冷鏈作為一具有高度發展潛力之新興產業，吸引眾多投資者之目光，而配送中心之選址對冷鏈整體運作效率至關重要，已成為一重要之議題。冷鏈與一般供應鏈最大不同之處，在於其貨物具有易腐性，將隨時間流逝而逐漸腐壞，故本研究參考過往相關研究，於建立冷鏈選址模型時，除最常見之成本外，還將加入易腐性的考量，追求商品完好新鮮度的最大化；同時，因冷鏈貨物對於保存環境有一定的要求，若早於或晚於需求端可配合收貨之時間窗範圍，皆容易造成顧客滿意度的流失，故亦將追求商品配送提早或延誤之時間的最小化。而由於冷鏈較一般供應鏈具有更高的風險，故本研究亦於求解冷鏈配送中心選址地點時，將過往文獻未曾考量之風險因素納入模型，使其更加完善。
多目標規劃法作為最常見的多準則決策方法之一，可針對具有實際資源配置限制，以及須同時考量多項目標之決策問題進行求解，因而被廣泛地運用於各領域的選址問題之中。而因明確的目標期望達成水準難以定義，故後續衍生模糊目標規劃方法，可利用歸屬度函數衡量決策者對各目標達成水準之滿意程度，進而統一各目標之單位尺度，求解能令決策者最滿意之結果。然而，此二種方法於建立求解模型的過程中，皆未考量到目標達成之風險問題，故本研究於模糊目標規劃模型中，加入各目標風險價值之限制，並修改過往研究計算可信度測度之公式，令決策者可於計算各目標風險價值之過程中，反映其過往經驗與主觀認知態度。
根據最後的結果分析，證實本研究所修改之可信度測度之公式，確實能較傳統的方法更進一步地反映決策者態度，且以此公式計算目標風險價值，能更加靈活地根據決策者之評估，訂定不同目標歸屬度之限制，進而得到更符合決策者實際考量之求解結果。

Cold chain is an emerging and very promising industry, and the location of distribution centers is crucial to the overall efficiency of cold chain operations. The cool cargo is perishable and has high demand for preservation conditions, and its quality will gradually deteriorate over time. Thus, cold chain will pose higher potential risk than the general supply chain. However, most studies do not consider the possible risk, or ignore the experienced risk information provided by decision makers in the actual situation. Therefore, this study will propose a fuzzy goal programming method that considers the attitude of the decision maker and the value at risk of different goals.
In this study, the model is developed in two stages. In the first stage, the initial fuzzy goal programming model is established. The cost, the intact rate of freshness, and the time that distribution time earlier or later than the acceptable arrival time window of demand point will be taken into account to establish a multi-objective programming model. In the second stage, value-at-risk conditional constraints of objectives will be added into the model. First, the fuzzy credibility measure formula is improved, so that it no longer assumes the decision maker is neutral, and also can reflect the decision maker’s degree of optimism or conservativeness to different goals. Then, we use the improved formula to calculate the value-at-risk for different goals, and use their corresponding membership degree as the minimum achievable lower bound for each goals.
Through the final analyses of the results, we found that the proposed method can reflect the decision maker's attitude toward each goal, and sets minimum achievable membership degree of different objectives, so that the determined solution is more consistent with the decision maker's actual considerations while taking into account the risks.

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