隨著電子商務以及物流業的興起，使得物流中心之相關研究日益受到重視，而越庫作業為解決物流問題的有效管理策略，能為物流中心帶來更有效率的貨物配送、成本節省。由於越庫相關議題廣泛，本研究主要著重於碼頭指派問題(Dock Assignment Problem)，考量卡車數量多於碼頭數量的狀況，強調安排卡車停靠裝卸貨碼頭，同時求取卡車停靠碼頭之順序，且每輛卡車皆有其抵達碼頭的硬時間窗限制，目標為最小化卡車實際抵達碼頭與預定抵達碼頭之時間差距，並同時考量問題的可行性會受到四種因素的影響：(1)時間窗的大小；(2)卡車數量與碼頭數量的比例；(3)案例規模大小；(4)卡車抵達時間分布。為解決碼頭指派問題，本研究提出兩種方法，第一種為方法為混合整數規劃模型，並透過最佳化軟體Gurobi求取小規模問題。另外，由於碼頭指派與卡車排程問題皆屬於NP-hard問題，代表隨著問題規模增加，此類問題較難求解，故本研究提出第二種方法：禁忌搜尋演算法，並與Gurobi之解驗證，透過小規模與大規模案例之測試後，證明禁忌搜尋法可在更有競爭力的時間下求得高品質的可行解，同時為碼頭指派的硬時間窗問題提供有效的數學模型。

With the rise of e-commerce and logistics industry, the distribution center has received more and more attention recently. Cross-docking is an effective management strategy to solve the problem of logistics. It can make the delivery more efficient and reduce the cost. In this research, we focus on the dock assignment problem in distribution center and we consider the number of trucks exceeds the number of docks. We emphasize assigning both inbound and outbound trucks to multiple docks and the sequence of trucks. Every truck has their hard time window constraint of the arrival time at the dock. The objective is to minimize the waiting time between actual and expected arrival time when the truck arrives at the dock. The dock assignment problem will be affected by four factors: the tightness of the time windows, the ratio of the number of the trucks and the number of dock doors, the size of the case, and the simultaneousness of the truck arrivals. To solve the dock assignment problem, we provide two methods in our research. The first method is to formulate a mixed integer programming model and use optimization solver, Gurobi, to solve the small-scale problems. Because dock assignment and truck scheduling problems are NP-hard, its integration is more difficult to solve. Therefore, we provide the second method: Tabu Search. The result shows that Tabu Search is able to find good solution in much more competitive computation time, comparing to Gurobi. We also provide an effective mathematical model of the dock assignment problem with hard time window.

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三、 網站部分
1. Retail e-commerce sales worldwide from 2014 to 2021 https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/