Abstract
Supply Chain Management (SCM) describes the discipline of optimizing the delivery of goods, services and information from supplier to customer. Transportation network design is one of the most important fields of SCM. It offers great potential to reduce costs and to improve service quality. In this paper, we consider an extension version of two-stage transportation problem (tsTP) to minimize the total logistic cost including the opening costs of distribution centers (DCs) and shipping cost from plants to DCs and from DCs to customers. To solve the problem, we developed a priority-based Genetic Algorithm (pb-GA), in which new decoding and encoding procedures were used to adapt to the characteristic of tsTP, and proposed a new crossover operator called as Weight Mapping Crossover (WMX). An experimental study was carried out into two-stages. While the effect of WMX on the performance of pb-GA was investigated in the first stage, pb-GA and another GA approach based on different representation method were compared according to solution quality and solution time in the second stage.
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References
Aikens CH (1985) Facility location models for distribution planning. Eur J Oper Res 22:263–279
Amiri A (2005) Designing a distribution network in a supply chain system: formulation and efficient solution procedure. Eur J Oper Res (in press)
Avealla P (1998) Some personal views on the current state and the future of locational analysis. Eur J Oper Res 104:269–287
Das C, Heragu S (1988) A transportation approach to locating plants in relation to potential markets and raw material sources. Decis Sci 19(4):819–829
Davis L (1995) Job-shop scheduling with genetic algorithms. Proceedings of the First International Conference on Genetic Algorithms. Lawrence Erlbaum Associates, pp 36–140
Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-Completeness. Freeman
Gen M, Cheng RW (1997) Genetic algorithms and engineering design. Wiley, New York
Gen M, Cheng RW (2000) Genetic algorithms and engineering optimization. Wiley, New York
Gen M, Li YZ (1998) Solving multi-objective transportation problem by spanning tree-base genetic algorithm. In: Adaptive computing in design and manufactured. Springer, Berlin Heidelberg New York, pp 98–108
Geoffrion AM, Graves GW (1974) Multicommodity distribution system design by benders decomposition. Manage Sci 20:822–844
Goldberg D, Lingle R (1995) Alleles, Loci and the traveling salesman problem. Proceedings of the First International Conference on Genetic Algorithms. Lawrence Erlbaum Associates, pp 154–159
Heragu S (1997) Facilities design. PSW
Hindi KS, Basta T, Pienkosz K (1998) Efficient solution of a multi-commodity, two-stage distribution problem with constraints on assignment of customers to distribution centers. Int Trans Oper Res 5(6):519–527
Hitchcock FL (1941) The distribution of a product from several sources to numerous localities. J Math Phys 20:24–230
Li YZ, Gen M, Ida K (1998) Improved genetic algorithm for solving multi-objective solid transportation problem with fuzzy number. Jpn J Fuzzy Theory Syst 4(3):220–229
Michalewicz Z, Vignaux GA, Hobbs M (1991) A non-standard genetic algorithm for the nonlinear transportation problem. ORSA J Comput 3(4):307–316
Pirkul H, Jayaraman V (1998) A multi-commodity, multi-plant capacitated facility location problem: formulation and efficient heuristic solution. Computer Operations Research 25(10):869–878
ReVelle C, Laporte G (1996) The plant location problem: new models and research prospects. Oper Res 44:864–874
Syarif A, Gen M (2003) Double spanning tree-based genetic algorithm for two stage transportation problem. Int J Knowl-Based Intell Eng Syst 7(4): October
Syswerda G (1995) Uniform crossover in genetic algorithms. Proceedings of the Third International Conference on Genetic Algorithms. Lawrence Erlbaum Associates, pp 2–9
Tilanus B (1997) Introduction to information system in logistics and transportation. In: Tilanus B (ed) Information systems in logistics and transportation. Elsevier, Amsterdam, pp 7–16
Tragantalerngsak S, Holt J, Ronnqvist M (2000) An exact method for two-echelon, single-source, capacitated facility location problem. Eur J Oper Res 123:473–489
Vignaux GA, Michalewicz Z (1991) A genetic algorithm for the linear transportation problem. IEEE Trans Syst Man Cybern 21(2):445–452
Acknowledgements
During the study, Dr. Fulya Altiparmak was a visitor researcher at Graduate School of Information, Production and Systems in Waseda University and her research had been supported by The Matsumae International Foundation in Japan.
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This work is partly supported by Waseda University Grant for Special Research Projects 2004 and the Ministry of Education, Science and Culture, the Japanese Government: Grant-in-Aid for Scientific Research (No.17510138).
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Gen, M., Altiparmak, F. & Lin, L. A genetic algorithm for two-stage transportation problem using priority-based encoding. OR Spectrum 28, 337–354 (2006). https://doi.org/10.1007/s00291-005-0029-9
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DOI: https://doi.org/10.1007/s00291-005-0029-9