物料堆疊問題在物流供應鏈扮演很重要的角色，因為他可以用於許多實際應用中，例如運輸業或物流業，物料堆疊的主要問題就是把一群貨物裝載進去貨車或是貨櫃中並且運用不同的堆疊順序以求出最大化的利用率。
本研究探討多目標物料堆疊問題，並考量利用率與車輛平衡，也考慮了實際的限制再加入了裝運優先順序，考量車輛平衡是為了避免卡車在運送貨物時造成貨物的損壞或是意外的發生。本研究之實驗使用BR系列例題，因為例題只有提供貨物的尺寸，因此隨機生成了貨物的重量以及優先順序，本研究使用多目標基因演算法及多目標粒子群演算法去求解，並使用效能指標去比較演算法的優劣。
根據計算的結果，因為多目標基因演算法在計算利用率方面有比較好的結果，而多目標粒子群演算法在計算平衡度方面表現比較好，即使使用了效能指標，結論仍然不容易得出，因此使用者可以依據他的意願去選擇其中一種方法去求解。

The container loading problem plays an important role in logistics and supply chains. It can be applied to many kinds of real-world applications such as the transportation, or logistics, industry for delivery. The problem is to pack a set of cargos into container and full the utility of the container space to maximize the efficiency.
The multi-objective is processed to solve the CLP. There are two objectives of this study. First objective is to maximize the utilization of container and the second objective is to minimize the force penalty of truck wheel axle. This study also consider the constraint in the real-world application and add loading priority. It will be more practical to consider FP in order to balance the truck which can avoid damage when the truck is delivering the cargo. This study employs NSGA II and MOPSO to solve current problem. Because the benchmark dataset didn’t have weight of cargo, we randomize the weights and priorities for each cargo of BR instances. As the fitness cannot decide which algorithm is better, this study use the quality indicators to compare both of algorithms.
According to the computational result, it is not easy to determine the best method since NSGA II has better result in container utilization, while MOPSO has better performance in terms of force penalty. Even the quality indicators are applied, the conclusion is still not easy to make. Thus, user can select either method based on the objective function he/she prefers.

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