傳統運輸工具使用石化燃料驅動，其碳排放為造成溫室效應的主因之一。近年環保意識興起，各國物流業者開始使用生質柴油或電力驅動的替代能源車輛執行市區配送。其中電動車因普及率較高且技術發展成熟而為物流業者所喜愛。然而，電動車具有充電站分布不均、數量稀少、續航力低且充電時間長等缺點。

綠能車輛路線問題由傳統車輛路線問題延伸，差異在於綠能車輛路線問題使用替代能源車輛並增加車輛續航力與補給站限制，較一般路線問題複雜。而本研究發展一套整數規劃模型，加入多車種、車容量與時窗限制並採用部分充電策略，以最小化總配送時間、車輛閒置時間與充電時間轉換運輸費用為目標，期望提升車輛配送效率與降低運輸成本。

本研究於演算法部分使用記錄更新法並參考適應性大型鄰域搜尋法的破壞與再生概念，將當前解打散以擴大求解空間，並採重組方式獲得新解以提升求解品質。本研究於破壞部分採隨機移除(Random Remove)、2-Opt*與2-Opt等三種方法打散當前解，並於重組部分採貪婪插入法(Greedy Insertion)方式求得新解。

本研究以電動車路線問題題庫針對小例題測試得出三種最佳誤差值後針對大例題求解，並於大例題中發現於三種誤差值下所求得的總成本差距較為接近，其中較優解其運算時間也較長；此外，其中兩種誤差值所求得之平均車輛使用總數相同但總成本有些微差距，可能原因為兩種誤差值下求解之車種使用比例不同而對總成本造成變化。

Conventional vehicle powered by fossil fuel has caused greenhouse effect for many years. Recently, the awareness of environmental protection inspires logis-tics using an alternative fuel vehicle (AFV) instead of a conventional vehicle for less CO2 emission. Among these AFV, the electric vehicle is popular and tech-nique mature which encourage logistics using it for city delivery. However, lim-ited refueling stations and driving range are disadvantages for electric vehicle. In this study, we formulate an integer programming model to minimize total travel time, vehicle idle time and charging time while considering vehicle loading, time window constraints and heterogeneous electric vehicle fleets. In solving phase we use record-to-record travel algorithm (RRT) with random remove, greedy insertion, 2-Opt and 2-Opt* to solve the large benchmark instances with three best RRT deviations 0.09, 0.07 and 0.05, we also find that solutions obtained by using these three parameters are similar, in which average total numbers of vehicle used in parameters 0.07 and 0.05 are the same but the total costs are different, this is because the rate of each vehicle type being used impact on the total cost.

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