Scholar 

  • Cheng R, Gen M (1997) Parallel machine scheduling problems using memetic algorithms. Comput Ind Eng 33(3):761–764

    Article  Google Scholar 

  • Cheng B-Y, Leung Joseph Y-T, Li K (2015) Integrated scheduling of production and distribution to minimize total cost using an improved ant colony optimization method. Comput Ind Eng 83:217–225

    Article  Google Scholar 

  • Chiang TC, Lin HJ (2011) Flexible job shop scheduling using a multiobjective memetic algorithm. In: International conference on intelligent computing, Springer, Berlin, pp 49–56

    Chapter  Google Scholar 

  • Chiang TC, Lin HJ (2013) A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling. Int J Prod Econ 141(1):87–98

    Article  Google Scholar 

  • Cho JH, Kim YD (1997) A simulated annealing algorithm for resource constrained project scheduling problems. J Oper Res Soc 48(7):736–744

    Article  MATH  Google Scholar 

  • Choobineh F Fred, Mohebbi E, Khoo H (2006) A multi-objective tabu search for a single-machine scheduling problem with sequence-dependent setup times. Eur J Oper Res 175(1):318–337

    Article  MATH  Google Scholar 

  • Chung SH, Choy KL (2012) A modified genetic algorithm for quay crane scheduling operations. Expert Syst Appl 39(4):4213–4221

    Article  Google Scholar 

  • Ciro GC et al (2015) A fuzzy ant colony optimization to solve an open shop scheduling problem with multi-skills resource constraints. IFAC-PapersOnLine 48(3):715–720

    Article  Google Scholar 

  • Clerc M (2010) Particle swarm optimization, vol 93. Wiley, New York

    MATH  Google Scholar 

  • Cochran JK, Horng SM, Fowler JW (2003) A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines. Comput Oper Res 30(7):1087–1102

    Article  MathSciNet  MATH  Google Scholar 

  • Coello JMA, Camilo dos Santos R (1998) Integrating CBR and heuristic search to solve complex real-time scheduling problems. AAAI Technical Report WS

  • Coello CA, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, vol 5. Springer, New York

    MATH  Google Scholar 

  • Costa D (1995) An evolutionary tabu search algorithm and the NHL scheduling problem. INFOR Inf Syst Oper Res 33(3):161–178

    MATH  Google Scholar 

  • Cotta C, Fernández AJ (2007) Memetic algorithms in planning, scheduling, and timetabling. In: Evolutionary scheduling, Springer, Berlin, pp 1–30

    MATH  Google Scholar 

  • Cotta C, Dotú I, Fernández AJ, Van Hentenryck P (2006) Scheduling social golfers with memetic evolutionary programming. In: International workshop on hybrid metaheuristics, Springer, Berlin, pp 150–161

    Chapter  Google Scholar 

  • Custodio LM, Sentieiro JJ, Bispo CF (1994) Production planning and scheduling using a fuzzy decision system. IEEE Trans Robot Autom 10(2):160–168

    Article  Google Scholar 

  • Czyzżak P, Jaszkiewicz A (1998) Pareto simulated annealing—a metaheuristic technique for multiple-objective combinatorial optimization. J Multicrit Decis Anal 7(1):34–47

    Article  MATH  Google Scholar 

  • Damm RB, Resende MG, Ronconi DP (2016) A biased random key genetic algorithm for the field technician scheduling problem. Comput Oper Res 75:49–63

    Article  MathSciNet  MATH  Google Scholar 

  • Damodaran P, Vélez-Gallego MC (2012) A simulated annealing algorithm to minimize makespan of parallel batch processing machines with unequal job ready times. Expert Syst Appl 39(1):1451–1458

    Article  Google Scholar 

  • Darwin C (1859) On the origin of species, 1st edn. Harvard University Press, Cambridge

    Google Scholar 

  • Davidrajuh R (2001) Automating supplier selection procedures. PhD Dissertation, Norwegian University of Science and Technology (NTNU), Narvik Institute of Technology Narvik, Norway

  • Dawkins R (1986) The blind watchmaker. Penguin, London

    Google Scholar 

  • Deal DE, Chen JG, Ignizio JP, Jeyakumar V (1990) An expert system scheduler: some reflections on expert systems development. Comput Oper Res 17(6):571–580

    Article  Google Scholar 

  • De Toni A, Nassimbeni G, Tonchia S (1996) An artificial, intelligence-based production scheduler. Integr Manuf Syst 7(3):17–25

    Article  Google Scholar 

  • Del Valle C et al (2003) On selecting and scheduling assembly plans using constraint programming. In: International conference on knowledge-based and intelligent information and engineering systems, Springer, Berlin

  • Della Croce F, Tadei R, Volta G (1995) A genetic algorithm for the job shop problem. Comput Oper Res 22(1):15–24

    Article  MATH  Google Scholar 

  • Deng G-F, Lin W-T (2011) Ant colony optimization-based algorithm for airline crew scheduling problem. Expert Syst Appl 38(5):5787–5793

    Article  Google Scholar 

  • Deng J, Wang L, Wu C, Wang J, Zheng X (2016) A competitive memetic algorithm for carbon-efficient scheduling of distributed flow shop. In: International conference on intelligent computing, Springer International Publishing, pp 476–488

  • Dong B, Jiao L, Jianshe W (2015) A two-phase knowledge based hyper-heuristic scheduling algorithm in cellular system. Knowl Based Syst 88:244–252

    Article  Google Scholar 

  • Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2-3):243–278

    Article  MathSciNet  MATH  Google Scholar 

  • Doulabi H, Hossein S, Rousseau L-M, Pesant G (2016) A constraint-programming-based branch-and-price-and-cut approach for operating room planning and scheduling. INFORMS J Comput 28(3):432–448

    Article  MathSciNet  MATH  Google Scholar 

  • Dowsland KA (1998) Nurse scheduling with tabu search and strategic oscillation. Eur J Oper Res 106(2):393–407

    Article  MathSciNet  MATH  Google Scholar 

  • Dowsland KA, Thompson JM (2005) Ant colony optimization for the examination scheduling problem. J Oper Res Soc 56(4):426–438

    Article  MATH  Google Scholar 

  • Dubois D, Fargier H, Prade H (1995) Fuzzy constraints in job shop scheduling. J Intell Manuf 6(4):215–234

    Article  Google Scholar 

  • Duenas A, Petrovic D (2008) Multi-objective genetic algorithm for single machine scheduling problem under fuzziness. Fuzzy Optim Decis Mak 7(1):87–104

    Article  MathSciNet  MATH  Google Scholar 

  • Dugardin F, Amodeo L, Yalaoui F (2011) Fuzzy Lorenz Ant Colony System to solve multiobjective reentrant hybride flow shop scheduling problem. In: 2011 International conference on communications, computing and control applications (CCCA), IEEE

  • Dumais S et al (1998) Inductive learning algorithms and representations for text categorization. In: Proceedings of the seventh international conference on information and knowledge management, ACM

  • Dzeng RJ, Lee HY (2004) Critiquing contractors’ scheduling by integrating rule-based and case-based reasoning. Autom Constr 13(5):665–678

    Article  Google Scholar 

  • Edis EB, Oguz C (2011) Parallel machine scheduling with additional resources: a lagrangian-based constraint programming approach. In: International conference on AI and OR techniques in constriant programming for combinatorial optimization problems, Springer, Berlin

    Chapter  MATH  Google Scholar 

  • Edis EB, Ozkarahan I (2011) A combined integer/constraint programming approach to a resource-constrained parallel machine scheduling problem with machine eligibility restrictions. Eng Optim 43(2):135–157

    Article  MathSciNet  Google Scholar 

  • Elkhyari A, Guéret C, Jussien N (2004) Constraint programming for dynamic scheduling problems. Hiroshi Kise, editor 04:84–89

    Google Scholar 

  • Elloumi S, Fortemps P (2010) A hybrid rank-based evolutionary algorithm applied to multi-mode resource-constrained project scheduling problem. Eur J Oper Res 205(1):31–41

    Article  MathSciNet  MATH  Google Scholar 

  • Elmi A, Topaloglu S (2016) Multi-degree cyclic flow shop robotic cell scheduling problem: ant colony optimization. Comput Oper Res 73:67–83

    Article  MathSciNet  MATH  Google Scholar 

  • Ergazakis K, Metaxiotis K, Samouilidis E, Psarras J (2003) Decision support through knowledge management: the role of the artificial intelligence. Inf Manag Comput Sec 11(5):216–221

    Article  Google Scholar 

  • Esquivel S, Ferrero S, Gallard R, Salto C, Alfonso H, Schütz M (2002) Enhanced evolutionary algorithms for single and multiobjective optimization in the job shop scheduling problem. Knowl Based Syst 15(1):13–25

    Article  Google Scholar 

  • Eswaramurthy VP, Tamilarasi A (2009) Hybridizing tabu search with ant colony optimization for solving job shop scheduling problems. Int J Adv Manuf Technol 40(9-10):1004–1015

    Article  MATH  Google Scholar 

  • Even C, Schutt A, Van Hentenryck P (2015) A constraint programming approach for non-preemptive evacuation scheduling. In: International conference on principles and practice of constraint programming, Springer, Cham

    Google Scholar 

  • Ezugwu AE et al (2016) Neural network-based multi-agent approach for scheduling in distributed systems. Concurr Comput Pract Exp 29:3887

    Article  Google Scholar 

  • Fangming G, Qiong L (2009) A hybrid PSO algorithm for job shop scheduling problems with fuzzy processing time and fuzzy due date. In: 2009 Fifth international conference on natural computation, vol 3, IEEE

  • Fayad C, Petrovic S (2005) A fuzzy genetic algorithm for real-world job shop scheduling. In: International conference on industrial, engineering and other applications of applied intelligent systems, Springer, Berlin

    Chapter  Google Scholar 

  • Feng L, Chen B, Gu H, Chunsheng G (2006) The research of fuzzy flexible job shop scheduling problem based on interval-valued fuzzy set. Comput Eng Appl 5:017

    Google Scholar 

  • Fortemps P (1995) Job shop scheduling problems with fuzzy or flexible durations. In: 1995 INRIA/IEEE symposium on emerging technologies and factory automation, 1995. ETFA’95, Proceedings, vol. 2, IEEE

  • Fortemps P (1997) Job shop scheduling with imprecise durations: a fuzzy approach. IEEE Trans Fuzzy Syst 5(4):557–569

    Article  Google Scholar 

  • Fox MS, Smith SF (1984) ISIS—a knowledge-based system for factory scheduling. Expert Syst 1(1):25–49

    Article  Google Scholar 

  • França PM et al (1996) A tabu search heuristic for the multiprocessor scheduling problem with sequence dependent setup times. Int J Prod Econ 43(2):79–89

    Article  Google Scholar 

  • França PM, Mendes A, Moscato P (2001) A memetic algorithm for the total tardiness single machine scheduling problem. Eur J Oper Res 132(1):224–242

    Article  MathSciNet  MATH  Google Scholar 

  • Frutos M, Olivera AC, Tohmé F (2010) A memetic algorithm based on a NSGAII scheme for the flexible job shop scheduling problem. Ann Oper Res 181(1):745–765

    Article  MathSciNet  Google Scholar 

  • Gao J, Gen M, Sun L, Zhao X (2007) A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Comput Ind Eng 53(1):149–162

    Article  Google Scholar 

  • Gao J, Chen R, Deng W (2013) An efficient tabu search algorithm for the distributed permutation flow shop scheduling problem. Int J Prod Res 51(3):641–651

    Article  Google Scholar 

  • Gao KZ et al (2016) Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion. Knowl Based Syst 109:1–16

    Article  Google Scholar 

  • Garrido A, Onaindia E, Sapena O (2008) Planning and scheduling in an e-learning environment. A constraint-programming-based approach. Eng Appl Artif Intell 21(5):733–743

    Article  Google Scholar 

  • Garrido A, Arangu M, Onaindia E (2009) A constraint programming formulation for planning: from plan scheduling to plan generation. J Sched 12(3):227–256

    Article  MathSciNet  MATH  Google Scholar 

  • Ge HW et al (2005) A particle swarm optimization-based algorithm for job shop scheduling problems. Int J Comput Methods 2(03):419–430

    Article  MATH  Google Scholar 

  • Gedik R et al (2018) A constraint programming approach for solving unrelated parallel machine scheduling problem. Comput Ind Eng 121:139–149

    Article  Google Scholar 

  • Gersmann K, Hammer B (2004) A reinforcement learning algorithm to improve scheduling search heuristics with the svm. In: Proceedings of 2004 IEEE international joint conference on neural networks, vol 3, IEEE, pp 1811–1816

  • Geske U (2005) Railway scheduling with declarative constraint programming. In: International conference on applications of declarative programming and knowledge management, Springer, Berlin

  • Gharehgozli AH, Tavakkoli-Moghaddam R, Zaerpour N (2009) A fuzzy-mixed-integer goal programming model for a parallel-machine scheduling problem with sequence-dependent setup times and release dates. Robot Comput Integr Manuf 25(4):853–859

    Article  Google Scholar 

  • Glover FW, Kochenberger GA (eds) (2006) Handbook of metaheuristics, vol 57. Springer, Berlin

    MATH  Google Scholar 

  • Glover F, Laguna M (1989) Target analysis to improve a tabu search method for machine scheduling. Working papers on artificial intelligence in management science, vol 1, pp 56–74

  • Goel V et al (2015) Constraint programming for LNG ship scheduling and inventory management. Eur J Oper Res 241(3):662–673

    Article  MathSciNet  MATH  Google Scholar 

  • Gogna A, Tayal A (2013) Metaheuristics: review and application. J Exp Theor Artif Intell 25(4):503–526

    Article  Google Scholar 

  • Gökgür B, Hnich B, Özpeynirci S (2018) Parallel machine scheduling with tool loading: a constraint programming approach. Int J Prod Res 30:1–17

    Google Scholar 

  • Gomes CP, van Hoeve VJ, Selman B (2006) Constraint programming for distributed planning and scheduling. In: AAAI spring symposium: distributed plan and schedule management, vol 1

  • Gonçalves JF, Resende MG (2014) An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling. Int Trans Oper Res 21(2):215–246

    Article  MathSciNet  MATH  Google Scholar 

  • Gonçalves JF, Mendes JDM, Resende MG (2008) A genetic algorithm for the resource constrained multi-project scheduling problem. Eur J Oper Res 189(3):1171–1190

    Article  MATH  Google Scholar 

  • González-Rodríguez I et al (2010) Heuristic local search for fuzzy open shop scheduling. In: 2010 IEEE International conference on fuzzy systems (FUZZ), IEEE

  • Gorrini V, Dorigo M (1995) An application of evolutionary algorithms to the scheduling of robotic operations. In: European conference on artificial evolution, Springer, Berlin, pp 345–354

    Google Scholar 

  • Graham RL et al (1979) Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann Discrete Math 5:287–326

    Article  MathSciNet  MATH  Google Scholar 

  • Greenwood GW, Gupta AK, McSweeney K (1994) Scheduling tasks in multiprocessor systems using evolutionary strategies. In: International conference on evolutionary computation, pp 345–349

  • Grimme C, Lepping J, Schwiegelshohn U (2013) Multi-criteria scheduling: an agent-based approach for expert knowledge integration. J Sched 16(4):369–383

    Article  MathSciNet  MATH  Google Scholar 

  • Grzonka D et al (2015) Artificial neural network support to monitoring of the evolutionary driven security aware scheduling in computational distributed environments. Future Gener Comput Syst 51:72–86

    Article  Google Scholar 

  • Gu F, Chen HP, Lu BY (2006) Optimization for fuzzy flexible job shop scheduling based on genetic algorithm. Syst Eng Electron 28(7):1017–1019

    MATH  Google Scholar 

  • Gu J, Gu X, Gu M (2009) A novel parallel quantum genetic algorithm for stochastic job shop scheduling. J Math Anal Appl 355(1):63–81

    Article  MathSciNet  MATH  Google Scholar 

  • Guerinik N, Van Caneghem M (1995) Solving crew scheduling problems by constraint programming. In: International conference on principles and practice of constraint programming, Springer, Berlin

    Chapter  Google Scholar 

  • Guiffrida AL, Nagi R (1998) Fuzzy set theory applications in production management research: a literature survey. J Intell Manuf 9:39–56

    Article  Google Scholar 

  • Guo C et al (2012) Decomposition-based classified ant colony optimization algorithm for scheduling semiconductor wafer fabrication system. Comput Ind Eng 62(1):141–151

    Article  Google Scholar 

  • Gupta D (2016) A study of three stage open shop scheduling by branch and bound technique under fuzzy environment. Arya Bhatta J Math Inf 8(1):15–22

    Google Scholar 

  • Gupta AK, Greenwood GW (1996) Applications of evolutionary strategies to fine-grained task scheduling. Parallel Process Lett 6(04):551–561

    Article  Google Scholar 

  • Gupta MC, Gupta YP, Kumar A (1993) Minimizing flow time variance in a single machine system using genetic algorithms. Eur J Oper Res 70(3):289–303

    Article  MathSciNet  MATH  Google Scholar 

  • Gupta D, Sharma S, Aggarwal S (2012) Specially structured flow shop production scheduling to minimize the rental cost in fuzzy environment. Int J Math Arch (IJMA) EISSN 3(9):2229–5046

    Google Scholar 

  • Gutjahr WJ, Rauner MS (2007) An ACO algorithm for a dynamic regional nurse-scheduling problem in Austria. Comput Oper Res 34(3):642–666

    Article  MATH  Google Scholar 

  • Ham AM (2018) Integrated scheduling of m-truck, m-drone, and m-depot constrained by time-window, drop-pickup, and m-visit using constraint programming. Transp Res Part C Emerg Technol 91:1–14

    Article  Google Scholar 

  • Hamad A, Sanugi B, Salleh S (2003) A neural network model for the common due date job scheduling on unrelated parallel machines. Int J Comput Math 80(7):845–851

    Article  MATH  Google Scholar 

  • Han S, Ishii H, Fujii S (1994) One machine scheduling problem with fuzzy due dates. Eur J Oper Res 79(1):1–12

    Article  MATH  Google Scholar 

  • Hao G, Lai KK, Tan M (2004) A neural network application in personnel scheduling. Ann Oper Res 128(1-4):65–90

    Article  MATH  Google Scholar 

  • Haridass K, Valenzuela J, Yucekaya AD, McDonald T (2014) Scheduling a log transport system using simulated annealing. Inf Sci 264:302–316

    Article  MathSciNet  Google Scholar 

  • Harjunkoski I, Grossmann IE (2001) Combined MILP-constraint programming approach for the optimal scheduling of multistage batch processes. In: Computer aided chemical engineering, vol 9, Elsevier, pp 877–882

  • Harjunkoski I, Grossmann IE (2002) Decomposition techniques for multistage scheduling problems using mixed-integer and constraint programming methods. Comput Chem Eng 26(11):1533–1552

    Article  Google Scholar 

  • Harjunkoski I, Jain V, Grossman IE (2000) Hybrid mixed-integer/constraint logic programming strategies for solving scheduling and combinatorial optimization problems. Comput Chem Eng 24(2-7):337–343

    Article  Google Scholar 

  • Harmanani HM, Ghosn SB (2016) An efficient method for the open shop scheduling problem using simulated annealing. In: Information technology: new generations, Springer International Publishing, pp 1183–1193

  • Hartmann S (1998) A competitive genetic algorithm for resource-constrained project scheduling. Naval Res Log (NRL) 45(7):733–750

    Article  MathSciNet  MATH  Google Scholar 

  • Hartmann S (2001) Project scheduling with multiple modes: a genetic algorithm. Ann Oper Res 102(1–4):111–135

    Article  MathSciNet  MATH  Google Scholar 

  • Haykin S, Lippmann R (1994) Neural networks, a comprehensive foundation. Int J Neural Syst 5(4):363–364

    Article  MATH  Google Scholar 

  • He R-J (2005) Parallel machine scheduling problem with time windows: a constraint programming and tabu search hybrid approach. In: Proceedings of 2005 international conference on machine learning and cybernetics, vol 5, IEEE

  • He Z et al (2009) Simulated annealing and tabu search for multi-mode project payment scheduling. Eur J Oper Res 198(3):688–696

    Article  MATH  Google Scholar 

  • Heinz S, Ku W-Y, Christopher Beck J (2013) Recent improvements using constraint integer programming for resource allocation and scheduling. In: International conference on AI and OR techniques in constriant programming for combinatorial optimization problems, Springer, Berlin

    Chapter  Google Scholar 

  • Heist SM (2003) A comparison of constraint programming and integer programming for an industrial planning problem. PhD thesis, Lehigh University

  • Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Hong TP, Wang TT (2000) Fuzzy flexible flow shops at two machine centers for continuous fuzzy domains. Inf Sci 129(1):227–237

    Article  MathSciNet  MATH  Google Scholar 

  • Hota PK, Barisal AK, Chakrabarti R (2009) An improved PSO technique for short-term optimal hydrothermal scheduling. Electric Power Syst Res 79(7):1047–1053

    Article  Google Scholar 

  • Hsu CY, Chang PC, Chen MH (2015) A linkage mining in block-based evolutionary algorithm for permutation flow shop scheduling problem. Comput Ind Eng 83:159–171

    Article  Google Scholar 

  • Hsu C-Y, Kao B-R, RobertLai K (2016) Agent-based fuzzy constraint-directed negotiation mechanism for distributed job shop scheduling. Eng Appl Artif Intell 53:140–154

    Article  Google Scholar 

  • Hu Y, Yin M, Li X (2011) A novel objective function for job shop scheduling problem with fuzzy processing time and fuzzy due date using differential evolution algorithm. Int J Adv Manuf Technol 56(9–12):1125–1138

    Article  Google Scholar 

  • Huang S-J (2001) Enhancement of hydroelectric generation scheduling using ant colony system based optimization approaches. IEEE Trans Energy Convers 16(3):296–301

    Article  MathSciNet  Google Scholar 

  • Huang K-L, Liao C-J (2008) Ant colony optimization combined with taboo search for the job shop scheduling problem. Comput Oper Res 35(4):1030–1046

    Article  MathSciNet  MATH  Google Scholar 

  • Huang J, Süer GA (2015) A dispatching rule-based genetic algorithm for multi-objective job shop scheduling using fuzzy satisfaction levels. Comput Ind Eng 86:29–42

    Article  Google Scholar 

  • Huang C-S, Huang Y-C, Lai P-J (2012) Modified genetic algorithms for solving fuzzy flow shop scheduling problems and their implementation with CUDA. Expert Syst Appl 39(5):4999–5005

    Article  Google Scholar 

  • Hübscher R, Glover F (1994) Applying tabu search with influential diversification to multiprocessor scheduling. Comput Oper Res 21(8):877–884

    Article  MATH  Google Scholar 

  • Ishibuchi H, Yamamoto N, Misaki S, Tanaka H (1994) Local search algorithms for flow shop scheduling with fuzzy due dates. Int J Prod Econ 33(1–3):53–66

    Article  Google Scholar 

  • Ishibuchi H, Misaki S, Tanaka H (1995) Modified simulated annealing algorithms for the flow shop sequencing problem. Eur J Oper Res 81(2):388–398

    Article  MATH  Google Scholar 

  • Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multiobjective permutation flow shop scheduling. IEEE Trans Evolut Comput 7(2):204–223

    Article  Google Scholar 

  • Ishii H, Tada M, Masuda T (1992) Two scheduling problems with fuzzy due dates. Fuzzy Sets Syst 46(3):339–347

    Article  MathSciNet  MATH  Google Scholar 

  • Itoh T, Ishii H (2005) One machine scheduling problem with fuzzy random due dates. Fuzzy Optim Decis Mak 4(1):71–78

    Article  MathSciNet  MATH  Google Scholar 

  • Iyer SK, Saxena B (2004) Improved genetic algorithm for the permutation flow shop scheduling problem. Comput Oper Res 31(4):593–606

    Article  MathSciNet  MATH  Google Scholar 

  • Jafari A et al (2012) A novel discrete electromagnetism-like for fuzzy open shop scheduling problem with parallel machines to minimize makespan. Int J 1(3):1–25

    Google Scholar 

  • Jain LC, Martin NM (1999) Introduction to neural networks, fuzzy systems, genetic algorithms, and their fusion. In: Fusion of neural networks, fuzzy sets, and genetic algorithms: industrial Applications. CRC Press, Boca Raton, pp 3–12

  • Jayaraman VK et al (2000) Ant colony framework for optimal design and scheduling of batch plants. Comput Chem Eng 24(8):1901–1912

    Article  Google Scholar 

  • Jia S, Zhi-Hua H (2014) Path-relinking Tabu search for the multi-objective flexible job shop scheduling problem. Comput Oper Res 47:11–26

    Article  MathSciNet  MATH  Google Scholar 

  • Jia HZ, Fuh JY, Nee AY, Zhang YF (2007) Integration of genetic algorithm and Gantt chart for job shop scheduling in distributed manufacturing systems. Comput Ind Eng 53(2):313–320

    Article  Google Scholar 

  • Jia Y, Qu J, Wang L (2016) A novel particle swarm optimization algorithm for permutation flow shop scheduling problem. In: International conference on human centered computing, Springer International Publishing

  • Jin F, Song S, Wu C (2009) A simulated annealing algorithm for single machine scheduling problems with family setups. Comput Oper Res 36(7):2133–2138

    Article  MathSciNet  MATH  Google Scholar 

  • Jin L, Zhang C, Shao X, Tian G (2016) Mathematical modeling and a memetic algorithm for the integration of process planning and scheduling considering uncertain processing times. In: Proceedings of the institution of mechanical engineers, part b: journal of engineering manufacture, 0954405415625916

  • Jolai F, Asefi H, Rabiee M, Ramezani P (2013) Bi-objective simulated annealing approaches for no-wait two-stage flexible flow shop scheduling problem. Sci Iran 20(3):861–872

    Google Scholar 

  • Jorapur VS, Puranik VS, Deshpande AS, Sharma M (2016) A promising initial population based genetic algorithm for job shop scheduling problem. J Softw Eng Appl 9(05):208

    Article  Google Scholar 

  • Józefowska J, Mika M, Różycki R, Waligóra G, Węglarz J (2001) Simulated annealing for multi-mode resource-constrained project scheduling. Ann Oper Res 102(1–4):137–155

    Article  MathSciNet  MATH  Google Scholar 

  • Kahlon KS (2014) An embedded fuzzy expert system for adaptive WFQ scheduling of IEEE 802.16 networks. Expert Syst Appl 41(16):7621–7629

    Article  Google Scholar 

  • Kalashnikov AV, Kostenko VA (2008) A parallel algorithm of simulated annealing for multiprocessor scheduling. J Comput Syst Sci Int 47(3):455–463

    Article  MATH  Google Scholar 

  • Kanet JJ, Ahire SL, Gorman MF (2004) Constraint programming for scheduling. In: Handbook of scheduling: algorithms, models, and performance analysis, vol 47. Chapman and Hall/CRC Press, Boca Raton, pp 1–21

  • Kaplan S, Rabadi G (2013) Simulated annealing and metaheuristic for randomized priority search algorithms for the aerial refuelling parallel machine scheduling problem with due date-to-deadline windows and release times. Eng Optim 45(1):67–87

    Article  MathSciNet  Google Scholar 

  • Karimi H, Rahmati SHA, Zandieh M (2012) An efficient knowledge-based algorithm for the flexible job shop scheduling problem. Knowl Based Syst 36:236–244

    Article  Google Scholar 

  • Karimi-Nasab M, Modarres M, Seyedhoseini SM (2015) A self-adaptive PSO for joint lot sizing and job shop scheduling with compressible process times. Appl Soft Comput 27:137–147

    Article  Google Scholar 

  • Kechadi M-T, Low KS, Goncalves G (2013) Recurrent neural network approach for cyclic job shop scheduling problem. J Manuf Syst 32(4):689–699

    Article  Google Scholar 

  • Khayat E, Ghada AL, Riopel D (2006) Integrated production and material handling scheduling using mathematical programming and constraint programming. Eur J Oper Res 175(3):1818–1832

    Article  MATH  Google Scholar 

  • Kim DW, Kim KH, Jang W, Chen FF (2002) Unrelated parallel machine scheduling with setup times using simulated annealing. Robot Comput Integr Manuf 18(3):223–231

    Article  Google Scholar 

  • Kır S, Yazgan HR (2016) A sequence dependent single machine scheduling problem with fuzzy axiomatic design for the penalty costs. Comput Ind Eng 92:95–104

    Article  Google Scholar 

  • Kırış Ş, Yüzügüllü N, Ergün N, Çevik AA (2010) A knowledge-based scheduling system for emergency departments. Knowl Based Syst 23(8):890–900

    Article  Google Scholar 

  • Koay CA, Srinivasan D (2003) Particle swarm optimization-based approach for generator maintenance scheduling. In: Proceedings of the swarm intelligence symposium, SIS’03, IEEE

  • Kocsis T et al (2014) Case-reasoning system for mathematical modelling options and resolution methods for production scheduling problems: case representation, acquisition and retrieval. Comput Ind Eng 77:46–64

    Article  Google Scholar 

  • Kolodner J (2014) Case-based reasoning. Morgan Kaufmann, Burlington

    Google Scholar 

  • Kolodner JL, Jona MY (1991) Case-based reasoning: an overview. Northwestern University, Evanston

    Google Scholar 

  • Konno T, Ishii H (2000) An open shop scheduling problem with fuzzy allowable time and fuzzy resource constraint. Fuzzy Sets Syst 109(1):141–147

    Article  MathSciNet  Google Scholar 

  • Koulinas G, Kotsikas L, Anagnostopoulos K (2014) A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem. Inf Sci 277:680–693

    Article  Google Scholar 

  • Kovács A, Váncza J (2004) Completable partial solutions in constraint programming and constraint-based scheduling. In: International conference on principles and practice of constraint programming, Springer, Berlin

    Chapter  MATH  Google Scholar 

  • Ku HM, Karimi IA (1991) Scheduling algorithms for serial multiproduct batch processes with tardiness penalties. Comput Chem Eng 15(5):283–286

    Article  Google Scholar 

  • Kuchcinski K, Wolinski C (2003) Global approach to assignment and scheduling of complex behaviors based on HCDG and constraint programming. J Syst Architect 49(12-15):489–503

    Article  Google Scholar 

  • Kumar K et al (2007) Optimization of flow shop scheduling with fuzzy due dates using a hybrid evolutionary algorithm. In: Proceedings of the international conference

  • Kuo IH et al (2009) An efficient flow shop scheduling algorithm based on a hybrid particle swarm optimization model. Expert Syst Appl 36(3):7027–7032

    Article  Google Scholar 

  • Kιlιç S, Kahraman C (2006) Metaheuristic techniques for job shop scheduling problem and a fuzzy ant colony optimization algorithm. In: Fuzzy applications in industrial engineering, Springer, Berlin, pp 401–425

  • Laarhoven V, Peter JM, Aarts EHL (1987) Simulated annealing. Simulated annealing: theory and applications. Springer, Dordrecht, pp 7–15

    Book  MATH  Google Scholar 

  • Laguna M, Glover F (1993) Integrating target analysis and tabu search for improved scheduling systems. Expert Syst Appl 6(3):287–297

    Article  Google Scholar 

  • Lai P-J, Wu H-C (2008) Using genetic algorithms to solve fuzzy flow shop scheduling problems based on possibility and necessity measures. Int J Uncertain Fuzziness Knowl Based Syst 16(03):409–433

    Article  MathSciNet  MATH  Google Scholar 

  • Lai LL, Ma JT, Lee JB (1998) Multitime-interval scheduling for daily operation of a two-cogeneration system with evolutionary programming. Int J Electr Power Energy Syst 20(5):305–311

    Article  Google Scholar 

  • Lapègue T, Prot D, Bellenguez-Morineau O (2012) A tour scheduling problem with fixed jobs: use of constraint programming. In: Practice and theory of automated timetabling

  • Ławrynowicz A (2008) Integration of production planning and scheduling using an expert system and a genetic algorithm. J Oper Res Soc 59(4):455–463

    Article  MATH  Google Scholar 

  • Le Pape C, Baptiste P (1997) A constraint programming library for preemptive and non-preemptive scheduling. In: Proceedings of PACT97

  • Lee SM, Asllani AA (2004) Job scheduling with dual criteria and sequence-dependent setups: mathematical versus genetic programming. Omega 32(2):145–153

    Article  Google Scholar 

  • Lee E-B, Harvey J, Samadian M (2005) Knowledge-based scheduling analysis software for highway rehabilitation and reconstruction projects. Transp Res Rec J Transp Res Board 1907:15–24

    Article  Google Scholar 

  • Lee CY, Hwang JJ, Chow YC, Anger FD (1988) Multiprocessor scheduling with interprocessor communication delays. Oper Res Lett 7(3):141–147

    Article  MathSciNet  MATH  Google Scholar 

  • Lee DH, Cao Z, Meng Q (2007) Scheduling of two-transtainer systems for loading outbound containers in port container terminals with simulated annealing algorithm. Int J Prod Econ 107(1):115–124

    Article  Google Scholar 

  • Lei D (2008) Pareto archive particle swarm optimization for multi-objective fuzzy job shop scheduling problems. Int J Adv Manuf Technol 37(1–2):157–165

    Article  Google Scholar 

  • Lei D (2010a) Fuzzy job shop scheduling problem with availability constraints. Comput Ind Eng 58(4):610–617

    Article  Google Scholar 

  • Lei D (2010b) A genetic algorithm for flexible job shop scheduling with fuzzy processing time. Int J Prod Res 48(10):2995–3013

    Article  MATH  Google Scholar 

  • Lei D (2010c) Solving fuzzy job shop scheduling problems using random key genetic algorithm. Int J Adv Manuf Technol 49(1–4):253–262

    Article  Google Scholar 

  • Lei D, Guo X (2012) Swarm-based neighbourhood search algorithm for fuzzy flexible job shop scheduling. Int J Prod Res 50(6):1639–1649

    Article  Google Scholar 

  • Lei D, Wu Z (2006) Crowding-measure-based multiobjective evolutionary algorithm for job shop scheduling. Int J Adv Manuf Technol 30(1–2):112–117

    Article  Google Scholar 

  • Li J et al (2012) Solving fuzzy job shop scheduling problem by a hybrid PSO algorithm. In: Swarm and evolutionary computation, Springer, Berlin, pp 275–282

    Chapter  Google Scholar 

  • Li X et al (2015) Study on resource scheduling method of predictive maintenance for equipment based on knowledge. In: 2015 10th international conference on intelligent systems and knowledge engineering (ISKE), IEEE

  • Li Y, Li TK (2007) Research on no-wait hybrid flowshop scheduling problem based on constraint programming. Control Instrum Chem Ind 34(3):26

    Google Scholar 

  • Li JQ, Pan YX (2013) A hybrid discrete particle swarm optimization algorithm for solving fuzzy job shop scheduling problem. Int J Adv Manuf Technol 66(1–4):583–596

    Article  Google Scholar 

  • Li Y, Luh PB, Guan X (1994) Fuzzy optimization-based scheduling of identical machines with possible breakdown. In: Proceedings of 1994 IEEE international conference on robotics and automation, IEEE, pp 3447–3452

  • Li DC, Wu C, Torng KY (1997) Using an unsupervized neural network and decision tree as knowledge acquisition tools for FMS scheduling. Int J Syst Sci 28(10):977–985

    Article  MATH  Google Scholar 

  • Li H et al (2000) A production rescheduling expert simulation system. Eur J Oper Res 124(2):283–293

    Article  MATH  Google Scholar 

  • Li T et al (2005) Constraint programming approach to steelmaking-making process scheduling. Commun IIMA 5(3):2

    Google Scholar 

  • Li J-Q et al (2011) A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem. Int J Adv Manuf Technol 52(5-8):683–697

    Article  Google Scholar 

  • Li X, Ishii H, Masuda T (2012) Single machine batch scheduling problem with fuzzy batch size. Comput Ind Eng 62(3):688–692

    Article  Google Scholar 

  • Li X, Ishii H, Chen M (2015a) Single machine parallel-batching scheduling problem with fuzzy due date and fuzzy precedence relation. Int J Prod Res 53(9):2707–2717

    Article  Google Scholar 

  • Li D, Meng X, Liang Q, Zhao J (2015b) A heuristic-search genetic algorithm for multi-stage hybrid flow shop scheduling with single processing machines and batch processing machines. J Intell Manuf 26(5):873–890

    Article  Google Scholar 

  • Liang RH, Hsu YY (1995) A hybrid artificial neural network—differential dynamic programming approach for short-term hydro scheduling. Electric Power Syst Res 33(2):77–86

    Article  Google Scholar 

  • Liang C, Huang Y, Yang Y (2009) A quay crane dynamic scheduling problem by hybrid evolutionary algorithm for berth allocation planning. Comput Ind Eng 56(3):1021–1028

    Article  Google Scholar 

  • Liao SH (2005) Expert system methodologies and applications, a decade review from 1995 to 2004. Expert Syst Appl 28(1):93–104

    Article  Google Scholar 

  • Liao C-J, Juan H-C (2007) An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups. Comput Oper Res 34(7):1899–1909

    Article  MATH  Google Scholar 

  • Liao LM, Liao CJ (1998) Single machine scheduling problem with fuzzy due date and processing time. J Chin Inst Eng 21(2):189–196

    Article  MathSciNet  Google Scholar 

  • Liao C-J, Tseng C-T, Luarn P (2007) A discrete version of particle swarm optimization for flow shop scheduling problems. Comput Oper Res 34(10):3099–3111

    Article  MATH  Google Scholar 

  • Liao C-J, Tjandradjaja E, Chung T-P (2012) An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem. Appl Soft Comput 12(6):1755–1764

    Article  Google Scholar 

  • Liaw CF (2000) A hybrid genetic algorithm for the open shop scheduling problem. Eur J Oper Res 124(1):28–42

    Article  MathSciNet  MATH  Google Scholar 

  • Liaw CF (2003) An efficient tabu search approach for the two-machine preemptive open shop scheduling problem. Comput Oper Res 30(14):2081–2095

    Article  MATH  Google Scholar 

  • Liess O, Michelon P (2008) A constraint programming approach for the resource-constrained project scheduling problem. Ann Oper Res 157(1):25–36

    Article  MathSciNet  MATH  Google Scholar 

  • Lim J et al (2016) Fast scheduling of semiconductor manufacturing facilities using case-based reasoning. IEEE Trans Semicond Manuf 29(1):22–32

    Article  Google Scholar 

  • Limtanyakul K, Schwiegelshohn U (2007) Scheduling tests on vehicle prototypes using constraint programming. In: Proceedings of the 3rd multidisciplinary international scheduling conference: theory and applications

  • Limtanyakul K, Schwiegelshohn U (2012) Improvements of constraint programming and hybrid methods for scheduling of tests on vehicle prototypes. Constraints 17(2):172–203

    Article  MathSciNet  Google Scholar 

  • Lin F-T (2002) Fuzzy job shop scheduling based on ranking level (/spl lambda/, 1) interval-valued fuzzy numbers. IEEE Trans Fuzzy Syst 10(4):510–522

    Article  Google Scholar 

  • Lin F-T (2003) A fuzzy approach to job shop scheduling problem based on imprecise processing times. In: Recent advances in intelligent paradigms and applications, Physica-Verlag, HD, pp 91–106

    Chapter  Google Scholar 

  • Lin J (2015) A hybrid biogeography-based optimization for the fuzzy flexible job shop scheduling problem. Knowl Based Syst 78:59–74

    Article  Google Scholar 

  • Lin SW, Ying KC (2015) A multi-point simulated annealing heuristic for solving multiple objective unrelated parallel machine scheduling problems. Int J Prod Res 53(4):1065–1076

    Article  Google Scholar 

  • Lin T-L et al (2010) An efficient job shop scheduling algorithm based on particle swarm optimization. Expert Syst Appl 37(3):2629–2636

    Article  Google Scholar 

  • Lin CC, Kang JR, Hsu TH (2015) A memetic algorithm with recovery scheme for nurse preference scheduling. J Ind Prod Eng 32(2):83–95

    Google Scholar 

  • Ling SH et al (2011) Hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem. Int J Comput Intell Appl 10(03):335–356

    Article  Google Scholar 

  • Liu H, Dong JJ (1996) Dispatching rule selection using artificial neural networks for dynamic planning and scheduling. J Intell Manuf 7(3):243–250

    Article  Google Scholar 

  • Liu S-X, Song J-H (2011) Combination of constraint programming and mathematical programming for solving resources-constrained project-scheduling problems. Control Theory Appl 28(8):1113–1120

    MATH  Google Scholar 

  • Liu C, Wang J (2016) Cell formation and task scheduling considering multi-functional resource and part movement using hybrid simulated annealing. Int J Comput Intell Syst 9(4):765–777

    Article  Google Scholar 

  • Liu B, Wang L, Jin Y-h (2005) Hybrid particle swarm optimization for flow shop scheduling with stochastic processing time. In: International conference on computational and information science, Springer, Berlin

    Chapter  Google Scholar 

  • Liu B, Wang L, Jin Y-H (2007) An effective hybrid particle swarm optimization for no-wait flow shop scheduling. Int J Adv Manuf Technol 31(9-10):1001–1011

    Article  Google Scholar 

  • Liu B, Wang L, Jin Y-H (2008) An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers. Comput Oper Res 35(9):2791–2806

    Article  MATH  Google Scholar 

  • Liu T et al (2012) Design and implementation of bus crew scheduling system using integrated case-based and rule-based reasoning. In: 2012 Fifth international joint conference on computational sciences and optimization (CSO), IEEE

  • Liu X, Ni Z, Qiu X (2016) Application of ant colony optimization algorithm in integrated process planning and scheduling. Int J Adv Manuf Technol 84(1-4):393–404

    Article  Google Scholar 

  • López B (2002) Combining CBR and CSP: a case study on holiday scheduling. Technical report, University of Girona, Spain

  • Lopez L, Carter MW, Gendreau M (1998) The hot strip mill production scheduling problem: a tabu search approach. Eur J Oper Res 106(2):317–335

    Article  MATH  Google Scholar 

  • Lu B-y et al (2004) The model for partial flexible job shop scheduling problem based on fuzzy logic. Chin J Manag Sci 6:010

    Google Scholar 

  • Lu B-y et al (2006) Research of earliness/tardiness problem in fuzzy job shop scheduling. J Syst Eng 6:013

    Google Scholar 

  • Luh PB, Zhao X, Thakur LS, Chen KH, Chiueh TD, Chang SC, Shyu JM (1999) Architectural design of neural network hardware for job shop scheduling. CIRP Ann Manuf Technol 48(1):373–376

    Article  Google Scholar 

  • Lustig IL, Puget IF (2001) Program does not equal program: constraint programming and its relationship to mathematical programming. Interfaces 31(6):29–53

    Article  Google Scholar 

  • Malik AM, Russell T, Chase M, Van Beek P (2008a) Learning heuristics for basic block instruction scheduling. J Heuristics 14(6):549–569

    Article  MATH  Google Scholar 

  • Malik AM, McInnes J, Van Beek P (2008b) Optimal basic block instruction scheduling for multiple-issue processors using constraint programming. Int J Artif Intell Tools 17(01):37–54

    Article  Google Scholar 

  • Mandal KK, Chakraborty N (2012) Daily combined economic emission scheduling of hydrothermal systems with cascaded reservoirs using self organizing hierarchical particle swarm optimization technique. Expert Syst Appl 39(3):3438–3445

    Article  Google Scholar 

  • Mandal KK, Basu M, Chakraborty N (2008) Particle swarm optimization technique based short-term hydrothermal scheduling. Appl Soft Comput 8(4):1392–1399

    Article  Google Scholar 

  • Marchiori E, Steenbeek A (2000).An evolutionary algorithm for large scale set covering problems with application to airline crew scheduling. In: Workshops on real-world applications of evolutionary computation, Springer, Berlin, pp 370–384

    Chapter  Google Scholar 

  • Marett R, Wright M (1996) A comparison of neighborhood search techniques for multi-objective combinatorial problems. Comput Oper Res 23(5):465–483

    Article  MATH  Google Scholar 

  • Marimuthu S, Naveen Sait A (2013) Performance evaluation of proposed differential evolution and particle swarm optimization algorithms for scheduling m-machine flow shops with lot streaming. J Intell Manuf 24(1):175–191

    Article  Google Scholar 

  • Marimuthu S, Ponnambalam SG, Jawahar N (2008) Evolutionary algorithms for scheduling m-machine flow shop with lot streaming. Robot Comput Integr Manuf 24(1):125–139

    Article  Google Scholar 

  • Marimuthu S, Ponnambalam SG, Jawahar N (2009) Threshold accepting and ant-colony optimization algorithms for scheduling m-machine flow shops with lot streaming. J Mater Process Technol 209(2):1026–1041

    Article  Google Scholar 

  • Marinakis Y, Marinaki M (2013) Particle swarm optimization with expanding neighborhood topology for the permutation flow shop scheduling problem. Soft Comput 17(7):1159–1173

    Article  MATH  Google Scholar 

  • Marsh CA (1985) MARS–an expert system using the automated reasoning tool to schedule resources. In: Robotics and expert systems–proceedings of Robex 85, Instrument Society of America, pp 123–125

  • Martin CH (2009) A hybrid genetic algorithm/mathematical programming approach to the multi-family flow shop scheduling problem with lot streaming. Omega 37(1):126–137

    Article  MathSciNet  Google Scholar 

  • Mathiyalagan P, Dhepthie UR, Sivanandam SN (2010) Grid scheduling using enhanced PSO algorithm. Int J Comput Sci Eng 2(2):140–145

    Google Scholar 

  • Matsumoto S et al (2009) Design of knowledge-based scheduling solution based on expert’s technical knowledge in printing process and proposal of its improvement. Int J Innov Comput Inf Control 5(11):4125–4143

    Google Scholar 

  • McCahon CS, Lee ES (1992) Fuzzy job sequencing for a flow shop. Eur J Oper Res 62(3):294–301

    Article  MATH  Google Scholar 

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133

    Article  MathSciNet  MATH  Google Scholar 

  • Meeran S, Morshed MS (2012) A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study. J Intell Manuf 23(4):1063–1078

    Article  Google Scholar 

  • Mehrabad MS, Pahlavani A (2009) A fuzzy multi-objective programming for scheduling of weighted jobs on a single machine. Int J Adv Manuf Technol 45(1–2):122–139

    Article  Google Scholar 

  • Mendes JJDM, Gonçalves JF, Resende MG (2009) A random key based genetic algorithm for the resource constrained project scheduling problem. Comput Oper Res 36(1):92–109

    Article  MathSciNet  MATH  Google Scholar 

  • Mesbah M (2014) Value management for construction projects via an expert system framework. Diss. Eastern Mediterranean University (EMU)-Doğu Akdeniz Üniversitesi (DAÜ)

  • Metaxiotis KS, Psarras JE, Askounis DT (2002) GENESYS: an expert system for production scheduling. Ind Manag Data Syst 102(6):309–317

    Article  Google Scholar 

  • Michalski RS (1983) A theory and methodology of inductive learning. In: Machine learning, Springer, Berlin, pp 83–134

    Chapter  Google Scholar 

  • Mika M, Waligora G, Węglarz J (2005) Simulated annealing and tabu search for multi-mode resource-constrained project scheduling with positive discounted cash flows and different payment models. Eur J Oper Res 164(3):639–668

    Article  MathSciNet  MATH  Google Scholar 

  • Mika M, Waligora G, Węglarz J (2008) Tabu search for multi-mode resource-constrained project scheduling with schedule-dependent setup times. Eur J Oper Res 187(3):1238–1250

    Article  MATH  Google Scholar 

  • Miranda S, Baker C, Woodbridge K, Griffiths H (2006) Knowledge-based resource management for multifunction radar: a look at scheduling and task prioritization. IEEE Signal Process Mag 23(1):66–76

    Article  Google Scholar 

  • Mitchell TM (1997) Machine learning. WCB. McGraw-Hill, New York

    Google Scholar 

  • Mitchell RS, Michalski JG, Carbonell TM (2013) An artificial intelligence approach. Springer, Berlin

    Google Scholar 

  • Mittal M, Singh TP, Gupta D (2016) Linkage of priority queue system to flow shop scheduling in fuzzy environment. Arya Bhatta J Math Inf 8(1):97–108

    Google Scholar 

  • Miyashita K (2000) Job shop scheduling with genetic programming. In: Proceedings of the 2nd annual conference on genetic and evolutionary computation, Morgan Kaufmann Publishers Inc., pp 505–512

  • Mladenovic S, Markovic M, Cangalovic M (2004) Constraint programming approach to train scheduling on railway network supported by heuristics. In: 10th World conference on transport researchworld conference on transport research society Istanbul Technical University

  • Mok PY, Kwong CK, Wong WK (2007) Optimisation of fault-tolerant fabric-cutting schedules using genetic algorithms and fuzzy set theory. Eur J Oper Res 177(3):1876–1893

    Article  MATH  Google Scholar 

  • Monette J-N, Deville Y, Van Hentenryck P (2009) Just-in-time scheduling with constraint programming. In: ICAPS

  • Morton TE, Ramnath P (1992) Guided forward tabu/beam search for scheduling very large dynamic job shops. No. 1992-47. Carnegie Mellon University, Tepper School of Business

  • Morton TE, Fox M, Sathi A (1984) PATRIARCH; a multilevel system for cost accounting, planning, scheduling. In: Partial working document Graduate School of Industrial Administration, Carnegie Mellon University

  • Moschakis IA, Karatza HD (2015) Multi-criteria scheduling of Bag-of-Tasks applications on heterogeneous interlinked clouds with simulated annealing. J Syst Softw 101:1–14

    Article  Google Scholar 

  • Murata T, Ishibuchi H, Tanaka H (1996) Genetic algorithms for flow shop scheduling problems. Comput Ind Eng 30(4):1061–1071

    Article  Google Scholar 

  • Muthusamy K, Sung SC, Vlach M, Ishii H (2003) Scheduling with fuzzy delays and fuzzy precedences. Fuzzy Sets Syst 134(3):387–395

    Article  MathSciNet  MATH  Google Scholar 

  • Nagar A, Haddock J, Heragu S (1995) Multiple and bicriteria scheduling: a literature survey. Eur J Oper Res 81(1):88–104

    Article  MATH  Google Scholar 

  • Nailwal KK, Gupta D, Sharma S (2015) Two stage flow shop scheduling under fuzzy environment. Indian J Sci Technol 8:16

    Article  Google Scholar 

  • Neto RT, Godinho Filho M (2011) An ant colony optimization approach to a permutational flow shop scheduling problem with outsourcing allowed. Comput Oper Res 38(9):1286–1293

    Article  MathSciNet  MATH  Google Scholar 

  • Neto RFT, Godinho Fabio M, Da Silva FM (2015) An ant colony optimization approach for the parallel machine scheduling problem with outsourcing allowed. J Intell Manuf 26(3):527–538

    Article  Google Scholar 

  • Nguyen S, Zhang M, Johnston M, Tan KC (2012) Evolving reusable operation-based due date assignment models for job shop scheduling with genetic programming. In: European conference on genetic programming, Springer, Berlin, pp 121–133

    Google Scholar 

  • Nguyen S, Zhang M, Johnston M, Tan KC (2013) Dynamic multi-objective job shop scheduling: a genetic programming approach. In: Automated scheduling and planning, Springer, Berlin, pp 251–282

    Google Scholar 

  • Niu Q, Jiao B, Gu X (2008) Particle swarm optimization combined with genetic operators for job shop scheduling problem with fuzzy processing time. Appl Math Comput 205(1):148–158

    MathSciNet  MATH  Google Scholar 

  • Noori-Darvish S, Tavakkoli-Moghaddam R (2011) Solving a bi-objective open shop scheduling problem with fuzzy parameters. J Appl Oper Res 3(2):59–74

    Google Scholar 

  • Nouiri M et al (2015) An effective and distributed particle swarm optimization algorithm for flexible job shop scheduling problem. J Intell Manuf 29:1–13

    Google Scholar 

  • Novara FM, Henning GP (2017) Scheduling of multiproduct multistage batch plants with uncertain processing times: an innovative constraint programming approach. In: Proceedings fundations of computer-aided process operations, Tucson, AZ, EE. UU

  • Novara FM, Novas JM, Henning GP (2016) A novel constraint programming model for large-scale scheduling problems in multiproduct multistage batch plants: limited resources and campaign-based operation. Comput Chem Eng 93:101–117

    Article  Google Scholar 

  • Novas JM, Henning GP (2010) Reactive scheduling framework based on domain knowledge and constraint programming. Comput Chem Eng 34(12):2129–2148

    Article  Google Scholar 

  • Novas JM, Henning GP (2012) A comprehensive constraint programming approach for the rolling horizon-based scheduling of automated wet-etch stations. Comput Chem Eng 42:189–205

    Article  Google Scholar 

  • Novas JM, Henning GP (2014) Integrated scheduling of resource-constrained flexible manufacturing systems using constraint programming. Expert Syst Appl 41(5):2286–2299

    Article  Google Scholar 

  • Ogbu FA, Smith DK (1990) The application of the simulated annealing algorithm to the solution of the n/m/C max flow shop problem. Comput Oper Res 17(3):243–253

    Article  MathSciNet  MATH  Google Scholar 

  • Osman IH, Potts CN (1989) Simulated annealing for permutation flow shop scheduling. Omega 17(6):551–557

    Article  Google Scholar 

  • Ouelhadj D, Petrovic S (2009) A survey of dynamic scheduling in manufacturing systems. J Sched 12(4):417–431

    Article  MathSciNet  MATH  Google Scholar 

  • Öztürk C et al (2012) A constraint programming model for balancing and scheduling of flexible mixed model assembly lines with parallel stations. IFAC Proc Vol 45(6):420–425

    Article  Google Scholar 

  • Palacios JJ et al (2014a) β-Robust solutions for the fuzzy open shop scheduling. In: International conference on information processing and management of uncertainty in knowledge-based systems, Springer International Publishing

  • Palacios JJ et al (2014b) Robust swarm optimisation for fuzzy open shop scheduling. Nat Comput 13(2):145–156

    Article  MathSciNet  Google Scholar 

  • Palacios JJ et al (2015) Swarm lexicographic goal programming for fuzzy open shop scheduling. J Intell Manuf 26(6):1201–1215

    Article  Google Scholar 

  • Park SC, Piramuthu S, Raman N, Shaw MJ (1990) Integrating inductive learning and simulation in rule-based scheduling. In: Expert systems in engineering principles and applications, Springer, Berlin, pp 152–167

    Chapter  Google Scholar 

  • Park J, Nguyen S, Zhang M, Johnston M (2015) Evolving ensembles of dispatching rules using genetic programming for job shop scheduling. In: European conference on genetic programming, Springer International Publishing, pp 92–104

  • Park J, Mei Y, Nguyen S, Chen G, Johnston M, Zhang M (2016) Genetic programming based hyper-heuristics for dynamic job shop scheduling: cooperative coevolutionary approaches. In: European conference on genetic programming, Springer International Publishing, pp 115–132

  • Pellerin D, Hérault J (1994) Scheduling with neural networks: application to timetable construction. Neurocomputing 6(4):419–442

    Article  MATH  Google Scholar 

  • Peng J, Song K (2001) Expected value goal programming model for fuzzy scheduling problem. In: The 10th IEEE international conference on fuzzy systems, 2001, vol 1, IEEE, pp 292–295

  • Peng J, Song K (2003) Fuzzy flow shop scheduling models based on credibility measure. In: The 12th IEEE international conference on fuzzy systems, FUZZ’03, vol 2, IEEE, pp 1423–1427

  • Peng B, Lü Z, Cheng TCE (2015) A tabu search/path relinking algorithm to solve the job shop scheduling problem. Comput Oper Res 53:154–164

    Article  MathSciNet  MATH  Google Scholar 

  • Pereira I, Madureira A (2010) Meta-heuristics tunning using CBR for dynamic scheduling. IN: 2010 IEEE 9th international conference on cybernetic intelligent systems (CIS), IEEE

  • Pereira I, Madureira A (2013) Self-optimization module for scheduling using case-based reasoning. Appl Soft Comput 13(3):1419–1432

    Article  Google Scholar 

  • Pessoa MAO et al (2013) Advanced planning and scheduling systems based on time windows and constraint programming. In: IFAC proceedings, vol 46.7, pp 192–197

    Article  Google Scholar 

  • Petrovic S, Xueyan S (2006) A new approach to two-machine flow shop problem with uncertain processing times. Optim Eng 7(3):329–342

    Article  MathSciNet  MATH  Google Scholar 

  • Pezzella F, Merelli E (2000) A tabu search method guided by shifting bottleneck for the job shop scheduling problem. Eur J Oper Res 120(2):297–310

    Article  MathSciNet  MATH  Google Scholar 

  • Piroozfard H, Wong KY, Hassan A (2016) A hybrid genetic algorithm with a knowledge-based operator for solving the job shop scheduling problems. J Optim 2016:1–13

    MathSciNet  MATH  Google Scholar 

  • Pongcharoen P, Hicks C, Braiden PM, Stewardson DJ (2002) Determining optimum genetic algorithm parameters for scheduling the manufacturing and assembly of complex products. Int J Prod Econ 78(3):311–322

    Article  MATH  Google Scholar 

  • Ponsich A, Coello CA (2013) A hybrid differential evolution—Tabu search algorithm for the solution of job shop scheduling problems. Appl Soft Comput 13(1):462–474

    Article  Google Scholar 

  • Pour SM et al (2018) A hybrid constraint programming/mixed integer programming framework for the preventive signaling maintenance crew scheduling problem. Eur J Oper Res 269(1):341–352

    Article  MathSciNet  MATH  Google Scholar 

  • Prakash A, Chan FT, Deshmukh SG (2011) FMS scheduling with knowledge based genetic algorithm approach. Expert Syst Appl 38(4):3161–3171

    Article  Google Scholar 

  • Priore P, De La Fuente D, Pino R (2001) Learning-based scheduling of flexible manufacturing systems using case-based reasoning. Appl Artif Intell 15(10):949–963

    Article  Google Scholar 

  • Priore P, de la Fuente D, Pino R, Puente J (2003) Dynamic scheduling of flexible manufacturing systems using neural networks and inductive learning. Integr Manuf Syst 14(2):160–168

    Article  Google Scholar 

  • Qian B, Wang L, Huang DX, Wang X (2008) Scheduling multi-objective job shops using a memetic algorithm based on differential evolution. Int J Adv Manuf Technol 35(9–10):1014–1027

    Article  Google Scholar 

  • Quinlan JR (2014) C4. 5: programs for machine learning. Elsevier, Amsterdam

    Google Scholar 

  • Quiroga O, Zeballos L, Henning G (2005) A constraint programming approach to tool allocation and resource scheduling in FMS. In: Proceedings of the 2005 IEEE international conference on robotics and automation. ICRA 2005, IEEE

  • Rabelo LC, Alptekin S, Kiran AS (1990) Synergy of artificial neural networks and knowledge-based expert systems for intelligent FMS scheduling. In: 1990 IJCNN international joint conference on neural networks, IEEE, pp 359–366

  • Raghavan NRS, Venkataramana M (2006) Scheduling parallel batch processors with incompatible job families using ant colony optimization. In: 2006 IEEE international conference on automation science and engineering, IEEE

  • Raja K, Arumugam C, Selladurai V (2008) Non-identical parallel-machine scheduling using genetic algorithm and fuzzy logic approach. Int J Serv Oper Manag 4(1):72–101

    Google Scholar 

  • Rajendran C, Ziegler H (2004) Ant-colony algorithms for permutation flow shop scheduling to minimize makespan/total flowtime of jobs. Eur J Oper Res 155(2):426–438

    Article  MATH  Google Scholar 

  • Rajpathak DG (2001) Intelligent scheduling—a literature review. Technical report KMI-TR-119, Knowledge Media Institute, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK

  • Rezaie N, Tavakkoli-Moghaddam R, Torabi SA (2009) A new mathematical model for fuzzy flexible flow shop scheduling of unrelated parallel machines maximizing the weighted satisfaction level. IFAC Proc 42(4):798–803

    Article  Google Scholar 

  • Ripon KSN, Tsang CH, Kwong S (2007) An evolutionary approach for solving the multi-objective job shop scheduling problem. In: Evolutionary scheduling, Springer, Berlin, pp 165–195

    Chapter  MATH  Google Scholar 

  • Rodriguez J (2007) A constraint programming model for real-time train scheduling at junctions. Transp Res Part B Methodol 41(2):231–245

    Article  Google Scholar 

  • Rostami M, Pilerood AE, Mazdeh MM (2015) Multi-objective parallel machine scheduling problem with job deterioration and learning effect under fuzzy environment. Comput Ind Eng 85:206–215

    Article  Google Scholar 

  • Russell RA, Urban TL (2006) A constraint programming approach to the multiple-venue, sport-scheduling problem. Comput Oper Res 33(7):1895–1906

    Article  MATH  Google Scholar 

  • Russell T, Malik AM, Chase M, Van Beek P (2009) Learning heuristics for the superblock instruction scheduling problem. IEEE Trans Knowl Data Eng 21(10):1489–1502

    Article  Google Scholar 

  • Sakawa M, Kubota R (2001) Two-objective fuzzy job shop scheduling through genetic algorithm. Electron Commun Jpn (Part III Fundam Electron Sci 84(4):60–68

    Article  Google Scholar 

  • Sakawa M, Mori T (1999) An efficient genetic algorithm for job shop scheduling problems with fuzzy processing time and fuzzy due date. Comput Ind Eng 36(2):325–341

    Article  Google Scholar 

  • Sathish S, Ganesan K (2016) Scheduling of flow shop problems on 3 machines in fuzzy environment with double transport facility. In: Innovations through mathematical and statistical research: proceedings of the 2nd international conference on mathematical sciences and statistics (ICMSS2016), vol 1739, no 1, AIP Publishing

  • Schaerf A (1999) Scheduling sport tournaments using constraint logic programming. Constraints 4(1):43–65

    Article  MathSciNet  MATH  Google Scholar 

  • Schalkoff RJ (1990) Artificial intelligence engine. McGraw-Hill Inc, New York

    Google Scholar 

  • Schirmer A (2000) Case-based reasoning and improved adaptive search for project scheduling. Naval Res Log (NRL) 47(3):201–222

    Article  MathSciNet  MATH  Google Scholar 

  • Schmidt G (1998) Case-based reasoning for production scheduling. Int J Prod Econ 56:537–546

    Article  Google Scholar 

  • Seçkiner SU, Kurt M (2008) Ant colony optimization for the job rotation scheduling problem. App Math Comput 201(1):149–160

    Article  MathSciNet  MATH  Google Scholar 

  • Sels V et al (2015) Hybrid tabu search and a truncated branch-and-bound for the unrelated parallel machine scheduling problem. Comput Oper Res 53:107–117

    Article  MathSciNet  MATH  Google Scholar 

  • Seraj O, Tavakkoli-Moghaddam R, Jolai F (2009) A fuzzy multi-objective tabu-search method for a new bi-objective open shop scheduling problem. In: International conference on computers and industrial engineering, 2009. CIE 2009, IEEE

  • Seredyński F, Koronacki J, Janikow CZ (1999) Distributed scheduling with decomposed optimization criterion: genetic programming approach. In: International parallel processing symposium, Springer, Berlin, pp 192–200

    Google Scholar 

  • Sevaux M, Jouglet A, Oguz C (2005) Combining constraint programming and memetic algorithm for the hybrid flowshop scheduling problem. In: ORBEL 19th annual conference of the SOGESCI-BVWB, Louvain-la-Neuve, Belgium, vol 25

  • Sha DY, Hsu Cheng-Yu (2008) A new particle swarm optimization for the open shop scheduling problem. Comput Oper Res 35(10):3243–3261

    Article  MATH  Google Scholar 

  • Sha DY, Lin H-H (2010) A multi-objective PSO for job shop scheduling problems. Expert Syst Appl 37(2):1065–1070

    Article  Google Scholar 

  • Shahzad A, Mebarki N (2016) Learning dispatching rules for scheduling: a synergistic view comprising decision trees, tabu search and simulation. Computers 5(1):3

    Article  Google Scholar 

  • Shaw MJ (1989) A pattern-directed approach to flexible manufacturing: a framework for intelligent scheduling, learning, and control. Int J Flex Manuf Syst 2(2):121–144

    Article  Google Scholar 

  • Shaw MJ, Park S, Raman N (1992) Intelligent scheduling with machine learning capabilities: the induction of scheduling knowledge. IIE Trans 24(2):156–168

    Article  Google Scholar 

  • Sheibani K (2010) A fuzzy greedy heuristic for permutation flow shop scheduling. J Oper Res Soc 61(5):813–818

    Article  MATH  Google Scholar 

  • Shen Y-J, Wang M-S (2008) Broadcast scheduling in wireless sensor networks using fuzzy Hopfield neural network. Expert Syst Appl 34(2):900–907

    Article  Google Scholar 

  • Shiau D-F (2011) A hybrid particle swarm optimization for a university course scheduling problem with flexible preferences. Expert Syst Appl 38(1):235–248

    Article  Google Scholar 

  • Shih W-C et al (2007) Parallel loop scheduling using knowledge-based workload estimation on grid environments. In: 2007 International symposium on applications and the internet, IEEE

  • Shine YR, Su CT (2002) Attribute selection for neural network-based adaptive scheduling systems in flexible manufacturing systems. Int J Adv Manuf Technol 20(7):532–544

    Article  Google Scholar 

  • Shivasankaran N, Senthilkumar P, Raja KV (2014) Hybrid non-dominated sorting simulated annealing algorithm for flexible job shop scheduling problems. In: ICT and critical infrastructure: proceedings of the 48th annual convention of computer society of India, vol I, Springer International Publishing, pp 101–107

  • Shivasankaran N, Kumar PS, Raja KV (2015) Hybrid sorting immune simulated annealing algorithm for flexible job shop scheduling. Int J Comput Intell Syst 8(3):455–466

    Article  Google Scholar 

  • Siler W, Buckley JJ (2005) Fuzzy expert systems and fuzzy reasoning. Wiley, New York

    MATH  Google Scholar 

  • Simankina T, Popova O (2014) Neural network application for scheduling of building constraction repair. In: Applied mechanics and materials, vol 584, Trans Tech Publications

  • Simon FYP, Takefuji T (1988) Integer linear programming neural networks for job shop scheduling. In: IEEE international conference on neural networks, IEEE, pp 341–348IEEE

  • Singh MR, Mahapatra SS (2016) A quantum behaved particle swarm optimization for flexible job shop scheduling. Comput Ind Eng 93:36–44

    Article  Google Scholar 

  • Sinha N, Chakrabarti R, Chattopadhyay PK (2003) Fast evolutionary programming techniques for short-term hydrothermal scheduling. Electric Power Syst Res 66(2):97–103

    Article  Google Scholar 

  • Sivanandam SN, Deepa SN (2008) Genetic algorithms. Introduction to genetic algorithms. Springer, Berlin, pp 15–37

    Book  MATH  Google Scholar 

  • Smith SF, Muscettola N, Matthys DC, Ow PS, Potvin JY (1990) OPIS: an opportunistic factory scheduling system. In: Proceedings of the 3rd international conference on industrial and engineering applications of artificial intelligence and expert systems, vol 1, ACM, pp 268–274

  • Song X et al (2006) Study on the combination of genetic algorithms and ant colony algorithms for solving fuzzy job shop scheduling problems. In: IMACS multiconference on computational engineering in systems applications, vol 2, IEEE

  • Soukour AA, Devendeville L, Lucet C, Moukrim A (2013) A memetic algorithm for staff scheduling problem in airport security service. Expert Syst Appl 40(18):7504–7512

    Article  Google Scholar 

  • Soykan B, Rabadi G (2016) A Tabu search algorithm for the multiple runway aircraft scheduling problem. In: Heuristics, metaheuristics and approximate methods in planning and scheduling, Springer International Publishing, pp 165–186

  • Srikanth UG et al (2012) Tasks scheduling using ant colony optimization. J Comput Sci 8(8):1314

    Article  MathSciNet  Google Scholar 

  • Starkweather T, Whitley D, Mathias K, McDaniel S (1992) Sequence scheduling with genetic algorithms. In: New directions for operations research in manufacturing, Springer, Berlin, pp 129–148

    Chapter  Google Scholar 

  • Steinhöfel K, Albrecht A, Wong CK (1999) Two simulated annealing-based heuristics for the job shop scheduling problem. Eur J Oper Res 118(3):524–548

    Article  MATH  Google Scholar 

  • Subramaniam V et al (2000) Job shop scheduling with dynamic fuzzy selection of dispatching rules. Int J Adv Manuf Technol 16(10):759–764

    Article  Google Scholar 

  • Suresh RK, Mohanasundaram KM (2006) Pareto archived simulated annealing for job shop scheduling with multiple objectives. Int J Adv Manuf Technol 29(1–2):184–196

    Article  Google Scholar 

  • Sutton AM, Neumann F (2012) A parameterized runtime analysis of simple evolutionary algorithms for makespan scheduling. In: International conference on parallel problem solving from nature, Springer, Berlin, pp 52–61

    Google Scholar 

  • Svozil D, Kvasnicka V, Pospichal J (1997) Introduction to multi-layer feed-forward neural networks. Chemom Intell Lab Syst 39(1):43–62

    Article  Google Scholar 

  • Switalski P, Seredynski F (2011) An efficient evolutionary scheduling algorithm for parallel job model in grid environment. In: International conference on parallel computing technologies, Springer, Berlin, pp 347–357

    Google Scholar 

  • Sycara K, Zeng D, Miyashita K (1995) Using case-based reasoning to acquire user scheduling preferences that change over time. In: Proceedings of 11th conference on artificial intelligence for applications, IEEE

  • Tahir MF, Asghar Saqib M (2016) Optimal scheduling of electrical power in energy-deficient scenarios using artificial neural network and Bootstrap aggregating. Int J Electr Power Energy Syst 83:49–57

    Article  Google Scholar 

  • Tang L, Liu W, Liu J (2005) A neural network model and algorithm for the hybrid flow shop scheduling problem in a dynamic environment. J Intell Manuf 16(3):361–370

    Article  Google Scholar 

  • Tang Y, Liu R, Sun Q (2014) Schedule control model for linear projects based on linear scheduling method and constraint programming. Autom Constr 37:22–37

    Article  Google Scholar 

  • Tang Y et al (2018) Scheduling optimization of linear schedule with constraint programming. Comput Aided Civ Infrastruct Eng 33(2):124–151

    Article  Google Scholar 

  • Tavakkoli-Moghaddam R, Javadi B, Jolai F, Ghodratnama A (2010) The use of a fuzzy multi-objective linear programming for solving a multi-objective single-machine scheduling problem. Appl Soft Comput 10(3):919–925

    Article  Google Scholar 

  • Teich T et al (2001) A new ant colony algorithm for the job shop scheduling problem. In: Proceedings of the 3rd annual conference on genetic and evolutionary computation, Morgan Kaufmann Publishers Inc

  • Teoh CK, Wibowo A, Ngadiman MS (2015) Review of state of the art for metaheuristic techniques in academic scheduling problems. Artif Intell Rev 44(1):1–21

    Article  Google Scholar 

  • Thammano A, Teekeng W (2015) A modified genetic algorithm with fuzzy roulette wheel selection for job shop scheduling problems. Int J Gen Syst 44(4):499–518

    Article  MathSciNet  MATH  Google Scholar 

  • Thiruvady D et al (2009) Hybridizing beam-aco with constraint programming for single machine job scheduling. In: International workshop on hybrid metaheuristics, Springer, Berlin

    Chapter  Google Scholar 

  • Timpe C (2002) Solving planning and scheduling problems with combined integer and constraint programming. OR Spectr 24(4):431–448

    Article  MATH  Google Scholar 

  • Tiwari PK, Vidyarthi DP (2014) Observing the effect of interprocess communication in auto controlled ant colony optimization-based scheduling on computational grid. Concurr Comput Pract Exp 26(1):241–270

    Article  Google Scholar 

  • T’kindt V et al (2002) An ant colony optimization algorithm to solve a 2-machine bicriteria flow shop scheduling problem. Eur J Oper Res 142(2):250–257

    Article  MATH  Google Scholar 

  • Topaloglu S, Ozkarahan I (2011) A constraint programming-based solution approach for medical resident scheduling problems. Comput Oper Res 38(1):246–255

    Article  MathSciNet  MATH  Google Scholar 

  • Torabi SA, Sahebjamnia N, Mansouri SA, Bajestani MA (2013) A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem. Appl Soft Comput 13(12):4750–4762

    Article  Google Scholar 

  • Toure S, Rabelo L, Velasco T (1993) Artificial neural networks for flexible manufacturing systems scheduling. Comput Ind Eng 25(1–4):385–388

    Article  Google Scholar 

  • Tran T-D et al (2014) Solving fuzzy job shop scheduling problems with a multiobjective optimizer. In: Knowledge and systems engineering, Springer International Publishing, pp 197–209

  • Trilling L, Guinet A, Le Magny D (2006) Nurse scheduling using integer linear programming and constraint programming. In: IFAC proceedings, vol 39.3, pp 671–676

    Article  Google Scholar 

  • Tripathy B, Dash S, Padhy SK (2015a) Dynamic task scheduling using a directed neural network. J Parallel Distrib Comput 75:101–106

    Article  Google Scholar 

  • Tripathy B, Dash S, Padhy SK (2015b) Multiprocessor scheduling and neural network training methods using shuffled frog-leaping algorithm. Comput Ind Eng 80:154–158

    Article  Google Scholar 

  • Trivedi A, Srinivasan D, Biswas S, Reindl T (2015) Hybridizing genetic algorithm with differential evolution for solving the unit commitment scheduling problem. Swarm Evolut Comput 23:50–64

    Article  Google Scholar 

  • Tsai CJ, Tseng SS, Wang CH, Yang CT, Jiang MF (1997) A fuzzy inductive learning algorithm for parallel loop scheduling. In: 1997 IEEE International conference on systems, man, and cybernetics, computational cybernetics and simulation, vol 1, IEEE, pp 178–183

  • Tsai J-T, Yang CI, Chou JH (2014) Hybrid sliding level Taguchi-based particle swarm optimization for flow shop scheduling problems. Appl Soft Comput 15:177–192

    Article  Google Scholar 

  • Tseng C-T, Liao C-J (2008) A discrete particle swarm optimization for lot-streaming flow shop scheduling problem. Eur J Oper Res 191(2):360–373

    Article  MATH  Google Scholar 

  • Turksen IB, Yurtsever T, Demirli K (1993) Fuzzy expert system shell for scheduling. In: Optical tools for manufacturing and advanced automation, International Society for Optics and Photonics

  • Unsal O, Oguz C (2013) Constraint programming approach to quay crane scheduling problem. Transp Res Part E Log Transp Rev 59:108–122

    Article  Google Scholar 

  • Vallada E, Ruiz R (2011) A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times. Eur J Oper Res 211(3):612–622

    Article  MathSciNet  Google Scholar 

  • Vallada E, Ruiz R, Minella G (2008) Minimising total tardiness in the m-machine flow shop problem: a review and evaluation of heuristics and metaheuristics. Comput Oper Res 35(4):1350–1373

    Article  MATH  Google Scholar 

  • Van Der Zwaan S, Marques C (1999) Ant colony optimisation for job shop scheduling. In: Proceedings of the’99 workshop on genetic algorithms and artficial life GAAL’99

  • Van Hentenryck P (1999) The OPL optimization programming language. MIT Press, Cambridge

    Google Scholar 

  • Van Peteghem V, Vanhoucke M (2010) A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. Eur J Oper Res 201(2):409–418

    Article  MathSciNet  MATH  Google Scholar 

  • vanHoeve W-J (2005) Operations research techniques in constraint programming. Tepper Sch Bus 19:532

    Google Scholar 

  • Varela R et al (2003) A knowledge-based evolutionary strategy for scheduling problems with bottlenecks. Eur J Oper Res 145(1):57–71

    Article  MathSciNet  MATH  Google Scholar 

  • Ventura JA, Yoon SH (2013) A new genetic algorithm for lot-streaming flow shop scheduling with limited capacity buffers. J Intell Manuf 24(6):1185–1196

    Article  Google Scholar 

  • Vilcot G, Billaut J-C (2011) A tabu search algorithm for solving a multicriteria flexible job shop scheduling problem. Int J Prod Res 49(23):6963–6980

    Article  Google Scholar 

  • Wan G, Wan F (2003) Job shop scheduling by taboo search with fuzzy reasoning. In: IEEE international conference on systems, man and cybernetics, vol 2, IEEE

  • Wang L, Tang DB (2011) An improved adaptive genetic algorithm based on hormone modulation mechanism for job shop scheduling problem. Expert Syst Appl 38(6):7243–7250

    Article  Google Scholar 

  • Wang S, Wang L (2015) A knowledge-based multi-agent evolutionary algorithm for semiconductor final testing scheduling problem. Knowl Based Syst 84:1–9

    Article  Google Scholar 

  • Wang L, Siegel HJ, Roychowdhury VP, Maciejewski AA (1997) Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach. J Parallel Distrib Comput 47(1):8–22

    Article  Google Scholar 

  • Wang C, Wang D, Ip WH, Yuen DW (2002) The single machine ready time scheduling problem with fuzzy processing times. Fuzzy Sets Syst 127(2):117–129

    Article  MathSciNet  MATH  Google Scholar 

  • Wang H, Jacob V, Rolland E (2003) Design of efficient hybrid neural networks for flexible flow shop scheduling. Expert Syst 20(4):208–231

    Article  Google Scholar 

  • Wang L, Zhang L, Zheng DZ (2006) An effective hybrid genetic algorithm for flow shop scheduling with limited buffers. Comput Oper Res 33(10):2960–2971

    Article  MathSciNet  MATH  Google Scholar 

  • Wang HM, Chou FD, Wu FC (2011) A simulated annealing for hybrid flow shop scheduling with multiprocessor tasks to minimize makespan. Int J Adv Manuf Technol 53(5–8):761–776

    Article  Google Scholar 

  • Wang Y et al (2012) An improved self-adaptive PSO technique for short-term hydrothermal scheduling. Expert Syst Appl 39(3):2288–2295

    Article  Google Scholar 

  • Wang S et al (2013) An effective estimation of distribution algorithm for the flexible job shop scheduling problem with fuzzy processing time. Int J Prod Res 51(12):3778–3793

    Article  Google Scholar 

  • Wang Yu et al (2015a) Particle swarm optimization-based planning and scheduling for a laminar-flow operating room with downstream resources. Soft Comput 19(10):2913–2926

    Article  Google Scholar 

  • Wang T, Meskens N, Duvivier D (2015b) Scheduling operating theatres: mixed integer programming versus constraint programming. Eur J Oper Res 247(2):401–413

    Article  MATH  Google Scholar 

  • Wang K, Huang Y, Qin H (2016a) A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flow shop scheduling problems under machine breakdown. J Oper Res Soc 67(1):68–82

    Article  Google Scholar 

  • Wang C, Abdul-Rahman H, See W, Chng WS (2016b) Ant colony optimization (ACO) in scheduling overlapping architectural design activities. J Civ Eng Manag 22(6):780–791

    Article  Google Scholar 

  • Wang DJ, Liu F, Jin Y (2017) A multi-objective evolutionary algorithm guided by directed search for dynamic scheduling. Comput Oper Res 79:279–290

    Article  MathSciNet  MATH  Google Scholar 

  • Watson J-P, Christopher Beck J (2008) A hybrid constraint programming/local search approach to the job-shop scheduling problem. In: International conference on integration of artificial intelligence (AI) and operations research (OR) techniques in constraint programming, Springer, Berlin

  • Weil G et al (1995) Constraint programming for nurse scheduling. IEEE Eng Med Biol Mag 14(4):417–422

    Article  Google Scholar 

  • Willems TM, Brandts LEMW (1995) Implementing heuristics as an optimization criterion in neural networks for job shop scheduling. J Intell Manuf 6(6):377–387

    Article  Google Scholar 

  • Wu C, Gu X (2004) A genetic algorithm for flow shop scheduling with fuzzy processing time and due date. In: Fifth world congress on intelligent control and automation, 2004. WCICA 2004, vol 4, IEEE

  • Wu X-Q, Carothers JD, Gassen D (1994) A neural network for scheduling and allocation in VLSI design. In: 1994 IEEE international conference on neural networks. IEEE world congress on computational intelligence, vol 3, IEEE

  • Wu CC, Hsu PH, Lai K (2011) Simulated-annealing heuristics for the single-machine scheduling problem with learning and unequal job release times. J Manuf Syst 30(1):54–62

    Article  Google Scholar 

  • Wu Z, Zhang C, Zhu X (2012) An ant colony algorithm for Master production scheduling optimization. In: 2012 IEEE 16th international conference on computer supported cooperative work in design (CSCWD), IEEE

  • Xia W-j, Wu Z-m (2006) A hybrid particle swarm optimization approach for the job shop scheduling problem. Int J Adv Manuf Technol 29(3-4):360–366

    Article  Google Scholar 

  • Xiang W, Yin J, Lim G (2015) An ant colony optimization approach for solving an operating room surgery scheduling problem. Comput Ind Eng 85:335–345

    Article  Google Scholar 

  • Xiao J, Ao X-T, Tang Y (2013) Solving software project scheduling problems with ant colony optimization. Comput Oper Res 40(1):33–46

    Article  MathSciNet  MATH  Google Scholar 

  • Xing L-N, Chen Y-W, Yang K-W (2007) Interactive fuzzy multi-objective ant colony optimization with linguistically quantified decision functions for flexible job shop scheduling problems. In: Frontiers in the convergence of bioscience and information technologies, 2007 (FBIT 2007), IEEE

  • Xing LN, Chen YW, Wang P, Zhao QS, Xiong J (2010) A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl Soft Comput 10(3):888–896

    Article  Google Scholar 

  • Xizheng Z, Yaonan W (2009) New mixed broadcast scheduling approach using neural networks and graph coloring in wireless sensor network. J Syst Eng Electron 20(1):185–191

    Google Scholar 

  • Xu X et al (2010a) An improved shuffled frog leaping algorithm for fuzzy flow shop scheduling problem. J East China Univ Sci Technol 5:019

    Google Scholar 

  • Xu K, Feng Z, Jun K (2010b) A tabu-search algorithm for scheduling jobs with controllable processing times on a single machine to meet due dates. Comput Oper Res 37(11):1924–1938

    Article  MathSciNet  MATH  Google Scholar 

  • Xu Y, Li K, Hu J, Li K (2014a) A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf Sci 270:255–287

    Article  MathSciNet  MATH  Google Scholar 

  • Xu H, Lü Z, Yin A, Shen L, Buscher U (2014b) A study of hybrid evolutionary algorithms for single machine scheduling problem with sequence-dependent setup times. Comput Oper Res 50:47–60

    Article  MathSciNet  MATH  Google Scholar 

  • Xu J, Yin Y, Cheng TCE, Wu CC, Gu S (2014c) A memetic algorithm for the re-entrant permutation flow shop scheduling problem to minimize the makespan. Appl Soft Comput 24:277–283

    Article  Google Scholar 

  • Xu Y et al (2015) An effective teaching–learning-based optimization algorithm for the flexible job shop scheduling problem with fuzzy processing time. Neurocomputing 148:260–268

    Article  Google Scholar 

  • Xujun Z, Zhimin L (2009) Model and solution for steelmaking-continuous casting scheduling problem based on constraint programming method. In: International conference on information technology and computer science, ITCS 2009, vol 1, IEEE

  • Yang S et al (2010) An improved constraint satisfaction adaptive neural network for job shop scheduling. J Sched 13(1):17–38

    Article  MathSciNet  MATH  Google Scholar 

  • Yeh WC (2002) A memetic algorithm for the n/2/flow shop/αF + βC max scheduling problem. Int J Adv Manuf Technol 20(6):464–473

    Article  Google Scholar 

  • Yeh WC, Lai PJ, Lee WC, Chuang MC (2014) Parallel-machine scheduling to minimize makespan with fuzzy processing times and learning effects. Inf Sci 269:142–158

    Article  MathSciNet  MATH  Google Scholar 

  • Yin J, Chen BJ (2011) Design and implementation of the supervisory control expert system for dynamic scheduling. In: Advanced materials research, vol 211, Trans Tech Publications

  • Ying K-C, Liao C-J (2003) An ant colony system approach for scheduling problems. Prod Plann Control 14(1):68–75

    Article  Google Scholar 

  • Ying K-C, Lin S-W (2006) Multiprocessor task scheduling in multistage hybrid flow shops: an ant colony system approach. Int J Prod Res 44(16):3161–3177

    Article  MATH  Google Scholar 

  • Yoon H-suk (2006) Optimization approaches to protein folding. Diss. Georgia Institute of Technology

  • Young JS, Lin YP, Shih PW (2013) Neural network approach to gain scheduling for traction control of electrical vehicles. In: Applied mechanics and materials, vol 392, Trans Tech Publications

  • Yu H, Liang W (2001) Neural network and genetic algorithm-based hybrid approach to expanded job shop scheduling. Comput Ind Eng 39(3):337–356

    Article  Google Scholar 

  • Yu I-K, Chou CS, Song Y-H (1998) Application of the ant colony search algorithm to short-term generation scheduling problem of thermal units. In: Proceedings of POWERCON’98. International conference on power system technology, vol 1, IEEE

  • Yun YS (2002) Genetic algorithm with fuzzy logic controller for preemptive and non-preemptive job shop scheduling problems. Comput Ind Eng 43(3):623–644

    Article  MathSciNet  Google Scholar 

  • Yun Y-S, Gen M (2002) Advanced scheduling problem using constraint programming techniques in SCM environment. Comput Ind Eng 43(1-2):213–229

    Article  Google Scholar 

  • Zamani R (2013) A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem. Eur J Oper Res 229(2):552–559

    Article  MathSciNet  MATH  Google Scholar 

  • Zarandi MF, Gamasaee R (2012) Type-2 fuzzy hybrid expert system for prediction of tardiness in scheduling of steel continuous casting process. Soft Comput 16(8):1287–1302

    Article  Google Scholar 

  • Zarandi MF, Azad FK (2013) A type 2 fuzzy multi agent based system for scheduling of steel production. In: 2013 Joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), IEEE, pp 992–996

  • Zarandi MHF, Esmaeilian M, Zarandi MMF (2007a) A systematic fuzzy system modeling for scheduling of textile manufacturing system. Int J Manag Sci Eng Manag 2(4):297–309

    Google Scholar 

  • Zarandi MHF, Zarandi MMF, Maknoon MY, Masoumi J (2007b) Scheduling of two and three machine robotic cells with fuzzy methodology. Int J Manag Sci Eng Manag 2(4):243–256

    Google Scholar 

  • Zarandi MF, Khorshidian H, Akbarpour Shirazi M (2016) A constraint programming model for the scheduling of JIT cross-docking systems with preemption. J Intell Manuf 27(2):297–313

    Article  Google Scholar 

  • Zeballos LJ (2010) A constraint programming approach to tool allocation and production scheduling in flexible manufacturing systems. Robot Comput Integr Manuf 26(6):725–743

    Article  Google Scholar 

  • Zeballos LJ, Castro PM, Méndez CA (2010a) Integrated constraint programming scheduling approach for automated wet-etch stations in semiconductor manufacturing. Ind Eng Chem Res 50(3):1705–1715

    Article  Google Scholar 

  • Zeballos LJ, Quiroga OD, Henning GP (2010b) A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations. Eng Appl Artif Intell 23(2):229–248

    Article  Google Scholar 

  • Zebullos L, Henning GP (2003) A constraint programming approach to the multi-stage batch scheduling problem. In: Foundation of computer-aided operations (FOCAPO), pp 343–346

  • Zhan S, Huo H (2012) Improved PSO-based task scheduling algorithm in cloud computing. J Inf Comput Sci 9(13):3821–3829

    Google Scholar 

  • Zhang R, Chiong R (2016) Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption. J Clean Prod 112:3361–3375

    Article  Google Scholar 

  • Zhang L, Wong TN (2015) An object-coding genetic algorithm for integrated process planning and scheduling. Eur J Oper Res 244(2):434–444

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang R, Wu C (2010) A hybrid immune simulated annealing algorithm for the job shop scheduling problem. Appl Soft Comput 10(1):79–89

    Article  Google Scholar 

  • Zhang H, Li H, Tam CM (2006) Particle swarm optimization for resource-constrained project scheduling. Int J Proj Manag 24(1):83–92

    Article  Google Scholar 

  • Zhang Q, Manier H, Manier M-A (2012a) A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times. Comput Oper Res 39(7):1713–1723

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang X, Lv Z, Song X (2012) Model and solution for hot strip rolling scheduling problem based on constraint programming method. In: 2012 IEEE 12th international conference on computer and information technology (CIT), IEEE

  • Zhang Z, Zhang N, Feng Z (2014) Multi-satellite control resource scheduling based on ant colony optimization. Expert Syst Appl 41(6):2816–2823

    Article  Google Scholar 

  • Zhao Z, Li X (2014) Scheduling elective surgeries with sequence-dependent setup times to multiple operating rooms using constraint programming. Oper Res Health Care 3(3):160–167

    Article  Google Scholar 

  • Zheng Y-l, Li Y-x, Lei D-m (2012) Multi-objective swarm-based neighborhood search for fuzzy flexible job shop scheduling. Int J Adv Manuf Technol 60(9–12):1063–1069

    Article  Google Scholar 

  • Zibran MF, Roy CK (2011) A constraint programming approach to conflict-aware optimal scheduling of prioritized code clone refactoring. In: 2011 11th IEEE working conference on source code analysis and manipulation, IEEE

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Fazel Zarandi, M.H., Sadat Asl, A.A., Sotudian, S. et al. A state of the art review of intelligent scheduling. Artif Intell Rev 53, 501–593 (2020). https://doi.org/10.1007/s10462-018-9667-6

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