在接駁式轉運的環境下暫時儲存(TSP-TS)的卡車派車作業問題，接駁式轉運終站提供可以暫時儲放貨物在一個臨時儲存區域，使不可出境的卡車以及入境貨車可於此地裝卸。一個較好的派遣行程可加速接駁式轉運的過程，以及保證貨品可以順利到達目的地。因此，一個成功的派遣行程能夠有效地在運送區間內最小化運送成本。我們提出了一個新的演算法，稱為Simple Iterative Mutation Algorithm (SIM-A),這個演算法用於解決TSP-TS的問題。創新的概念，它結合了遺傳算法（GA）和交叉熵算法（CE）中最佳樣本的概念。這種組合結合了兩種算法的優點。遺傳算法提供了一個大範圍的搜索樣本，而交叉熵演算法可確保該演算法保持與精英樣品機制的最佳解決方案。計算結果顯示，Simple Iterative Mutation Algorithm在一個最小計算時間內可以獲得TSP-TS問題的良好解決方案。

The Truck Scheduling Problem with Temporary Storage (TSP-TS) takes place under a cross docking environment that allows the cross docking terminal to temporarily hold freight at a temporary storage area when the associated outbound trucks of the unloading inbound trucks are not available. A good truck schedule can accelerate the cross docking process and guarantee the products are transferred well. Thus, finding a good truck schedule is important in minimizing the operation cost inside the terminal. We propose a new algorithm, called the Simple Iterative Mutation Algorithm (SIM-A), for solving the TSP-TS. It combines the mutation concept of the Genetic Algorithm (GA) and the elite sample concept of the Cross Entropy Algorithm (CE). This combination implements the advantages from both algorithms. The GA provides a wide range of search samples, while the CE ensures the algorithm keeps the best solutions with the elite sample mechanism. Computational results show that the SIM-A obtains good solutions to the TSP-TS within a minimal computational time.

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