多目標群粒子搜尋演算法應用於正逆向物流整合型永續供應鏈網絡設計 -以太陽能產業為例

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题名:	多目標群粒子搜尋演算法應用於正逆向物流整合型永續供應鏈網絡設計 -以太陽能產業為例
其它题名:	Development of a Multi-Objective Particle Swarm Optimization Model for Closed-Loop Sustainable Supply Chain Design and Application in Solar Cell Industry
作者:	王心恕 Wang,Allen
贡献者:	鄭辰仰 Cheng,Chen-Yang 工業工程與經營資訊學系
关键词:	多目標粒子群最佳化;逆物流;永續供應鏈設計;太陽能產業 Multi-Objective Particle Swarm Optimization;Supply Chain Design;Closed-Loop;Solar Energy Industry
日期:	2013
上传时间:	2014-02-19T06:58:59Z (UTC)
摘要:	本研究提出一套整合正、逆向物流之供應鏈設計模式，考量環境永續經營議題與正、逆物流系統之建置，且包括運輸流量、產能投資及技術水準設置等問題；運用Crowding Distance與Standard Distance多目標求解方法(Multi-Objective Programming; MOP)，搭配粒子群最佳化(Particle Swarm Optimization; PSO)，藉以求取整體供應鏈設計之總成本與二氧化碳排放約當量最小化。根據分析結果指出，逆物流建置會依據不同的原物料取得成本與市場需求而呈現不同經濟效益；並透過柏拉圖最適解集合(Pareto-optimal Set)，分析成本與二氧化碳排放之間的交互影響，並依據不同的碳排放量上限提供企業兼具成本與環境效益的供應鏈設計結果。 Solar energy industry is an exceptional industry which desperately relies on government support and subsidy. The demand is decreasing since the government support reduction, moreover, the dramatically increase China solar manufacturers have great impact on solar product price in recent years. Because the insufficient supply of silicon materials carries the issue of solar cell recycle, the solar manufacturer must design a sustainable closed-loop supply chain to recycle and reuse the retired solar cells to achieve 3E (Effective, Efficient, Environmental; 3E) objectives. This paper studies an integrated forward and reverse (closed-loop) supply chain network design problem with sustainable concerns in the solar energy industry. We are interested in the logistics flows, capacity expansion and technology investments of existing and potential facilities in the multi-stage closed loop supply chain. Therefore, a deterministic multi-objective mixed integer programming model capturing the tradeoffs between the total cost and the carbon dioxide (CO2) emission is developed to tackle the multi-stage closed-loop supply chain design problem from both economic and environmental perspectives. Due to the multi-objective nature and computational complexity, a multi-objective particle swarm optimization (MOPSO) with novel flow assignment algorithms is designed to search non-dominated /Pareto supply chain design solutions. Finally, a case study of crystalline solar energy industry is illustrated to verify the proposed multi-objective supply chain network design model and demonstrate the efficiency of the developed MOPSO algorithm in terms of computational time and solution quality.
显示于类别:	[工業工程與經營資訊學系所] 碩博士論文

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