臺灣博碩士論文加值系統

由於電動機車與傳統機車最大的不同就在於其續航力的不足，而完善的基礎設施是成功推動電動機車的必要條件，本研究探討混合式電動機車能源補充站最佳化設址規劃問題，此問題屬於區位指派問題，其中能源補充站有充電站與電池交換站，若只考慮單一類型之設站模式，會導致能源補充站之使用率低或無法服務到更多電動機車使用者。在不同之人口密度以及高地價與低地價之比下，會造成設站比例的差異，而本研究根據不同之人口密度與地價比，在有限之預選設施數量下，達到最大服務量以及最小成本之雙目標。本研究依據能源補充站之服務量以及距離之限制設計數學模型，並使用Cplex驗證數學模型；根據誤差率、最大延展度、分布性與差異性，找出適合之多目標粒子群演算法的學習因子，此學習因子能使多目標粒子群演算法求出之柏拉圖前緣解更接近最佳解，並從柏拉圖前緣解中，透過多服務一位電動機車使用者所增加之平均成本，分析出在人口密度高時應設置交換站，與在人口密度低時應設置充電站，能夠更有效益的服務到電動機車使用者，最後將使用角度聚焦法與使用率，分別找出在人口密集度高與人口密集度低之城市中，充電站與交換站的最佳之設站比例。

The difference between electric scooters and traditional scooters was the durability. Setting the infrastructure completely was a necessary factor to promote electric scooters successfully. Our research discuss the optimal location problem of setting refuel stations. This issue is classified as a location-allocation problem. There were two types of refuel stations, recharge station and battery exchange station. The singular setting mold would decrease the utility of refuel stations or could not serve the most user. Different population density and land cost might cause the difference of setting pattern. Our research is based on different population density and land cost, and the multi - object are maximum utility and minimum cost under the limit of facility numbers. Our research design a mathematical mold which considered the limit of capacity and distance, and use Cplex to validate if the logic of mathematical mold and multi-object particle swarm optimization are correspond to each other or not. Our research use generational distance、maximum spread、spacing and diversity metric to find an optimal parameter of particle swarm optimization. Finally, our research use angle based focus method and utilization to find an optimal proportion of charge stations and battery exchange stations.

摘 要 i
ABSTRACT ii
誌謝 iii
目錄 iv
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 5
1.4 研究架構 5
第二章 文獻探討 7
2.1 電動機車能源補充設施 7
2.2 區位問題介紹 8
2.3 多目標粒子群聚演算法(Multi-Object Particle Swarm Optimization; MOPSO) 13
第三章 電動機車能源補充站模型規劃 15
3.1 問題描述 15
3.2 混合式電動機車能源補充站數學模型 15
3.3 多目標粒子群演算法 18
第四章 驗證與柏拉圖前緣解分析 23
4.1 驗證與參數設計 23
4.2 柏拉圖前緣解分析 28
第五章 結論 38
5.1結論 38
5.2未來研究方向 38
參考文獻 40
附錄 43

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