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A State-of-the-Art Review on Meta-heuristics Application in Remanufacturing

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Abstract

The objective of this study is to present a state-of-the-art review on applications and trends of meta-heuristics for remanufacturing problems. This literature review mainly encompass the most popular and frequently employed meta-heuristics like genetic algorithm, artificial bee colony, particle swarm optimization, ant colony optimization, simulated annealing, tabu search, variable neighborhood search and the hybrid of these approaches. By adopting a systematic procedure consisting of article collection and article selection, this research work selected 123 articles for literature analysis. The selected literature is categorized based on the application area of the remanufacturing problem; meta-heuristics techniques i.e. individual or hybrid meta-heuristics used; and mathematical models objective type (single or multi-objective models); objective function types based on the sustainable dimension (economic, social or environmental objective); objective functions such as minimize cost/time, maximize profit, etc. considered in a single and multi-objective models. Based on the analysis it is found that production planning and scheduling is the most focused application area of remanufacturing followed with reverse logistics. Further, GA is the most popular individual meta-heuristics used for optimization of the problem. The analysis also finds that hybrid meta-heuristics have gained increased attention in the past few years. The majority of the remanufacturing optimization models considered single objective. The present study also suggests several research avenues for further investigation.

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References

  1. SitcharangsieS IW, Wong TC (2019) Decision makings in key remanufacturing activities to optimise remanufacturing outcomes: a review. J Clean Prod 232(20):1465–1481

    Google Scholar 

  2. Ilgin MA, Gupta SM (2012) Remanufacturing modeling and analysis. CRC Press

  3. Ijomah W (2002) A model-based definition of the generic remanufacturing business process. ProQuest Dissertations Publishing

  4. Junior ML, Filho MG (2012) Production planning and control for remanufacturing: literature review and analysis. Prod Plan Control 23(6):419–435

    Google Scholar 

  5. Lu Q, Ren Y, Jin H, Meng L, Li L, Zhang C, Sutherland JW (2020) A hybrid metaheuristic algorithm for a profit-oriented and energy-efficient disassembly sequencing problem. Robot Comput-Integr Manuf. https://doi.org/10.1016/j.rcim.2019.101828

    Article  Google Scholar 

  6. Zhang R, Ong SK, Nee AY (2015) A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling. Appl Soft Comput 37:521–532

    Google Scholar 

  7. Yi P, Huang M, Guo L, Shi T (2016) A retailer oriented closed-loop supply chain network design for end of life construction machinery remanufacturing. J Clean Prod 124:191–203

    Google Scholar 

  8. Su C, Shi Y, Dou J (2017) Multi-objective optimization of buffer allocation for remanufacturing system based on TS-NSGAII hybrid algorithm. J Clean Prod 166:756–770

    Google Scholar 

  9. Chan CK, ManN FF, Campbell JF (2020) Supply chain coordination with reverse logistics: a vendor/recycler-buyer synchronized cycles model. Omega 95:102090. https://doi.org/10.1016/j.omega.2019.07.006

    Article  Google Scholar 

  10. Parsa S, Saadat M (2019) Intelligent selective disassembly planning based on disassemblability characteristics of product components. Int J Adv Manuf Technol 104:1769–1783

    Google Scholar 

  11. Steeneck DW, Sarin SC (2013) Pricing and production planning for reverse supply chain: a review. Int J Prod Res 51(23–24):6972–6989

    Google Scholar 

  12. Griffis SE, BellJE CDJ (2012) Metaheuristics in logistics and supply chain management. J Bus Logist 33(2):90–106

    Google Scholar 

  13. Kaveh A (2017) Advances in metaheuristic algorithms for optimal design of structures, 2nd edn. Springer, Switzerland

    MATH  Google Scholar 

  14. Gao KZ, He ZM, Huang Y, Duan PY, Suganthan PN (2020) A survey on meta-heuristics for solving disassembly line balancing, planning and scheduling problems in remanufacturing. Swarm Evol Comput 57:100719

    Google Scholar 

  15. Kerin M, Pham DT (2019) A review of emerging industry 4.0 technologies in remanufacturing. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.117805

    Article  Google Scholar 

  16. Peters K (2016) Methodological issues in life cycle assessment for remanufactured products: a critical review of existing studies and an illustrative case study. J Clean Prod 126:21–37

    Google Scholar 

  17. Jena SK, Sarmah SP (2016) Future aspect of acquisition management in closed-loop supply chain. Int J Sustain Eng 9(4):266–276

    Google Scholar 

  18. Wei S, Tang O, Sundin E (2015) Core (product) Acquisition management for remanufacturing: a review. J Remanuf 5(1):4. https://doi.org/10.1186/s13243-015-0014-7

    Article  Google Scholar 

  19. Goodall P, Rosamond E, Harding J (2014) A review of the state of the art in tools and techniques used to evaluate remanufacturing feasibility. J Clean Prod 81:1–15

    Google Scholar 

  20. Morgan SD, Gagnon RJ (2013) A systematic literature review of remanufacturing scheduling. Int J Prod Res 51(16):4853–4879

    Google Scholar 

  21. Akçalı E, Cetinkaya S (2011) Quantitative models for inventory and production planning in closed-loop supply chains. Int J Prod Res 49(8):2373–2407

    Google Scholar 

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

    Google Scholar 

  23. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS'95. Proceedings of the sixth international symposium on micro machine and human science, IEEE, pp 39–43

  24. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol 4, pp 1942–1948

  25. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyesuniversity, Engineering Faculty, Computer Engineering Department, vol 200, pp 1–10

  26. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B Cybern 26(1):29–41

    Google Scholar 

  27. Dorigo M, Gambardella LM (1997) Ant colonies for the travelling salesman problem. Biosystems 43(2):73–81

    Google Scholar 

  28. Stützle T, Hoos HH (1996) Improving the ant system: a detailed report on the MAX–MIN Ant System. FG Intellektik, FB Informatik, TU Darmstadt, Germany, Tech. Rep. AIDA–96–12

  29. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    MathSciNet  MATH  Google Scholar 

  30. Glover F (1989) Tabu search—part I. ORSA J Comput 1(3):190–206

    MATH  Google Scholar 

  31. Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24(11):1097–1100

    MathSciNet  MATH  Google Scholar 

  32. Raidl GR (2015) Decomposition based hybrid metaheuristics. Eur J Oper Res 244(1):66–76

    MathSciNet  MATH  Google Scholar 

  33. PellerinR PN, Berthaut F (2019) A survey of hybrid metaheuristics for the resource-constrained project scheduling problem. Eur J Oper Res. https://doi.org/10.1016/j.ejor.2019.01.063

    Article  Google Scholar 

  34. Jabbour CJC, de Sousa Jabbour ABL, Sarkis J (2019) Unlocking effective multi-tier supply chain management for sustainability through quantitative modeling: Lessons learned and discoveries to be made. Int J Prod Econ 217:11–30

    Google Scholar 

  35. Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14(3):207–222

    Google Scholar 

  36. Guide VDR Jr (2000) Production planning and control for remanufacturing: industry practice and research needs. J Oper Manag 18(4):467–483

    Google Scholar 

  37. Gong G, Deng Q, Chiong R, Gong X, Huang H, Han W (2020) Remanufacturing-oriented process planning and scheduling: mathematical modelling and evolutionary optimisation. Int J Prod Res 58(12):3781–3799

    Google Scholar 

  38. Li LL, Li CB, Li L, Tang Y, Yang QS (2019) An integrated approach for remanufacturing job shop scheduling with routing alternatives. Math Biosci Eng MBE 16(4):2063–2085

    MathSciNet  Google Scholar 

  39. Wen H, Hou S, Liu Z, Liu Y (2017) An optimization algorithm for integrated remanufacturing production planning and scheduling system. Chaos, Solitons Fractals 105:69–76

    MathSciNet  MATH  Google Scholar 

  40. Ansari ZN, Kant R, Shankar R (2020) Evaluation and ranking of solutions to mitigate sustainable remanufacturing supply chain risks: a hybrid fuzzy SWARA-fuzzy COPRAS framework approach. Int J Sustain Eng. https://doi.org/10.1080/19397038.2020.1758973

    Article  Google Scholar 

  41. Paydar MM, Olfati M (2018) Designing and solving a reverse logistics network for polyethylene terephthalate bottles. J Clean Prod 195:605–617

    Google Scholar 

  42. Kongar E, Gupta SM (2006) Disassembly sequencing using genetic algorithm. Int J Adv Manuf Technol 30(5–6):497–506

    Google Scholar 

  43. Ren Y, Zhang C, Zhao F, Xiao H, Tian G (2018) An asynchronous parallel disassembly planning based on genetic algorithm. Eur J Oper Res 269(2):647–660

    MathSciNet  MATH  Google Scholar 

  44. Huang M, Yi P, Guo L, Shi T (2016) A modal interval based genetic algorithm for closed-loop supply chain network design under uncertainty. IFAC-PapersOnLine 49(12):616–621

    Google Scholar 

  45. Zhen L, Sun Q, Wang K, Zhang X (2019) Facility location and scale optimisation in closed-loop supply chain. Int J Prod Res 57(24):7567–7585

    Google Scholar 

  46. Yazdian SA, Shahanaghi K, Makui A (2016) Joint optimisation of price, warranty and recovery planning in remanufacturing of used products under linear and non-linear demand, return and cost functions. Int J Syst Sci 47(5):1155–1175

    MathSciNet  MATH  Google Scholar 

  47. Alshamsi A, Diabat A (2017) A genetic algorithm for reverse logistics network design: a case study from the GCC. J Clean Prod 151:652–669

    Google Scholar 

  48. Kar AK (2016) Bio inspired computing–a review of algorithms and scope of applications. Expert Syst Appl 59:20–32

    Google Scholar 

  49. Guo J, He L, Gen M (2019) Optimal strategies for the closed-loop supply chain with the consideration of supply disruption and subsidy policy. Comput Ind Eng 128:886–893

    Google Scholar 

  50. Reeves C (2003) Genetic algorithms’. In: Glover, Kochenberger GA (eds) Handbook of metaheuristics. Kluwer Academic Publishers, Boston, pp 55–82

    Google Scholar 

  51. Shokouhyar S, Aalirezaei A (2017) Designing a sustainable recovery network for waste from electrical and electronic equipment using a genetic algorithm. Int J Environ Sustain Dev 16(1):60–79

    Google Scholar 

  52. Yang SS, Nasr N, Ong SK, Nee AYC (2016) A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning. Adv Manuf 4(3):189–201

    Google Scholar 

  53. Badurdeen F, Aydin R, Brown A (2018) A multiple lifecycle-based approach to sustainable product configuration design. J Clean Prod 200:756–769

    Google Scholar 

  54. Yang Y, Huang Z, Qiang QP, Zhou G (2017) A mathematical programming model with equilibrium constraints for competitive closed-loop supply chain network design. Asia-Pacific J Oper Res 34(05):1–31

    MathSciNet  MATH  Google Scholar 

  55. Zhang L, Zhao X, Ke Q, Dong W, Zhong Y (2019) Disassembly line balancing optimization method for high efficiency and low carbon emission. Int J Precis Eng Manuf-Green Technol. https://doi.org/10.1007/s40684-019-00140-2

    Article  Google Scholar 

  56. Wang Q, Tang D, Li S, Yang J, Salido MA, Giret A, Zhu H (2019) An optimization approach for the coordinated low-carbon design of product family and remanufactured products. Sustainability 11(2):460

    Google Scholar 

  57. Kalayci CB, Polat O, Gupta SM (2016) A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem. Ann Oper Res 242(2):321–354

    MathSciNet  MATH  Google Scholar 

  58. Ting TO, Yang XS, Cheng S, Huang K (2015) Hybrid metaheuristic algorithms: past, present, and future. In: Recent advances in swarm intelligence and evolutionary computation, Springer, Cham, pp 71–83

  59. Zhou XC, Zhao ZX, Zhou KJ, He CH (2012) Remanufacturing closed-loop supply chain network design based on genetic particle swarm optimization algorithm. J Central South Univ 19(2):482–487

    Google Scholar 

  60. Messmann L, Zender V, Thorenz A, Tuma A (2020) How to quantify social impacts in strategic supply chain optimization: state of the art. J Clean Prod 257:120459

    Google Scholar 

  61. Liu J, Zhou Z, Pham DT, Xu W, Ji C, Liu Q (2020) Collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem using improved discrete Bees algorithm in remanufacturing. Robot Comput-Integr Manuf . https://doi.org/10.1016/j.rcim.2019.101829

    Article  Google Scholar 

  62. Jiang Z, Ding Z, Liu Y, Wang Y, Hu X, Yang Y (2020) A data-driven based decomposition–integration method for remanufacturing cost prediction of end-of-life products. Robot Comput-Integr Manuf. https://doi.org/10.1016/j.rcim.2019.101838

    Article  Google Scholar 

  63. Wang Y, Jiang Z, Hu X, Li C (2020) Optimization of reconditioning scheme for remanufacturing of used parts based on failure characteristics. Robot Comput-Integr Manuf 61:101833. https://doi.org/10.1016/j.rcim.2019.101833

    Article  Google Scholar 

  64. Ren Y, Meng L, Zhang C, Zhao F, Saif U, Huang A, Mendis GP, Sutherland JW (2020) An efficient metaheuristics for a sequence-dependent disassembly planning. J Clean Prod 245:118644. https://doi.org/10.1016/j.jclepro.2019.118644

    Article  Google Scholar 

  65. Liu W, Ma W, Hu Y, Jin M, Li K, Chang X, Yu X (2019) Production planning for stochastic manufacturing/remanufacturing system with demand substitution using a hybrid ant colony system algorithm. J Clean Prod 213:999–1010

    Google Scholar 

  66. Bensmain Y, Dahane M, Bennekrouf M, Sari Z (2019) Preventive remanufacturing planning of production equipment under operational and imperfect maintenance constraints: a hybrid genetic algorithm based approach. Reliab Eng Syst Saf 185:546–566

    Google Scholar 

  67. Liu Q, Shang Z, Ding K, Guo L, Zhang L (2019) Multi-process routes based remanufacturability assessment and associated application on production decision. J Clean Prod 240:118114. https://doi.org/10.1016/j.jclepro.2019.118114

    Article  Google Scholar 

  68. Zhang HP (2019) Optimization of remanufacturing production scheduling considering uncertain factors. Int J Simul Model 18(2):344–354

    Google Scholar 

  69. Yan R, Yan B (2019) Location model for a remanufacturing reverse logistics network based on adaptive genetic algorithm. SIMULATION. https://doi.org/10.1177/0037549718821162

    Article  Google Scholar 

  70. Geda MW, Kwong CK, Jiang H (2019) Fastening method selection with simultaneous consideration of product assembly and disassembly from a remanufacturing perspective. Int J Adv Manuf Technol 101(5–8):1481–1493

    Google Scholar 

  71. Jiang Z, Wang H, Zhang H, Mendis G, Sutherland JW (2019) Value recovery options portfolio optimization for remanufacturing end of life product. J Clean Prod 210:419–431

    Google Scholar 

  72. Zhang F, Guan Z, Zhang L, Cui Y, Yi P, Ullah S (2019) Inventory management for a remanufacture-to-order production with multi-components (parts). J Intell Manuf 30(1):59–78

    Google Scholar 

  73. Ren Y, Gao D, Xu L (2019) Prediction of service life of large centrifugal compressor remanufactured impeller based on clustering rough set and fuzzy Bandelet neural network. Appl Soft Comput 78:132–140

    Google Scholar 

  74. Li D, Liu C, Li K (2019) A remanufacturing logistics network model based on improved multi-objective ant colony optimization. J Eur Syst Autom 52(4):391–395

    Google Scholar 

  75. Tran LV, Huynh BH, Akhtar H (2019) Ant colony optimization algorithm for maintenance, repair and overhaul scheduling optimization in the context of industrie 4.0. Appl Sci 9(22):4815. https://doi.org/10.3390/app9224815

    Article  Google Scholar 

  76. Cao J, Xia X, Wang L, Zhang Z, Liu X (2019) A novel multi-efficiency optimization method for disassembly line balancing problem. Sustainability 11(24):6969. https://doi.org/10.3390/su11246969

    Article  Google Scholar 

  77. Zhao J, Peng S, Li T, Lv S, Li M, Zhang H (2019) Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level. Front Mech Eng 14(4):474–488

    Google Scholar 

  78. Peng H, Wang H, Chen D (2019) Optimization of remanufacturing process routes oriented toward eco-efficiency. Front Mech Eng 14(4):422–433

    Google Scholar 

  79. He P (2018) Optimization and simulation of remanufacturing production scheduling under uncertainties. Int J Simul Model 17(4):734–743

    Google Scholar 

  80. Torkaman S, Ghomi SF, Karimi B (2018) Hybrid simulated annealing and genetic approach for solving a multi-stage production planning with sequence-dependent setups in a closed-loop supply chain. Appl Soft Comput 71:1085–1104

    Google Scholar 

  81. Goli A, Davoodi SMR (2018) Coordination policy for production and delivery scheduling in the closed loop supply chain. Prod Eng Res Devel 12(5):621–631

    Google Scholar 

  82. Liao TY (2018) Reverse logistics network design for product recovery and remanufacturing. Appl Math Model 60:145–163

    MathSciNet  MATH  Google Scholar 

  83. Benaissa M, Slama I, Dhiaf MM (2018) Reverse Logistics Network Problem using simulated annealing with and without priority-algorithm. Arch Transp 47(3):7–17

    Google Scholar 

  84. Tian G, Ren Y, Feng Y, Zhou M, Zhang H, Tan J (2018) Modeling and planning for dual-objective selective disassembly using AND/OR graph and discrete artificial bee colony. IEEE Trans Indu Inf 15(4):2456–2468

    Google Scholar 

  85. Ren Y, Zhang C, Zhao F, Triebe MJ, Meng L (2018) An MCDM-based multiobjective general variable neighborhood search approach for disassembly line balancing problem. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/TSMC.2018.2862827

    Article  Google Scholar 

  86. Gong G, Deng Q, Gong X, Zhang L, Wang H, Xie H (2018) A bee evolutionary algorithm for multiobjective vehicle routing problem with simultaneous pickup and delivery. Math Probl Eng. https://doi.org/10.1155/2018/2571380

    Article  Google Scholar 

  87. Koken P, Seok H, Yoon SW (2018) A simulated annealing algorithm with neighbourhood list for capacitated dynamic lot-sizing problem with returns and hybrid products. Int J Comput Integr Manuf 31(8):739–747

    Google Scholar 

  88. Koken P, Raghavan VA, Yoon SW (2018) A genetic algorithm based heuristic for dynamic lot sizing problem with returns and hybrid products. Comput Ind Eng 119:453–464

    Google Scholar 

  89. Gao Y, Feng Y, Wang Q, Zheng H, Tan J (2018) A multi-objective decision making approach for dealing with uncertainty in EOL product recovery. J Clean Prod 204:712–725

    Google Scholar 

  90. Yuchi Q, Wang N, He Z, Chen H (2018) Hybrid heuristic for the location-inventory-routing problem in closed-loop supply chain. Int Trans Oper Res. https://doi.org/10.1111/itor.12621

    Article  Google Scholar 

  91. Ray A, Mondal S (2018) An optimisation approach for finding an optimal pricing in a hybrid manufacturing/remanufacturing system. Int J Ind Syst Eng 29(1):1–18

    Google Scholar 

  92. Torkaman S, Ghomi SF, Karimi B (2017) Multi-stage multi-product multi-period production planning with sequence-dependent setups in closed-loop supply chain. Comput Ind Eng 113:602–613

    Google Scholar 

  93. Wang H, Jiang Z, Zhang X, Wang Y, Wang Y (2017) A fault feature characterization based method for remanufacturing process planning optimization. J Clean Prod 161:708–719

    Google Scholar 

  94. Chen X, Chuluunsukh A, Yun Y (2017) Integrated logistics network optimization by using a hybrid genetic algorithm. New Phys Sae Mulli 67(5):628–637

    Google Scholar 

  95. Jeng SY, Lin CWR (2017) Fuzzy cradle to cradle remanufacturing planning for a recycled toner cartridge industry. Int J Ind Syst Eng 25(4):423–442

    Google Scholar 

  96. Ren Y, Tian G, Zhao F, Yu D, Zhang C (2017) Selective cooperative disassembly planning based on multi-objective discrete artificial bee colony algorithm. Eng Appl Artif Intell 64:415–431

    Google Scholar 

  97. Liu J, Wang S (2017) Balancing disassembly line in product recovery to promote the coordinated development of economy and environment. Sustainability 9(2):309

    Google Scholar 

  98. Cui YY, Guan Z, Saif U, Zhang L, Zhang F, Mirza J (2017) Close loop supply chain network problem with uncertainty in demand and returned products: Genetic artificial bee colony algorithm approach. J Clean Prod 162:717–742

    Google Scholar 

  99. Song BD, Ko YD (2017) Effect of inspection policies and residual value of collected used products: a mathematical model and genetic algorithm for a closed-loop green manufacturing system. Sustainability 9(9):1589. https://doi.org/10.3390/su9091589

    Article  Google Scholar 

  100. Turki S, Didukh S, Sauvey C, Rezg N (2017) Optimization and analysis of a manufacturing–remanufacturing–transport–warehousing system within a closed-loop supply chain. Sustainability 9(4):561. https://doi.org/10.3390/su9040561

    Article  Google Scholar 

  101. Cho S, Jun HB, Kiritsis D (2017) Heuristic algorithms for maximising the total profit of end-of-life computer remanufacturing. Int J Prod Res 55(5):1350–1367

    Google Scholar 

  102. Yazdian SA, Shahanaghi K, Naini SGJ (2017) Integrated marketing and operational decisions in remanufacturing of end-of-life products: model and hybrid solution algorithms. Int J Oper Res 29(1):34–66

    MathSciNet  Google Scholar 

  103. Maleki L, Pasandideh SHR, Niaki STA, Cárdenas-Barrón LE (2017) Determining the prices of remanufactured products, capacity of internal workstations and the contracting strategy within queuing framework. Appl Soft Comput 54:313–321

    Google Scholar 

  104. Wu Z, Kwong CK, Aydin R, Tang J (2017) A cooperative negotiation embedded NSGA-II for solving an integrated product family and supply chain design problem with remanufacturing consideration. Appl Soft Comput 57:19–34

    Google Scholar 

  105. Gao KZ, Suganthan PN, Pan QK, Chua TJ, Chong CS, Cai TX (2016) An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time. Expert Syst Appl 65:52–67

    Google Scholar 

  106. Gao KZ, Suganthan PN, Pan QK, Tasgetiren MF, Sadollah A (2016) Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion. Knowl-Based Syst 109:1–16

    Google Scholar 

  107. Jing Y, Li W, Wang X, Deng L (2016) Production planning with remanufacturing and back-ordering in a cooperative multi-factory environment. Int J Comput Integr Manuf 29(6):692–708

    Google Scholar 

  108. Roshani A, Giglio D, Paolucci M (2016) A simulated annealing approach for the capacitated dynamic lot sizing problem in a closed remanufacturing system. IFAC-PapersOnLine 49(12):1496–1501

    Google Scholar 

  109. Jiang Z, Zhou T, Zhang H, Wang Y, Cao H, Tian G (2016) Reliability and cost optimization for remanufacturing process planning. J Clean Prod 135:1602–1610

    Google Scholar 

  110. Aydin R, Kwong CK, Ji P (2016) Coordination of the closed-loop supply chain for product line design with consideration of remanufactured products. J Clean Prod 114:286–298

    Google Scholar 

  111. Liang Y, Qiao PL, Luo ZY, Song LL (2016) Constrained stochastic joint replenishment problem with option contracts in spare parts remanufacturing supply chain. Int J Simul Model 15(3):553–565

    Google Scholar 

  112. Chen JM, Chang CI (2016) Pricing and collection effort decisions in a closed-loop supply chain. J Ind Prod Eng 33(8):568–578

    Google Scholar 

  113. Liu M, Liu C, Xing L, Liu Z, Li X, Lin L (2016) Assembly process control method for remanufactured parts with variable quality grades. Int J Adv Manuf Technol 85(5–8):1471–1481

    Google Scholar 

  114. Yuchi Q, He Z, Yang Z, Wang N (2016) A location-inventory-routing problem in forward and reverse logistics network design. Discret Dyn Nat Soc. https://doi.org/10.1155/2016/3475369

    Article  MathSciNet  MATH  Google Scholar 

  115. Wu Z, Kwong CK, Lee CKM, Tang J (2016) Joint decision of product configuration and remanufacturing for product family design. Int J Prod Res 54(15):4689–4702

    Google Scholar 

  116. Gao KZ, SuganthanPN CTJ, Chong CS, Cai TX, Pan QK (2015) A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion. Expert Syst Appl 42(21):7652–7663

    Google Scholar 

  117. Lin D, Teo CC, Lee CKM (2015) Heuristics for integrated job assignment and scheduling in the multi-plant remanufacturing system. Int J Prod Res 53(9):2674–2689

    Google Scholar 

  118. Pasandideh SHR, Niaki STA, Maleki L (2015) A bi-objective remanufacturing problem within queuing framework: an imperialist competitive algorithm. Int J Manag Sci Eng Manag 10(3):199–209

    Google Scholar 

  119. Kumar VV, Liou FW, Balakrishnan SN, Kumar V (2015) Economical impact of RFID implementation in remanufacturing: a Chaos-based Interactive Artificial Bee Colony approach. J Intell Manuf 26(4):815–830

    Google Scholar 

  120. Guo J, Ya G (2015) Optimal strategies for manufacturing/remanufacturing system with the consideration of recycled products. Comput Ind Eng 89:226–234

    Google Scholar 

  121. Parsopoulos KE, Konstantaras I, Skouri K (2015) Metaheuristic optimization for the single-item dynamic lot sizing problem with returns and remanufacturing. Comput Ind Eng 83:307–315

    Google Scholar 

  122. Piñeyro P, Viera O (2015) The economic lot-sizing problem with remanufacturing: analysis and an improved algorithm. J Remanuf 5(1):12. https://doi.org/10.1186/s13243-015-0021-8

    Article  Google Scholar 

  123. Sifaleras A, Konstantaras I (2015) General variable neighborhood search for the multi-product dynamic lot sizing problem in closed-loop supply chain. Electron Not Discrete Math 47:69–76

    MathSciNet  MATH  Google Scholar 

  124. Sifaleras A, Konstantaras I, Mladenović N (2015) Variable neighborhood search for the economic lot sizing problem with product returns and recovery. Int J Prod Econ 160:133–143

    Google Scholar 

  125. Aydin R, Kwong CK, Ji P (2015) A novel methodology for simultaneous consideration of remanufactured and new products in product line design. Int J Prod Econ 169:127–140

    Google Scholar 

  126. Dem H, Singh SR (2015) Joint replenishment modelling of a multi–item system with greening policy and volume flexibility. Int J Oper Res 22(2):148–166

    MathSciNet  MATH  Google Scholar 

  127. Li L, Li C, Ma H, Tang Y (2015) An optimization method for the remanufacturing dynamic facility layout problem with uncertainties. Discret Dyn Nat Soc. https://doi.org/10.1155/2015/685408

    Article  MathSciNet  Google Scholar 

  128. Pasandideh SHR, Niaki STA, Maleki L (2014) A queuing approach for a tri-objective manufacturing problem with defects: a tuned Pareto-based genetic algorithm. Int J Adv Manuf Technol 73(9–12):1373–1385

    Google Scholar 

  129. Liu D (2014) Network site optimization of reverse logistics for E-commerce based on genetic algorithm. Neural Comput Appl 25(1):67–71

    Google Scholar 

  130. Roghanian E, Pazhoheshfar P (2014) An optimization model for reverse logistics network under stochastic environment by using genetic algorithm. J Manuf Syst 33(3):348–356

    Google Scholar 

  131. Subulan K, Baykasoğlu A, Saltabaş A (2014) An improved decoding procedure and seeker optimization algorithm for reverse logistics network design problem. J Intell Fuzzy Syst 27(6):2703–2714

    Google Scholar 

  132. Tanimizu Y, Shimizu Y (2014) A study on closed-loop supply chain model for parts reuse with economic efficiency. J Adv Mech Des Syst Manuf. https://doi.org/10.1299/jamdsm.2014jamdsm0068

    Article  Google Scholar 

  133. Li X, Baki F, Tian P, Chaouch BA (2014) A robust block-chain based tabu search algorithm for the dynamic lot sizing problem with product returns and remanufacturing. Omega 42(1):75–87

    Google Scholar 

  134. Zolfagharinia H, Hafezi M, Farahani RZ, Fahimnia B (2014) A hybrid two-stock inventory control model for a reverse supply chain. Transp Res Part E Logist Transp Rev 67:141–161

    Google Scholar 

  135. Liu M, Liu C, Zhu Q (2014) Optional classification for reassembly methods with different precision remanufactured parts. Assem Autom 34(4):315–322

    Google Scholar 

  136. Azadeh A, Mazaheri E (2013) Optimum production planning of a dedicated remanufacturing process by an integrated simulation-based optimisation approach. Int J Logist Syst Manag 16(1):67–84

    Google Scholar 

  137. Diabat A, Kannan D, Kaliyan M, Svetinovic D (2013) An optimization model for product returns using genetic algorithms and artificial immune system. Resour Conserv Recycl 74:156–169

    Google Scholar 

  138. Li WD, Xia K, Gao L, Chao KM (2013) Selective disassembly planning for waste electrical and electronic equipment with case studies on liquid crystal displays. Robot Comput-Integr Manuf 29(4):248–260

    Google Scholar 

  139. Prakash PKS, Ceglarek D, Tiwari MK (2012) Constraint-based simulated annealing (CBSA) approach to solve the disassembly scheduling problem. Int J Adv Manuf Technol 60(9–12):1125–1137

    Google Scholar 

  140. Zhang J, Liu X, Tu YL (2011) A capacitated production planning problem for closed-loop supply chain with remanufacturing. Int J Adv Manuf Technol 54(5–8):757–766

    Google Scholar 

  141. KumarVV CFT (2011) A superiority search and optimisation algorithm to solve RFID and an environmental factor embedded closed loop logistics model. Int J Prod Res 49(16):4807–4831

    Google Scholar 

  142. Karamouzian A, Teimoury E, Modarres M (2011) A model for admission control of returned products in a remanufacturing facility using queuing theory. Int J Adv Manuf Technol 54(1–4):403–412

    Google Scholar 

  143. Trappey AJ, Trappey CV, Wu CR (2010) Genetic algorithm dynamic performance evaluation for RFID reverse logistic management. Expert Syst Appl 37(11):7329–7335

    Google Scholar 

  144. Chu LK, Shi Y, Lin S, Sculli D, Ni J (2010) Fuzzy chance-constrained programming model for a multi-echelon reverse logistics network for household appliances. J Oper Res Soc 61(4):551–560

    MATH  Google Scholar 

  145. Pineyro P, Viera O (2010) The economic lot-sizing problem with remanufacturing and one-way substitution. Int J Prod Econ 124(2):482–488

    Google Scholar 

  146. Chang YJ, Yao MJ (2009) A genetic algorithm for solving the economic lot scheduling problem with reworks. J Chin Inst Ind Eng 26(5):411–425

    Google Scholar 

  147. Kannan G (2009) A metaheuristics-based decision support system for the performance measurement of reverse supply chain management. Int J Bus Perform Manag 11(1–2):152–169

    Google Scholar 

  148. Li J, González M, Zhu Y (2009) A hybrid simulation optimization method for production planning of dedicated remanufacturing. Int J Prod Econ 117(2):286–301

    Google Scholar 

  149. Roy A, Maity K, Maiti M (2009) A production–inventory model with remanufacturing for defective and usable items in fuzzy-environment. Comput Ind Eng 56(1):87–96

    Google Scholar 

  150. Lee CKM, Chan TM (2009) Development of RFID-based reverse logistics system. Expert Syst Appl 36(5):9299–9307

    Google Scholar 

  151. Lee JE, Gen M, Rhee KG (2009) Network model and optimization of reverse logistics by hybrid genetic algorithm. Comput Ind Eng 56(3):951–964

    Google Scholar 

  152. Easwaran G, Üster H (2009) Tabu search and benders decomposition approaches for a capacitated closed-loop supply chain network design problem. Transp Sci 43(3):301–320

    Google Scholar 

  153. Li SG, Rong YL (2009) The research of online price quotation for the automobile parts exchange programme. Int J Comput Integr Manuf 22(3):245–256

    Google Scholar 

  154. Lee JE, Gen M, Rhee KG (2008) A multi-stage reverse logistics network problem by using hybrid priority-based genetic algorithm. IEEJ Trans Electron Inf Syst 128(3):450–455

    Google Scholar 

  155. Li Y, Chen J, Cai X (2007) Heuristic genetic algorithm for capacitated production planning problems with batch processing and remanufacturing. Int J Prod Econ 105(2):301–317

    Google Scholar 

  156. McGovern SM, Gupta SM (2007) Combinatorial optimization analysis of the unary NP-complete disassembly line balancing problem. Int J Prod Res 45(18–19):4485–4511

    MATH  Google Scholar 

  157. McGovern SM, Gupta SM (2007) A balancing method and genetic algorithm for disassembly line balancing. Eur J Oper Res 179(3):692–708

    MATH  Google Scholar 

  158. McGovern SM, Gupta SM (2006) Ant colony optimization for disassembly sequencing with multiple objectives. Int J Adv Manuf Technol 30(5–6):481–496

    Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Babaria Institute of Technology, Varnama, Vadodara, Gujarat, 391240, India

    Zulfiquar N. Ansari & Sachin D. Daxini

  2. Gujarat Technological University, Ahmedabad, Gujarat, India

    Zulfiquar N. Ansari & Sachin D. Daxini

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  1. Zulfiquar N. Ansari
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Table 5 Summary of articles by application area, purpose, objective type, objective function, meta-heuristics used and techniques used for result comparison (in chronological order according to year of publication)
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Ansari, Z.N., Daxini, S.D. A State-of-the-Art Review on Meta-heuristics Application in Remanufacturing. Arch Computat Methods Eng 29, 427–470 (2022). https://doi.org/10.1007/s11831-021-09580-z

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