A genetic approach to two-phase optimization of dynamic supply chain scheduling |
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| Authors: | Alebachew D. Yimer Kudret Demirli |
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| Affiliation: | 1. Faculty of Science, Kunming University of Science and Technology, Kunming 650093, China;2. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;3. School of Management Science and Engineering, Dalian University of Technology, Dalian 116023, China;4. Department of Statistics, Feng Chia University, Taichung, Taiwan;1. Computer Science, Federal Institute of Maranhao—IFMA, Av. Getulio Vargas, 04-Monte Castelo, 65.025-001 Sao Luis, Maranhao, Brazil;2. Computer Science, Federal University of Ceara (UFC), Campus do Pici-Bloco 910, 60.455-760 Fortaleza, Ceara, Brazil;3. Computer Science, National Laboratory for Scientific Computing (LNCC), Av. Getulio Vargas, 333-Quitandinha, 25.651-075 Petropolis, Rio de Janeiro, Brazil;1. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3G8;2. RiskLab, University of Toronto, Toronto, Ontario, Canada M5S 3G8;3. School of Management, University of Science and Technology of China, 96 Jinzhai, Hefei, Anhui 230026, PR China;4. SUNY, Buffalo, Department of Industrial and System Engineering, USA;5. Department of Management, University of Nebraska, Lincoln, NE 68588-0491, USA;1. State Key Laboratory Breeding Base of Nuclear Resources and Environment, East China Institute of Technology, Nanchang 330013, China;2. School of Sciences, East China Institute of Technology, Fuzhou, Jiangxi 344000, China;3. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;4. Graduate Institute of Business Administration, Cheng Shiu University, Kaohsiung County, Taiwan;5. Department of Statistics, Feng Chia University, Taichung, Taiwan |
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| Abstract: | In today’s competitive environment, agility and leanness have become two crucial strategic concerns for many manufacturing firms in their efforts to broaden market share. Recently, the build-to-order (BTO) manufacturing strategy is becoming a popular operation strategy to achieve both in a mass-scale customization process. BTO system combines the characteristics of make-to-order strategy with a forecast driven make-to-stock strategy. As a means to improve customer responsiveness, customized products are assembled according to specific orders while standard components are pre-manufactured based on short-term forecasts. Planning of the two subsystems using a two-phase sequential approach offers both operational and modeling incentives. In this paper, we formulate a two-phase mixed integer linear programming (MILP) model for material procurement, components fabrication, product assembly and distribution scheduling of a BTO supply chain system. In the proposed approach, the entire problem is first decomposed into two subsystems and evaluated sequentially. The first phase deals with assembling and distribution scheduling of customizable products, while the second phase addresses fabrication and procurement planning of components and raw-materials. The objective of both models is to minimize the aggregate costs associated with each subsystem, while meeting customer service requirements. The search space for the first phase problem involves a complex landscape with too many candidate solutions. A genetic algorithm based solution procedure is proposed to solve the sub-problem efficiently. |
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