本研究的主要目的在於探討電子商務出現後，即時訂貨資訊對於物流配送作業的影響，並釐定出配送作業所應具備與過去較為不同的特性。藉由這些特性的釐清，構建出符合此一物流配送特性的問題模式，並結合即時的訂貨資訊與車輛派遣，建立一套能夠處理此一模式的作業規劃方法。最後經由此一方法的應用，幫助物流業者決定出最適的車隊規模，以降低其營運成本。本研究中加入動態的觀點，設計出適用於本研究的演算流程，配合禁忌搜尋法(Tabu Search)與基因演算法(Genetic Algorithm)用以構建路線與作路線的改善，並藉由持續更新的各項資訊，如需求資訊、車輛屬性資料等，不斷的改善車輛繞行路徑，最後以C語言自行撰寫程式用以求解本問題。案例的測試部分，以三種不同的機率分配產生亂數，作為各時段新加入的訂貨數量，加入於各點原有的需求量上，用以比較本研究所提出的演算法於不同情形下的表現情形。最後據模擬出的最大車輛次數分佈情形，幫助決策者決定出其最適的車隊規模。 The purpose of this study is to develop a dynamic delivery operation plan, which can deal with the real time demand and traffic information in the design of the delivery operation. Due to the dynamic nature of this planning process, the introduction of the robust optimization into the process should be able to reach the most suitable optimization of the process. Based on the characteristics of the electronic commerce, a dynamic route assignment structure will be developed to reach the robust optimization of the delivery operation, which should provide the competition edge for the firm under consideration. Two types of solution procedures were applied in this study, i.e., Genetic Algorithms (GA) and Tabu Search. A series of case studies with different characteristics such as demand density, demand size were used to test the solution capability of the proposed algorithms. Several versions of revised algorithms were developed in these studies. Based on the result of the case studies, a revised GA was identified as the most suitable solution procedure for the problem addressed in this study. The proposed procedure can be used to develop a suitable vehicle routing operations with real-time demand information. In addition, it also provides suitable vehicle requirement information for the decision maker to determine the optimal fleet size.