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A hybrid genetic algorithm for a dynamic logistics network with multi-commodities and components

Published online by Cambridge University Press:  22 September 2011

Peng-Sheng You ,
Yi-Chih Hsieh  and
Hisn-Hung Chen
Peng-Sheng You
Affiliation:
Graduate Institute of Marketing & Logistics/Transportation, National Chia-Yi University, Taiwan. [email protected]; [email protected]
Yi-Chih Hsieh
Affiliation:
Department of Industrial Management, National Formosa University, Taiwan. [email protected]
Hisn-Hung Chen
Affiliation:
Graduate Institute of Marketing & Logistics/Transportation, National Chia-Yi University, Taiwan. [email protected]; [email protected]
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Abstract

Various topics related to reverse logistics have been discussed over the years. Most of them have assumed that facilities are kept open once they are established, and no returned products or recovery parts are stocked in intermediate recycling stations. However, firms may have the right to repeatedly open or close their facilities according to their economic benefits if they can acquire their facilities by lease. It also turns out that intermediate recycling stations like collection centers and disassembly centers usually stock returned products or parts in their facilities. By simultaneously relaxing these two assumptions, this study explores a logistics system with multiple items, each of which consists of some components among a variety of spare parts. The purpose is to maximize the total logistics costs by establishing a production schedule and reverse logistics framework over finite time periods for a logistics system. The mathematical model established in this study is a constrained linear integer programming problem. A genetic based algorithm is developed with the help of linear programming to find solutions to this problem. Limited computational experiments show that the proposed approach can produce better feasible solutions than the well-known CPLEX 10.0 software.

Keywords

Reverse logisticsgenetic algorithmconstrained integer programmingproduction scheduleinventory
Type
Research Article
Information
RAIRO - Operations Research , Volume 45 , Issue 2 , April 2011 , pp. 153 - 178
Copyright
© EDP Sciences, ROADEF, SMAI, 2011

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References

Brahimi, N., Dauzere-Peres, S., Najid, N.M. and Nordli, A., Single item lot sizing problems. Eur. J. Oper. Res. 168 (2006) 1–16. CrossRef
Canel, C., Khumawala, B.M., Law, J. and Loh, A., An algorithm for the capacitated, multi-commodity multi-period location problem. Comput. Oper. Res. 28 (2001) 411–427. CrossRef
Chau, K.W., A two-stage dynamic model on allocation of construction facilities with genetic algorithm. Automat. Constr. 13 (2004) 481–490. CrossRef
Dias, J., Captivo, M.E. and Climaco, J., Efficient primal-dual heuristic for a dynamic location problem. Comput. Oper. Res. 34 (2007) 1800–1823. CrossRef
S.D. Ekşioğlu, Optimizing integrated production, inventory and distibution problems in supply chains. Ph. D. thesis, University of Florida, USA (2002) (fcla.edu/fcla/etd/UFE0000529).
Fleischmann, M., Beullens, P. and Bloemhof-Ruwaard, J.M., The impact of product recovery on logistics network design. Prod. Oper. Manage. 10 (2001) 156–173. CrossRef
Hinojosa, Y., Kalcsics, J., Nickel, S., Puerto, J. and Velten, S., Dynamic supply chain design with inventory. Comput. Oper. Res. 35 (2008) 373–391. CrossRef
Jans, R. and Degraeve, Z., Meta-heuristics for dynamic lot sizing: A review and comparison of solution approaches. Eur. J. Oper. Res. 177 (2007) 1855–1875. CrossRef
Jayaraman, V., Patterson, R.A. and Rolland, E., The design of reverse distribution network: Model and solution procedures. Eur. J. Oper. Res. 150 (2003) 128–149. CrossRef
Krarup, J. and Pruzan, P.M., The simple plant location problem: survey and synthesis. Eur. J. Oper. Res. 12 (1983) 36–81. CrossRef
Ko, H.J. and Evans, G.W., A genetic algorithm-based heuristic for dynamic integrated forward/reverse logistics network for 3PLs. Comput. Oper. Res. 34 (2007) 346–366. CrossRef
Ko, H.J., Ko, C.S. and Kim, T., A hybrid optimization/simulation approach for a distribution network design of 3PLS. Comput. Ind. Eng. 50 (2006) 440–449. CrossRef
Krarup, J. and Pruzan, P.M., The simple plant location problem: Survey and synthesis. Eur. J. Oper. Res. 12 (1983) 36–57. CrossRef
Kusumastuti, R.D., Piplani, R. and Lim, G.H., Redesigning closed-loop service network at a computer manufacturer: A case study. Int. J. Prod. Econ. 111 (2008) 244–260. CrossRef
Langella, I.M., Heuristics for demand-driven disassembly planning. Comput. Oper. Res. 34 (2007) 552–577. CrossRef
Lee, H. and Dong, M., A heuristic approach to logistics network design for end-of-lease computer products recovery. Transport. Res. E. 44 (2008) 455–474. CrossRef
Lieckens, K. and Vandaele, N., Reverse logistics network design with stochastic lead times. Comput. Oper. Res. 34 (2007) 395–416. CrossRef
Lu, Z. and Bostel, N., A facility location model for logistics systems including reverse flows: The case of remanufacturing activities. Comput. Oper. Res. 34 (2007) 299–323. CrossRef
Melachrinoudis, E., Min, H. and Wu, X., A multiobjective model for the dynamic location of landfills. Location Science 3 (1995) 143–166. CrossRef
Melo, M.T., Nickel, S. and Gama, F.S., Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning. Comput. Oper. Res. 33 (2005) 181–208. CrossRef
Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs. 3rd edition, Springer-Verlag, London, UK (1996).
Min, H. and The, H.J. Ko dynamic design of a reverse logistics network from the perspective of third-party logistics service providers. Int. J. Prod. Econ. 113 (2008) 176–192. CrossRef
Min, H., Ko, C.S. and The, H.J. Ko spatial and temporal consolidation of returned products in a closed-loop supply chain network. Comput. Ind. Eng. 51 (2006) 309–320. CrossRef
G.C. Onwubolu and B.V. Babu, New optimization techniques in engineering. Springer-Verlag, Berlin, Heidelberg (2004) Chap. 2.
Pishvaee, M.S., Farahani, R.Z. and Dullaert, W., A memetical gorithm for bi-objective integrated forward/reverse logistics network design. Comput. Oper. Res. 37 (2005) 181–208.
Prahinski, C. and Kocabasoglu, C., Empirical research opportunities in reverse supply chains. Omega-Int. J. Manage. Sci. 34 (2006) 519–532. CrossRef
Salema, M.I.G., Barbosa-Povoa, A.P. and Novais, A.Q., An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. Eur. J. Oper. Res. 179 (2007) 1063–1077. CrossRef
E.A. Silver, D.F. Pyke and R. Peterson, Inventory management and production planning and scheduling. 3rd edition, John Wiley & Sons, USA (1998).
Sodhi, M. and Reimer, B., Models for recycling end-of-life products. OR-Spectrum 23 (2001) 97–115. CrossRef
Spengler, T., Püchert, H., Penkuhn, T. and Rentz, O., Environmental integrated production and recycling management. Eur. J. Oper. Res. 97 (1997) 308–326. CrossRef
Yongsheng, Z. and Shouyang, W., Generic Model of Reverse Logistics Network Design. J. Transport. Syst. Eng. Inf. Tech. 8 (2008) 71–78.
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