Abstract
Logistics network is increasingly crucial because of shortened product life cycles, increasing competition, and uncertainty introduced by globalization. The logistics network distribution involves a multistage supply chain that consists of the flexible forward directions (i.e., factories, distribution centers, retailers, and various customers) and the flexible backward directions (i.e., re-manufacturing and reuse). Customer demands fluctuate and are unpredictable, thereby causing an imprecise customer quantity demand in each period in the production distribution model, and increasing inventory and related costs. Most studies have addressed the traditional multistage forward directions problem with certain demands or a single period. To fill the gap, this chapter proposes the hybrid genetic algorithm approaches for solving flexible, multiple periods, multiple stages, and forward/reverse logistics network. In particular, triangular fuzzy demands are considered to minimize the total cost, including transportation costs, inventory costs, shortage costs, and ordering costs, in the multistage and multi-time-period supply chain. The experimental results demonstrated practical viability for the proposed approaches.
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Acknowledgments
This research is supported by the Ministry of Science and Technology, Taiwan (NSC102-2221-E-007-057-MY3; NSC103-2622-E-007-002; MOST103-2218-E-007-023), the Advanced Manufacturing and Service Management Research Center of National Tsing Hua University (102N2075E1), and Hsinchu Science Park (102A26).
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Jamrus, T., Chien, CF., Gen, M., Sethanan, K. (2015). Flexible Multistage Forward/Reverse Logistics Network Under Uncertain Demands with Hybrid Genetic Algorithm. In: Kachitvichyanukul, V., Sethanan, K., Golinska- Dawson, P. (eds) Toward Sustainable Operations of Supply Chain and Logistics Systems. EcoProduction. Springer, Cham. https://doi.org/10.1007/978-3-319-19006-8_26
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DOI: https://doi.org/10.1007/978-3-319-19006-8_26
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