傳統供應鏈網路主要是在不確定的商業環境下考量經濟模型以最佳化供應鏈設計。社會迫切需要設計更可永續的供應鏈網路，在合理的成本之下減少供應鏈活動產生的碳排放。本研究解決了永續供應鏈網絡設計問題，其中考慮了碳交易政策和供應商的信用交易，以最佳化實體市場和碳市場中的總供應鏈利潤。這個問題不僅需要決定設施的數量、位置和容量，網絡中實體之間的產品流，產品的銷售價格，還要決定在不同信用交易方案下供應商的最佳經濟訂單數量。基於整合穩健最佳化和模糊規劃的穩健模糊規劃模型被用於解決需求和相關成本之不確定性。本研究以台灣一家鋼鐵公司進行案例分析，以證明該模型的有效性和效率。結果表明，與基於情景之穩健隨機規劃相比，該模型將總供應鏈利潤（包括來自實體市場和碳市場的利潤）提高了約1％，並將計算時間減少了3.6倍。我們的研究也顯示，供應鏈網絡的最佳配置對不同情景的碳交易具敏感性，而供應商的選擇則受信用交易政策的影響。

Classical supply chain networks mainly concern on the economic optimization model by determining the optimal supply chain configuration under the chaotic business environment. There has been an immense pressure from the society to design more sustainable supply chain networks for reducing the carbon emissions generated from supply chain activities with reasonable cost. This study addresses the sustainable supply chain network design problem considering carbon trading policy and trade-credit from suppliers to optimize the total supply chain profit in both physical market and carbon market. The problem entails decisions regarding not only the number, location, and capacity of facilities, the product flow among entities in the network, and the product-selling price but also the optimal economic order quantity to suppliers under different trade-credit schemes. A robust fuzzy programming model based on the integration of robust optimization and fuzzy programming is applied to tackle the uncertainties in demand and relevant costs. A case study in Taiwan steel firm was conducted to demonstrate the efficacy and efficiency of the proposed model. Results show that the proposed model improves the total supply chain profit, including the profits from physical and carbon markets, by approximately 1 percent and reduces the computational time by 3.6 times compared to the scenario-based robust stochastic programing. Our findings also show that the optimal configuration of supply chain network is sensitive to different scenario of carbon trade and selection of supplier is affected by the trade-credit policy.

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