本研究提出具時間窗、部份充電策略與易腐性商品之電動車輛途程問題(Electric Vehicle Routing Problem with Time Windows, Partial Recharges, and Perishable Products; EVRPTW-PR-PP)，是具時間窗之電動車輛途程問題(Electric Vehicle Routing Problem with Time Windows; EVRPTW)之延伸問題。本論文在運輸過程中考量電動車輛溫度控制，並結合時間窗與部份充電策略，以規劃運送生鮮食品的最佳路線與車輛設定之溫度，確保產品運送至顧客的過程中能維持產品的新鮮度。在問題中使用一組同性質的電動車輛服務顧客，電動車採用部分充電策略。顧客對於不同產品皆有需求，需在顧客規定的時間內將產品送達。本研究對所提出之問題建立數學規劃模型，目標為最小化總行駛距離。透過CPLEX以及建立適應性大規模鄰域搜尋(Adaptive Large Neighborhood Search; ALNS)演算法求解小型題庫，比較兩者求解結果與效率。本研究結合具時間窗之電動車輛途程問題與易腐性商品車輛途程問題之題庫，以建立EVRPTW-PR-PP題庫，並將演算法所得結果與電動車輛途程問題之結果比較以驗證演算法效率。測試結果顯示本研究所提出之ALNS演算法可在短時間內求解小型題庫，並在求解大型題庫上能獲得與其他先進演算法相當的結果。

This study proposes the Electric Vehicle Routing Problem with Time Windows, Partial Recharges, and Perishable Products (EVRPTW-PR-PP), which is an extension of the Electric Vehicle Routing Problem with Time Windows (EVRPTW). This thesis considers temperature control of the electric vehicles during transportation, and at the same time combines time windows and partial charging policies to plan the best route and temperature of the vehicle for perishable products to keep the freshness of the products during delivery. In the problem use a group of homogeneous electric vehicles serving customers, the electric vehicle takes the partial recharging policy. The customers have demand for different products, and the products must be delivered within the specified time that is regulated by customers. This research establishes a mathematical model for the problem, the objective is to minimize the total travel distance. Through CPLEX and develop an Adaptive Large Neighborhood Search(ALNS) algorithm to solve the small instances and compare the computational result. This research combines the instance of Electric Vehicle Routing Problem with Time Windows and Vehicle Routing Problem with Perishable Products to build the EVRPTW-PR-PP instances and compare the computational results with Electric Vehicle Routing Problem to prove the efficiency of the algorithm. The result of the research shows that the build ALNS algorithm can solve the small instances in a short period and can obtain similar results for the large instances with other advanced algorithms.

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