冷凍車及油罐車在配送產品時，通常是使用兩個載具以上的車輛來儲存不同的特定產品，與一般常見之配送模式不同，因此衍生出多載具車輛途程問題(Multi-Compartment Vehicle Routing Problem; MC-VRP)。由於實務上，某些配送業者使用可調整車輛上載具空間之車輛，以期能夠節省更多的途程成本，本研究進一步延伸MC-VRP問題，定義可調式多載具的車輛途程問題(Adjustable Compartments Vehicle Routing Problem; AC-VRP)，並且建構AC-VRP的數學模型。因為最佳化軟體無法求解大型AC-VRP問題，不能滿足實際營運上之需求所以本研究亦發展了一套符合AC-VRP特性的演算法，以期能在一定時限內，求得不錯之解。此演算法以禁忌搜尋法為基礎，結合了若干特色，包括縮小搜尋的鄰近結構空間及加入指導策略來尋找最佳解，經過實驗的測試之後，結果顯示本研究所提出之演算法不但能夠找到不錯的AC-VRP解，在求解MC-VRP問題上，也略優於過去研究所提出之演算法。

Cold logistics and oil distributers often use special vehicles with multiple compartments to ship different products to customers. This special transportation mode results in a new type of vehicle routing problem, namely the multi-compartment vehicle routing problem (MC-VRP). In practice, some of these carriers adopted vehicles with adjustable compartments to further reduce transportation costs. Therefore, in this research, we extended the MC-VRP and defined the adjustable compartments vehicle routing problem (AC-VRP). We also developed a mathmatical programming model for this new problem. Since large-scale AC-VRP instances in practice are difficult to solve by commercial optimization software, we developed a heuristics algorithm based on tabu search for the AC-VRP so that quality solutions may be obtained within a reasonable amount of time. The proposed algorithm integrates certain problem specific features such as solution space reduction and guided search strategies. Results of computational experiments indicate that the proposed heuristic is capable of solving the AC-VRP efficiently. Moreover, it's performance on solving MC-VRP is slightly better than previous approaches in the literature.

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