使用基因演算法的E-Bus服務支援系統

A Supplementary Dispatch Method of the E-Bus Management System Using Genetic Algorithm

林益民(Lin, YiMin)

指導教授 ： 黃國勝

國立中正大學/工學院/電機工程學系/碩士(2012年)

摘要

近年來隨著網路的發展，GIS服務有愈發普及的趨勢，如能將此結合至傳統公車服務系統，將可擴展公車服務系統的支援功能，本論文提出一套公車救援系統，利用公車動態GPS資訊，使用基因演算法來計算各救援車輛的救援派遣方式，以達成減少救援時間、救援成本的目的，此外也針對本論文的情境，對基因演算法的各項參數做出分析。
在本系統中，由車輛監控模組監控各服務車輛的狀況，當事故發生時便立即回傳事故資訊給計算中心，以便提供最完善的救援派遣策略，以最快的速度或最少的成本將事故排除，此外本系統結合了車輛救援系統與車站資訊系統，使用Google MAP來展示救援路線、公車站點資訊及站點周遭的景點資訊，提供管理者指派救援車輛，也提供一般使用者或乘客瀏覽公車的動態資訊和周邊景點資訊，以期能提供一個完善的管理平台。

關鍵字

基因演算法 ； Google Map ； GIS

並列摘要

The development of Internet has increased recently and GIS has an increasingly popular trend. If the GIS combines to conventional bus service, it will be able to expand the support functions of bus service. This thesis proposes a rescue system with GIS service,
The dispatch strategy is computed by Genetic Algorithm according to the dynamic coordination of bus and rescue vehicle, the purpose of this system is to reduce time-cost and consumption of resources for rescue. In addition, all parameters of genetic algorithm will be discussed.
In this system, the accident condition is detected by the detector module on bus, when the accident occurred, the module will send back the bus information immediately, in order to provide the most comprehensive strategy for rescue dispatch, excluding all bus accident condition with the fastest speed or the least cost. Besides, this system combines the vehicle rescue system and bus information system, using Google Map displays the rescue route, information of bus station, information of scenic spot nearby bus station, providing supervisor assigns rescue vehicle, and also providing user or passenger browses the dynamic information of bus and information of scenic spot nearby bus station. The expectation is to provide a comprehensive management platform.

並列關鍵字

Genetic Algorithm ； Google Map ； GIS

參考文獻

[3]. Áslaug Sóley Bjarnadóttir, “Solving the Vehicle Routing Problem with Genetic Algorithms”, 2004, thesis.

[5]. Chunyu Ren, “Solving Min-Max Vehicle Routing Problem”, 2011, Journal of Software Vol. 6 No. 9

[6]. Jean Berger and Mohamed Barkaoui, “A Parallel Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows”, 1998, 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence.

[7]. Paolo Toth And Daniele Vigo, “An Exact Algorithm for the Vehicle Routing Problem with Backhauls”, 2011

[8]. Fermín Alfredo Tang Montané and Roberto Diéguez Galvão, “A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service”, 2006, Computers & Operations Research 33.