An Immunity-Based Algorithm for Distribution Center Location

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Abstract:

The distribution center is the bridge connected supply points and demand points, lies in pivotal status in modern logistics system. Firstly, the mathematical model of a distribution center location is established, based on the study of using neural network to solve distribution center location of the previous scholars, a new method is presented as well as the improved immune algorithm. A new affinity formula is designed for immunoselection criteria. Simultaneously, based on the mathematical model and cases, the algorithm is drilled concrete. A case shows that the improved immune algorithm can better solve the problem of the distribution center location.

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Advanced Materials Research (Volumes 971-973)

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1537-1542

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June 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Brimberg J, Mehrez A. Multi-facility location using a maxi-min criterion and rectangular distances[J] . Location Sci-ence, 2004, (2) : 11-19.

Google Scholar

[2] Eiichi Taniguchi, Michihiko Noritake, Tadashi Yamada, et al. Optimal Size and Location Planning lf Public Logistics Terminals . Transportation Research Part E Logistics and Transportation Review, 1999, 35 (3) : 207-222.

DOI: 10.1016/s1366-5545(99)00009-5

Google Scholar

[3] Herrera F, Martinez L. A 2-tuple fuzzy linguistic representation model for computing with words . IEEE Transactions on Fuzzy Systems, 2000, 8 (6) : 746-752.

DOI: 10.1109/91.890332

Google Scholar

[4] Goyal SK, Giri BC. Recent trends in modeling of deteriorating inventory . European Journal of Operational Research, 2001, 134 (1) : 1-16.

DOI: 10.1016/s0377-2217(00)00248-4

Google Scholar

[5] Chun JS, Kim MK, Jung HK, et al. Shape optimization of electromagnetic devices using immune algorithm . IEEE Transactions on Magnetics, 1997, 33 (2) : 1876-1879.

DOI: 10.1109/20.582650

Google Scholar

[6] Chun J S, Jung H K, Hahn S Y. A study on comparison of optimization performance between immune algorithm and other heuristic algorithms . IEEE Transactions on Magnetics, 1998, 34 (5) : 2972-2975.

DOI: 10.1109/20.717694

Google Scholar

[7] Chun-Tao Chang. An EOQ model with deteriorating items under inflation when supplier credits linked to order quantity . International Journal of Production Economics, 2004, 88 (3) : 307-316.

DOI: 10.1016/s0925-5273(03)00192-0

Google Scholar

[8] Daskin, M. S. Network and Discrete Location: Models Algorithms and Applications . New York: Wiley Interscience, 1995.

Google Scholar

[9] Raymer M L, Punch W F, Goodman E D, et al. Dimensionality reduction using genetic algorithms[J] . IEEE Transactions on Evolutionary Compu-tation, 2010, 4 (2) : 164-171.

DOI: 10.1109/4235.850656

Google Scholar

[10] Freschi F, Repetto M. VIS: An artificial immune network for multi-objective optimization . Engineering Optimization, 2006, 38 (8) : 975-996.

DOI: 10.1080/03052150600880706

Google Scholar

[11] Maoguo Gong, Licheng Jiao, Haifeng Du, Liefeng Bo. Multiobjective ImmuneAlgorithm with Nondominated Neighbor-based Selection . Evolutionary Computation, 2008, 16 (2) : 225–255.

DOI: 10.1162/evco.2008.16.2.225

Google Scholar

[12] H-K Chen, C-F Hsueh, M-S Chang. Production scheduling and vehicle routing with time windows for perishable food products . Comput Oper Res, 2009, 36 (7) : 2311-2319.

DOI: 10.1016/j.cor.2008.09.010

Google Scholar