Abstract
This chapter focuses on the second of a three-stage, integrated methodology for modeling and optimising distribution networks (DN) based on hybrid genetic algorithms (HGA). The methodology permits any combination of transportation and warehousing costs for deterministic/stochastic demand. This chapter analyses and compares the fluctuation of overall costs when the number of facilities varies and indicates how to minimize them. The chapter concentrates on capacitated location allocation of distribution centers, a large scale, highly constrained, NP-hard, combinatorial problem. The HGA used has a classical structure, but incorporates a special encoding of solutions as chromosomes and integrates linear programming/mixed integer programming modules in the genetic operators (GO). A complex and extensive case study is described, demonstrating the robustness of the HGA and the optimization approach.
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Marian, R.M., Luong, L., Dao, S.D. (2012). Hybrid Genetic Algorithm Optimisation of Distribution Networks—A Comparative Study. In: Ao, S., Castillo, O., Huang, X. (eds) Intelligent Control and Innovative Computing. Lecture Notes in Electrical Engineering, vol 110. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1695-1_9
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DOI: https://doi.org/10.1007/978-1-4614-1695-1_9
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