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.
