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Reverse logistics network design using simulated annealing

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Abstract

Reverse logistics is becoming more important in overall industry area because of the environmental and business factors. Planning and implementing a suitable reverse logistics network could bring more profit, customer satisfaction, and a nice social picture for companies. But, most of logistics networks are not equipped to handle the return products in reverse channels. This paper proposes a mixed integer linear programming model to minimize the transportation and fixed opening costs in a multistage reverse logistics network. Since such network design problems belong to the class of NP-hard problems, we apply a simulated annealing (SA) algorithm with special neighborhood search mechanisms to find the near optimal solution. We also compare the associated numerical results through exact solutions in a set of problems to present the high-quality performance of the applied SA algorithm.

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References

  1. 1.

    Aras N, Aksen D, Tanugur AG (2008) Locating collection centers for incentive-dependent returns under a pickup policy with capacitated vehicles. Eur J Operat Res 191:1223–1240

  2. 2.

    Aarts E, Lenstra JK (1997) Local search in combinatorial optimization. Wiley, New York

  3. 3.

    Arts E, Korst J (1989) Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing. Wiley, Chichester

  4. 4.

    Davis PS, Ray TL (1969) A branch-and-bound algorithm for the capacitated facilities location problem. Nav Res Logist 16:331–344

  5. 5.

    Du F, Evans GW (2008) A biobjective reverse logistics network analysis for post-sale service. Comput Oper Res 35:2617–2634

  6. 6.

    Fleischmann M, Beullens P, Bloemhof-ruwaard JM, Wassenhohve V (2001) The impact of product recovery on logistics network design. Prod Oper Manag 10:156–173

  7. 7.

    Gen M, Cheng R (2000) Genetic algorithms and engineering optimization. Wiley, New York

  8. 8.

    Gen M, Altiparmak F, Lin L (2006) A genetic algorithm for two-stage transportation problem using priority-based encoding. OR Spectrum 28:337–354

  9. 9.

    Jayaraman V, Guige VDR Jr, Srivastava R (1999) A closed-loop logistics model for remanufacturing. J Oper Res Soc 50:497–508

  10. 10.

    Jayaraman V, Ross A (2003) A simulated annealing methodology to distribution network design and management. Eur J Oper Res 144:629–645

  11. 11.

    Jayaraman V, Patterson RA, Rolland E (2003) The design of reverse distribution networks: models and solution procedures. Eur J Oper Res 150:128–149

  12. 12.

    Krikke HR, Van Harten A, Schuur PC (1999) Reverse logistic network redesign for copiers. OR Spektrum 21:381–409

  13. 13.

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

  14. 14.

    Laha D, Chakraborty UK (2007) An efficient stochastic hybrid heuristic for flowshop scheduling. Eng Appl Artif Intell 20:851–856

  15. 15.

    Laha D, Chakraborty UK (2008) An efficient hybrid heuristic for makespan minimization in permutation flow shop scheduling. Int J Adv Manuf Technol. doi:10.1007/s00170-008-1845-2

  16. 16.

    Listes O, Dekker R (2005) A stochastic approach to a case study for product recovery network design. Eur J Oper Res 160:268–287

  17. 17.

    Lu Z, Bostel N (2007) A facility location model for logistics systems including reverse flows: the case of remanufacturing activities. Comput Oper Res 34:299–323

  18. 18.

    Meade L, Sarkis J, Presley A (2007) The theory and practice of reverse logistics. Int J Logist Syst Manag 3:56–84

  19. 19.

    Meepetchdee Y, Shah N (2007) Logistical network design with robustness and complexity considerations. Int J Phys Distrib Logist Manag 37:201–222

  20. 20.

    Michalewicz Z, Vignaux GA, Hobbs M (1991) A non-standard genetic algorithm for the transportation problem. ORSA J Comput 3:307–316

  21. 21.

    Min H, Ko CS, Ko HJ (2006) The spatial and temporal consolidation of returned products in a closed-loop supply chain network. Comput Ind Eng 51:309–320

  22. 22.

    Pirlot M (1992) General local search heuristics in combinatorial optimization: a tutorial. Belg J Oper Res, Stat Comput Sci 32:7–68

  23. 23.

    Üster H, Easwaran G, Akçali E, Çetinkaya S (2007) Benders decomposition with alternative multiple cuts for a multi-product closed-loop supply chain network design model. Nav Res Logist 54:890–907

  24. 24.

    Wojanowski R, Verter V, Boyaci T (2007) Retail–collection network design under deposit–refund. Comput Oper Res 34:324–345

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Correspondence to Behrooz Karimi.

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Pishvaee, M.S., Kianfar, K. & Karimi, B. Reverse logistics network design using simulated annealing. Int J Adv Manuf Technol 47, 269–281 (2010). https://doi.org/10.1007/s00170-009-2194-5

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Keywords

  • Reverse logistics
  • Logistics network design
  • Supply chain network
  • Simulated annealing
  • Priority-based encoding