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The Scientific World Journal
Volume 2014, Article ID 595902, 19 pages
http://dx.doi.org/10.1155/2014/595902
Research Article

Designing a Multistage Supply Chain in Cross-Stage Reverse Logistics Environments: Application of Particle Swarm Optimization Algorithms

1Department of Business Administration, National Taipei College of Business, Taipei 10051, Taiwan
2Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan
3Complex System and Computational Intelligent Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, China
4State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China

Received 11 October 2013; Accepted 23 December 2013; Published 18 February 2014

Academic Editors: C. H. Aladag and C.-C. Jane

Copyright © 2014 Tzu-An Chiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. J. Vidal and M. Goetschalckx, “Strategic production-distribution models: a critical review with emphasis on global supply chain models,” European Journal of Operational Research, vol. 98, no. 1, pp. 1–18, 1997. View at Google Scholar · View at Scopus
  2. B. M. Beamon, “Supply chain design and analysis: models and methods,” International Journal of Production Economics, vol. 55, no. 3, pp. 281–294, 1998. View at Google Scholar · View at Scopus
  3. Ş. S. Erengüç, N. C. Simpson, and A. J. Vakharia, “Integrated production/distribution planning in supply chains: an invited review,” European Journal of Operational Research, vol. 115, no. 2, pp. 219–236, 1999. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Xu, Q. Liu, and R. Wang, “A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor,” Information Sciences, vol. 178, no. 8, pp. 2022–2043, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. R. A. Aliev, B. Fazlollahi, B. G. Guirimov, and R. R. Aliev, “Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management,” Information Sciences, vol. 177, no. 20, pp. 4241–4255, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. S.-W. Chiou, “A non-smooth optimization model for a two-tiered supply chain network,” Information Sciences, vol. 177, no. 24, pp. 5754–5762, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. D. Y. Sha and Z. H. Che, “Virtual integration with a multi-criteria partner selection model for the multi-echelon manufacturing system,” The International Journal of Advanced Manufacturing Technology, vol. 25, no. 7-8, pp. 793–802, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. D. Y. Sha and Z. H. Che, “Supply chain network design: partner selection and production/distribution planning using a systematic model,” Journal of the Operational Research Society, vol. 57, no. 1, pp. 52–62, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. H. Che and Z. Cui, “Unbalanced supply chain design using the analytic network process and a hybrid heuristic-based algorithm with balance modulating mechanism,” The International Journal of Bio-Inspired Computation, vol. 3, no. 1, pp. 56–66, 2011. View at Google Scholar
  10. J. R. Stock, Reverse Logistics, Council of Logistics Management, Oak Brook, Ill, USA, 1992.
  11. B. Trebilcock, “Reverse logistics heroes,” Modern Materials Handling, vol. 56, no. 10, pp. 63–65, 2001. View at Google Scholar · View at Scopus
  12. M. Cohen, “Replace. Rebuild or remanufacture,” Equipment Management, vol. 16, no. 1, pp. 22–26, 1988. View at Google Scholar
  13. C. D. White, E. Masanet, C. M. Rosen, and S. L. Beckman, “Product recovery with some byte: an overview of management challenges and environmental consequences in reverse manufacturing for the computer industry,” Journal of Cleaner Production, vol. 11, no. 4, pp. 445–458, 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Gattorna, Strategic Supply Chain Alignment-Best Practice in Supply Chain Management, Ashgate, 1998.
  15. C. R. Carter and L. M. Ellram, “Reverse logistics: a review of the literature and framework for future investigation,” Journal of Business Logistics, vol. 19, no. 1, pp. 85–102, 1998. View at Google Scholar
  16. S. Dowlatshahi, “Developing a theory of reverse logistics,” Interfaces, vol. 30, no. 3, pp. 143–155, 2000. View at Google Scholar · View at Scopus
  17. M. Fleischmann, J. M. Bloemhof-Ruwaard, R. Dekker, E. van der Laan, J. A. E. E. van Nunen, and L. N. van Wassenhove, “Quantitative models for reverse logistics: a review,” European Journal of Operational Research, vol. 103, no. 1, pp. 1–17, 1997. View at Google Scholar · View at Scopus
  18. D. S. Rogers and R. Tibben-Lembke, “An examination of reverse logistics practices,” Journal of Business Logistics, vol. 22, no. 2, pp. 129–148, 2001. View at Publisher · View at Google Scholar
  19. T. Spengler, H. Püchert, T. Penkuhn, and O. Rentz, “Environmental integrated production and recycling management,” European Journal of Operational Research, vol. 97, no. 2, pp. 308–326, 1997. View at Google Scholar · View at Scopus
  20. V. Jayaraman, V. D. R. Guide Jr., and R. Srivastava, “A closed-loop logistics model for remanufacturing,” Journal of the Operational Research Society, vol. 50, no. 5, pp. 497–508, 1999. View at Google Scholar · View at Scopus
  21. A. I. Barros, R. Dekker, and V. Scholten, “A two-level network for recycling sand: a case study,” European Journal of Operational Research, vol. 110, no. 2, pp. 199–214, 1998. View at Google Scholar · View at Scopus
  22. L. Kroon and G. Vrijens, “Returnable containers: an example of reverse logistics,” International Journal of Physical Distribution & Logistics Management, vol. 25, no. 2, pp. 56–68, 1995. View at Google Scholar
  23. M. Fleischmann, Quantitative Models for Reverse Logistics, Springer, Berlin, Germany, 2001.
  24. M. M. Amini, D. Retzlaff-Roberts, and C. C. Bienstock, “Designing a reverse logistics operation for short cycle time repair services,” International Journal of Production Economics, vol. 96, no. 3, pp. 367–380, 2005. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Fleischmann, P. Beullens, J. M. Bloemhof-Ruwaard, and L. N. van Wassenhove, “The impact of product recovery on logistics network design,” Production and Operations Management, vol. 10, no. 2, pp. 156–173, 2001. View at Google Scholar · View at Scopus
  26. R. C. Savaskan, S. Bhattacharya, and L. N. V. Wassenhove, “Closed loop supply chain models with product remanufacturing,” Management Science, vol. 50, no. 2, pp. 239–252, 2004. View at Google Scholar · View at Scopus
  27. M. Chouinard, S. D'Amours, and D. Aït-Kadi, “Integration of reverse logistics activities within a supply chain information system,” Computers in Industry, vol. 56, no. 1, pp. 105–124, 2005. View at Publisher · View at Google Scholar · View at Scopus
  28. Y. Kainuma and N. Tawara, “A multiple attribute utility theory approach to lean and green supply chain management,” International Journal of Production Economics, vol. 101, no. 1, pp. 99–108, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Nagurney and F. Toyasaki, “Reverse supply chain management and electronic waste recycling: a multitiered network equilibrium framework for e-cycling,” Transportation Research E, vol. 41, no. 1, pp. 1–28, 2005. View at Publisher · View at Google Scholar · View at Scopus
  30. Y. Nikolaidis, “A modelling framework for the acquisition and remanufacturing of used products,” International Journal of Sustainable Engineering, vol. 2, no. 3, pp. 154–170, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. G. Nenes and Y. Nikolaidis, “A multi-period model for managing used product returns, international,” Journal of Production Research, vol. 50, pp. 1360–1376, 2012. View at Google Scholar
  32. M. Salema, A. Barbosa-Póvoa, and A. Novais, “Simultaneous design and planning of supply chains with reverse flows: a generic modelling framework,” European Journal of Operational Research, vol. 203, no. 2, pp. 336–349, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. T. Pinto-Varela, A. P. Barbosa-Póvoa, and A. Q. Novais, “Bi-objective optimization approach to the design and planning of supply chains: economic versus environmental performances,” Computers and Chemical Engineering, vol. 35, no. 8, pp. 1454–1468, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Amin and G. Zhang, “A proposed mathematical model for closed-loop network configuration based on product life cycle,” International Journal of Advanced Manufacturing Technology, vol. 58, no. 5–8, pp. 791–801, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. M. Huang, M. Song, L. H. Lee, and W. K. Ching, “Analysis for strategy of closed-loop supply chain with dual recycling channel,” International Journal of Production Economics, vol. 144, no. 2, pp. 510–520, 2013. View at Google Scholar
  36. P. L. Meena and S. P. Sarmah, “Multiple sourcing under supplier failure risk and quantity discount: a genetic algorithm approach,” Transportation Research E, vol. 50, pp. 84–97, 2013. View at Google Scholar
  37. B. Stojanovic, M. Milivojevic, M. Ivanovic, N. Milivojevic, and D. Divac, “Adaptive system for dam behavior modeling based on linear regression and genetic algorithms,” Advances in Engineering Software, vol. 65, pp. 182–190, 2013. View at Google Scholar
  38. R. Belevičius, D. Jatulis, and D. Šešok, “Optimization of tall guyed masts using genetic algorithms,” Engineering Structures, vol. 56, pp. 239–245, 2013. View at Google Scholar
  39. Z. H. Che and C. J. Chiang, “A modified Pareto genetic algorithm for multi-objective build-to-order supply chain planning with product assembly,” Advances in Engineering Software, vol. 41, no. 7-8, pp. 1011–1022, 2010. View at Publisher · View at Google Scholar · View at Scopus
  40. S. P. Nachiappan and N. Jawahar, “A genetic algorithm for optimal operating parameters of VMI system in a two-echelon supply chain,” European Journal of Operational Research, vol. 182, no. 3, pp. 1433–1452, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. H. S. Wang and Z. H. Che, “An integrated model for supplier selection decisions in configuration changes,” Expert Systems with Applications, vol. 32, no. 4, pp. 1132–1140, 2007. View at Publisher · View at Google Scholar · View at Scopus
  42. Z. H. Che and T. A. Chiang, “Designing a collaborative supply chain plan using the analytic hierarchy process and genetic algorithm with cycle time estimation,” International Journal of Production Research, vol. 50, no. 16, pp. 4426–4443, 2012. View at Google Scholar
  43. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, December 1995. View at Scopus
  44. Z. Liao and J. Rittscher, “A multi-objective supplier selection model under stochastic demand conditions,” International Journal of Production Economics, vol. 105, no. 1, pp. 150–159, 2007. View at Publisher · View at Google Scholar · View at Scopus
  45. L.-P. Zhang, H.-J. Yu, and S.-X. Hu, “Optimal choice of parameters for particle swarm optimization,” Journal of Zhejiang University Science A, vol. 6, no. 6, pp. 528–534, 2005. View at Publisher · View at Google Scholar · View at Scopus
  46. X. H. Shi, Y. C. Liang, C. Lu, H. P. Lee, and Q. X. Wang, “Particle swarm optimization-based algorithms for TSP and generalized TSP,” Information Processing Letters, vol. 103, no. 5, pp. 169–176, 2007. View at Publisher · View at Google Scholar · View at Scopus
  47. Z. H. Che, “PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding,” Computers and Industrial Engineering, vol. 58, no. 4, pp. 625–637, 2010. View at Publisher · View at Google Scholar · View at Scopus
  48. Z. H. Che, “A particle swarm optimization algorithm for solving unbalanced supply chain planning problems,” Applied Soft Computing, vol. 12, no. 4, pp. 1279–1287, 2012. View at Publisher · View at Google Scholar · View at Scopus
  49. C. Priya and P. Lakshmi, “Particle swarm optimisation applied to real time control of spherical tank system,” International Journal of Bio-Inspired Computation, vol. 4, no. 4, pp. 206–216, 2012. View at Google Scholar
  50. L. Ali, S. L. Sabat, and S. K. Udgata, “Particle swarm optimisation with stochastic ranking for constrained numerical and engineering benchmark problems,” International Journal of Bio-Inspired Computation, vol. 4, no. 3, pp. 155–166, 2012. View at Google Scholar
  51. E. García-Gonzalo and J. L. Fernández-Martínez, “A brief historical review of particle swarm optimization (PSO),” Journal of Bioinformatics and Intelligent Control, vol. 1, no. 1, pp. 3–16, 2012. View at Publisher · View at Google Scholar
  52. L. Ali and S. L. Sabat, “Particle swarm optimization based universal solver for global optimization,” Journal of Bioinformatics and Intelligent Control, vol. 1, no. 1, pp. 95–105, 2012. View at Google Scholar
  53. M. Salehi Maleh, S. Soleymani, R. Rasouli Nezhad, and N. Ghadimi, “Using particle swarm optimization algorithm based on multi-objective function in reconfigured system for optimal placement of distributed generation,” Journal of Bioinformatics and Intelligent Control, vol. 2, no. 2, pp. 119–1124, 2013. View at Google Scholar
  54. H. M. Abdelsalam and A. M. Mohamed, “Optimal sequencing of design projects' activities using discrete particle swarm optimisation,” International Journal of Bio-Inspired Computation, vol. 4, no. 2, pp. 100–110, 2012. View at Google Scholar
  55. Y. Dong, J. Tang, B. Xu, and D. Wang, “An application of swarm optimization to nonlinear programming,” Computers and Mathematics with Applications, vol. 49, no. 11-12, pp. 1655–1668, 2005. View at Publisher · View at Google Scholar · View at Scopus
  56. P.-Y. Yin and J.-Y. Wang, “A particle swarm optimization approach to the nonlinear resource allocation problem,” Applied Mathematics and Computation, vol. 183, no. 1, pp. 232–242, 2006. View at Publisher · View at Google Scholar · View at Scopus
  57. A. Salman, I. Ahmad, and S. Al-Madani, “Particle swarm optimization for task assignment problem,” Microprocessors and Microsystems, vol. 26, no. 8, pp. 363–371, 2002. View at Publisher · View at Google Scholar · View at Scopus
  58. W. A. McCall, Measurement, Macmillan, New York, NY, USA, 1939.
  59. Z. H. Che, “A genetic algorithm-based model for solving multi-period supplier selection problem with assembly sequence,” International Journal of Production Research, vol. 48, no. 15, pp. 4355–4377, 2010. View at Publisher · View at Google Scholar · View at Scopus
  60. D. E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley, New York, NY, USA, 1988.
  61. R. C. Eberhart and Y. Shi, “Comparison between genetic algorithms and particle swarm optimization,” in Proceedings of the 7th Annual Conference on Evolutionary Programming, pp. 611–616, Springer, Berlin, Germany, 1998.
  62. M. Clerc, “The swarm and the queen: towards a deterministic and adaptive particle swarm optimization,” in Proceeding of the IEEE Congress on Evolutionary Computation, vol. 3, pp. 1951–1957, 1999.
  63. R. Eberhart and J. Kennedy, “New optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39–43, October 1995. View at Scopus
  64. Á. E. Eiben, R. Hinterding, and Z. Michalewicz, “Parameter control in evolutionary algorithms,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 124–141, 1999. View at Publisher · View at Google Scholar · View at Scopus
  65. H. A. Scheffé, “A method for judging all contrasts in the analysis of variance,” Biometrika, vol. 40, no. 1-2, pp. 87–104, 1953. View at Google Scholar