Skip to main content
SpringerLink
Log in

An improved differential evolution algorithm for solving a distributed assembly flexible job shop scheduling problem

  • Regular Research Paper
  • Published:
Memetic Computing Aims and scope Submit manuscript

Abstract

The single-factory manufacturing is gradually transiting to the multi-factory collaborative production with the globalization. The decentralization of resources and the heterogeneity of the production modes make it difficult to solve this kind of problem. Therefore, the distributed assembly flexible job shop scheduling problem (DAFJSP) is studied. DAFJSP can be decomposed into several flexible job shop scheduling problems and several single machine factory scheduling problems. To begin with, a mixed integer linear programming model for the DAFJSP is formulated to minimize the earliness/tardiness and the total cost simultaneously. Then, an improved differential evolution simulated annealing algorithm (IDESAA) is proposed. The balanced scheduling algorithm is designed to trade off the two objectives. Two crossover and mutation operators are designed. Due to its strong robustness, simulated annealing is integrated to local search the best Pareto solutions. The greedy idea combined with the Non-Dominated Sorted selection is employed to select the offspring. Finally, comprehensive experiments are conducted and the results show that the proposed algorithm can solve DAFJSP effectively and efficiently.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Akram K, Kamal K, Zeb A (2016) Fast simulated annealing hybridized with quenching for solving job shop scheduling problem. Appl Soft Comput 49:510–523

    Article  Google Scholar 

  2. Behnamian J (2016) Graph colouring-based algorithm to parallel jobs scheduling on parallel factories. Int J Comput Integr Manuf 29(6):622–635

    Article  Google Scholar 

  3. Behnamian J, Ghomi SMTF (2016) A survey of multi-factory scheduling. J Intell Manuf 27(1):231–249

    Article  Google Scholar 

  4. Behnamian J (2017) Matheuristic for the decentralized factories scheduling problem. Appl Math Model 47:668–684

    Article  MathSciNet  Google Scholar 

  5. Bonomiy F, Kumar A (1990) Adaptive optimal load balancing in a nonhomogeneous multiserver system with a central job scheduler. Trans Comput 39(10):1232–1250

    Article  Google Scholar 

  6. Brandimarte P (1993) Routing and scheduling in a flexible job shop by tabu search. Ann Oper Res 41(3):157–183

    Article  MATH  Google Scholar 

  7. Chan FTS, Prakash A, Ma HL et al (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 

  8. Chen Z, Pundoor G (2006) Order assignment and scheduling in a supply chain. Oper Res 54(3):555–572

    Article  MathSciNet  MATH  Google Scholar 

  9. Deng J, Wang L, Wang S et al (2017) A competitive memetic algorithm for the distributed two-stage assembly flow-shop scheduling problem. Int J Prod Res 54(12):1–17

    Google Scholar 

  10. Gonzalez-neira EM, Ferone D, Hatami S et al (2017) A biased-randomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times. Simul Model Pract Theor 79:23–36

    Article  Google Scholar 

  11. Hatami S, Rubén R, Romano CA (2013) The distributed assembly permutation flowshop scheduling problem. Int J Prod Res 51(17):5292–5308

    Article  Google Scholar 

  12. Hatami S, Rubén R, Romano CA (2014) Two Simple Constructive algorithms for the Distributed Assembly Permutation Flowshop Scheduling Problem. Managing Complexity: Challenges for Industrial Engineering and Operations Management, pp 139–145. https://doi.org/10.1007/978-3-319-04705-8_16

    Google Scholar 

  13. Iavagnilio R, Mossa G, Mummolo G, Leva AD (2003) Production planning of a multi-site manufacturing system by hybrid modelling: a case study from the automotive industry. Int J Prod Econ 85(2):251–262

    Article  Google Scholar 

  14. Khalid MNA, Yusof UK (2014) Improved immune algorithm for optimizing distributed production scheduling problem in flexible manufacturing system subject to machine maintenance. Int J Math Comput Simul 8:18–30

    Google Scholar 

  15. Li JQ, Sang HY, Han YY et al (2018) Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions. J Clean Prod 181:584–598

    Article  Google Scholar 

  16. Li JQ, Pan QK, Tasgetiren MF (2014) A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities. Appl Math Model 38(3):1111–1132

    Article  MathSciNet  MATH  Google Scholar 

  17. Lin J, Zhang S (2016) An effective hybrid biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem. Comput Ind Eng 97:128–136

    Article  Google Scholar 

  18. Lu, P. H., Wu, M. C., Tan, H. et al. (2015). A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems. Journal of Intelligent Manufacturing, 1-16

  19. Naderi B, Ruiz R (2014) A scatter search algorithm for the distributed permutation flow shop scheduling problem. Eur J Oper Res 239(2):323–334

    Article  MATH  Google Scholar 

  20. Pan QK, Wang L, Qian B (2009) A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems. Comput Oper Res 36(8):2498–2511

    Article  MathSciNet  MATH  Google Scholar 

  21. Ribas I, Companys R, Tort-Martorell X (2017) Efficient heuristics for the parallel blocking flow shop scheduling problem. Expert Syst Appl 74:41–54

    Article  Google Scholar 

  22. Rifai AP, Nguyen HT, Dawal SZM (2016) Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling. Appl Soft Comput 40:42–57

    Article  Google Scholar 

  23. Sauer J, Suelmann G, Appelrath HJ (1998) Multi-site scheduling with fuzzy concepts. Int J Approx Reason 19:145–160

    Article  MATH  Google Scholar 

  24. 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 break down. J Oper Res Soc 67(1):68–82

    Article  Google Scholar 

  25. Wang L, Deng J, Wang SY (2016b) Survey on optimization algorithms for distributed shop scheduling. Control Decis 31(1):1–11 (in Chinese)

    MATH  Google Scholar 

  26. Wang SY, Wang L (2015) An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem. IEEE Trans Syst Man Cybern Syst 46(1):139–149

    Article  Google Scholar 

  27. Wu MC, Lin CS, Lin CH et al (2017) Effects of different chromosome representations in developing genetic algorithms to solve DFJS scheduling problems. Comput Oper Res 80:101–112

    Article  MathSciNet  MATH  Google Scholar 

  28. Wu X, Wu S (2017) An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem. J Intell Manuf 28(6):1441–1457

    Article  Google Scholar 

  29. Wu X, Sun Y (2018) A green scheduling algorithm for flexible job shop with energy-saving measures. J Clean Prod 172:3249–3264

    Article  Google Scholar 

  30. Zhang G, Xing K, Cao F (2017) Scheduling distributed flowshops with flexible assembly and set-up time to minimise Makespan. Int J Prod Res 5:1–19

    Google Scholar 

  31. Zhao X, Liu D, Liu H et al (2016) A failure remember-driven differential evolution. International Conference on Natural Computation, Changsha, China, pp 81-86

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant [Grant No. 51305024].

Author information

Authors and Affiliations

  1. Department of Logistics Engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Room 514B, Beijing, 100083, China

    Xiuli Wu, Xiajing Liu & Ning Zhao

Authors
  1. Xiuli Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiajing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ning Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiuli Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, X., Liu, X. & Zhao, N. An improved differential evolution algorithm for solving a distributed assembly flexible job shop scheduling problem. Memetic Comp. 11, 335–355 (2019). https://doi.org/10.1007/s12293-018-00278-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12293-018-00278-7

Keywords

Search

Navigation