近三十年以來，電子商務迅速蓬勃發展，在一個講求效率與方便的世代，城市物流成了電子商務發展的一大核心，進而衍生最後一哩路問題，凸顯了此問題在效率上的重要性。隨著消費者對網路購物的期待與日俱增，除了要求貨物品質，更期待能快速且準確地取得貨物，使得最後一哩路問題面臨新的難題。當貨物運送到顧客家中卻無人接收時，業者必須將貨物載回倉庫存放，並承擔二次配送成本，以及相關人事費用。為了應對此問題的發生，除了選擇超商取貨付款，近年來市面上亦推出一款無人智慧取件宅配櫃，即導入智慧櫃系統以解決此種問題。智慧櫃系統是二十四小時無人服務的設施，消費者可選取任意地點的智慧櫃，在有效期間內的任意時間寄取貨物。因應此系統的產生所改變的配送模式，本研究著重在貨物從零售商倉庫遞送至消費者手中的過程，並藉此延伸宅配結合智慧櫃之車輛途程問題(Vehicle Routing Problem with Parcel Lockers; VRPPL)，提出宅配結合智慧櫃之同時收送貨問題(Simultaneous Pickup and Delivery Problem with Parcel Lockers; SPDPPL)，以更符合實際運送模式。本研究建構一SPDPPL的數學模型，並使用模擬退火法(Simulated Annealing; SA)來求解此問題，以最小化路徑成本為目標式，探討如何規劃物流運送之最佳路線，而滿足不同型態顧客的需求。由於SPDPPL為新問題並無標準題庫，因此，本研究將以Wang等人所產生的具時間窗之同時收送貨問題(Simultaneous Delivery and Pickup Problem with Time Windows; SDPPTW) 題庫為基礎，進行測試，並產生適用於SPDPPL的新題庫，分別使用Gurobi和SA演算法求解再作比較。結果顯示，本研究所提出之SA在計算時間和成本上皆優於Gurobi。

In the past three decades, E-commerce market has developed rapidly. Urban logistics has become an enabler of E-commerce development which led to the importance of last mile delivery efficiency. As customers expect timely and accurate delivery of online shopping purchase, this brings new challenges for last mile delivery. In a case when the goods are delivered to the customers but are not received, the goods must be carried back to the warehouse, stored; and in effect, accrues an additional cost. To deal with this problem, parcel lockers systems and convenient stores are launched to serve as pick-up and payment stations. The said system allows round-the-clock service, flexibility on location and schedule of pickup and delivery. This research investigates the end-to-end process of delivering the goods from the retailer's warehouse to the customers with a change of the delivery mode in the last mile. This research extends the Vehicle Routing Problem with Parcel Lockers (VRPPL) and proposes the Simultaneous Pickup and Delivery Problem with Parcel Lockers (SPDPPL) to complement real-world scenario. This research formulates a mathematical model and proposes a simulated annealing (SA) algorithm for solving SPDPPL to minimize total traveling cost. The results were compared to instances of simultaneous delivery and pickup problem with time windows (SDPPTW) from Wang and Chen’s benchmark. A new set of SPDPPL instances which was modified from SDPPTW instances was generated for this model. The results from designed SA were better in computational time and optimality compared to Gurobi.

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