近年來網路越來越發達的情況下，越來越多顧客藉由電子商務進行線上購物。企業不僅要提升服務水準來吸引更多的顧客消費外，為了增加企業的總營業利潤，減少營運成本以及產品運送到消費者上所需要的運送成本是相當重要的。為了要配合銷售的成長還有讓運送成本減少，許多企業從單一倉儲模式轉變為多倉儲模式。舉例來說，像是美國以及中國這麼大的國家應用多倉儲模式會優於單一倉儲模式。因此多倉儲問題在學術研究以及現實世界的應用是很重要的研究主題。在工業4.0的模組下，智慧物流和智慧製造的整合變成相當重要的產業升級，像是先進製造科技。
在本研究中，我們結合增強式掃描法(Enhanced Sweep Method)以及基因演算法(Genetic Algorithm)和禁忌搜索法(Tabu Search Algorithm)來完成兩個混合模型。本研究的目標希望滿足所有限制條件下，達到總成本(運輸及營運)的最小化，藉由此混合模型對多倉儲車輛運輸問題(MDVRP)快速去進行求解。為了驗證及比較兩個混合模型的效能，本研究以15組多倉儲車輛運輸問題的標竿問題來進行實驗。
實驗結果顯示，本研究所提出的兩個混合模型皆能快速找到近似最佳解，且兩模型的表現在統計上無顯著相異，有著相似的結果。然而混合模型中結合增強式掃描法和禁忌搜索法具有較快的收斂以及計算時間，在處理規模更大的問題時會更加適合；因此，本研究提出的混合模型在處理組合最佳化問題上能有不錯的效能。

Due to rapid development of the Internet, more and more people go online shopping nowadays by e-commerce. Companies not only need to improve their customer service levels but also hope to increase enterprise’s overall profit by reducing operating costs and transport costs from warehouses to customers. In order to match rapid growth of sales and reduce delivery costs, most enterprises have shifted from single warehouse model to multi-warehouse model. For example, applying multi-warehouse model is better than single warehouse model for large countries such as United States of America and mainland China. Therefore, Multi-Depot Vehicles Routing Problems (MDVRPs) have become important topics for both academic research and real world practice. Following the model of Industry 4.0, the integration of the intelligent logistics and smart manufacturing have become important production advancement such as Advanced Manufacturing Technology (AMT).
In this thesis, we proposed two hybrid models by combining with the Enhanced Sweep Method, Genetic Algorithm and Tabu Search Algorithm to solve the MDVRPs. The goal of this study is to minimize total cost (transport costs and operation costs) under all constraints. In order to verify and compare the effectiveness of two hybrid models, our research used 15 MDVRP benchmark problems to conduct our experiments.
From experimental results, the two hybrid models in this thesis can quickly find near-optimal solutions. Moreover, these two models had similar results and no significant difference from statistic point of view. Because the hybrid model combined with the Enhanced Sweep Method and the Tabu Search Algorithm is faster convergence and shorter calculate time to solving the MDVRPs, it is more applicable to solve larger scale problems. Hence, the hybrid models in this thesis have great performance in solving combination optimization problems.

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