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研究生:林智祥
研究生(外文):Chih-Hsian Lin
論文名稱:基於基因演算法於雲端運算上求解共乘服務問題
論文名稱(外文):A Genetic Algorithm based Approach to Solve the Carpool Service Problem in Cloud Computing
指導教授:黃士嘉黃士嘉引用關係
指導教授(外文):Shih-Chia Huang
口試委員:蔡偉和李宗演郭斯彥
口試委員(外文):Wei-Ho TsaiTrong-Yen LeeSy-Yen Kuo
口試日期:2012-07-04
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電腦與通訊研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:101
語文別:英文
論文頁數:31
中文關鍵詞:共乘服務問題基因演算法共乘
外文關鍵詞:Carpool service problemgenetic algorithmcarpool
相關次數:
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  • 收藏至我的研究室書目清單書目收藏:2
在全球許多都市,交通壅塞已經成為一個嚴重的問題,並已造成了許多不良的效應,像是空氣汙染、能源浪費、增加通勤時間以及產生二氧化碳等。共乘是一個有效解決交通壅塞問題的方法之一,這將有效地提升車輛的使用率以及減少閒置座位。在本文提出的行動客戶端,共乘使用者可以透過他們的智慧型手持裝置連接上行動通訊網路,送出共乘需求到全球雲端共乘服務模組,並接收共乘配對結果。接下來,全球雲端共乘服務模組是用來處理接收到的共乘需求,並透過本文提出的基於基因演算法之共乘路由與配對演算法,產生合適的共乘配對結果,其中,本演算法將大幅度的減少求解大量使用者之共乘路由配對問題時,所需要的運算時間。此外,為了發展一個可用於世界各地的共乘服務,我們整合了開放式全球地理圖資系統到我們提出的全球雲端共乘服務模組之中。我們的實驗結果顯示,在求解不同的測試情境之共乘路由與配對問題時,當同時考慮到解的品質與所需要的處理時間,本文提出的基於基因演算法之共乘路由與配對演算法有辦法提供近似於窮舉演算法所計算出來之擁有最短旅途成本的最佳解,而且運算速度也快於窮舉演算法以及亂數配對演算法,進而可以大幅地減少所需要的處理時間。

Traffic congestion has been a serious problem in many urban areas around the world, causing various negative impacts such as generation of air pollution, wasting of fuel, increased travel time, production of carbon dioxide emissions, and so on. Carpooling is one of the most effective solutions to traffic congestion. It consists of increasing the occupancy rate of cars by reducing the empty seats in these vehicles effectively. In this paper, we propose an intelligent carpool system called BlueNet. The proposed system involves two significant proposed modules: the Mobile Client module and the Cloud Global Carpool Services module. In our proposed Mobile Client module, the carpool users can send carpool requests and receive carpool matching results via the proposed Cloud Global Carpool Services module by using their smart handheld devices via the mobile communication network. The following Cloud Global Carpool Services module is used to manage requests and generate suitable match results through the proposed Genetic-based Carpool Route and Matching algorithm. The Genetic-based Carpool Route and Matching algorithm furthers the solution to the carpool route and matching problem by dramatically reducing the time required to match a large number of users. In order to develop a carpool service that can be used around the world, we integrated the open GIS system with abundant global geographical information into the proposed Cloud Global Carpool Services module. In regard to the quality of the solutions and processing time, the experimental results show that the proposed Genetic-based Carpool Route and Matching algorithm consistently is able to find carpool route and matching results that are among the most optimal. Use of the Genetic-based Carpool Route and Matching algorithm may result in a similar minimum travel cost as obtained by the exhaustive algorithm. However the Genetic-based Carpool Route and Matching algorithm operates with significantly less computational complexity than does the Exhaustive algorithm, thus requiring less time. The Genetic-based Carpool Route and Matching algorithm is also substantially faster than the Random Matching algorithm.

中文摘要 i
ABSTRACT ii
誌謝 iv
CONTENTS v
LIST OF TABLES vi
LIST OF FIGURES vii
Chapter 1 INTRODUCTION 1
Chapter 2 THE PROPOSED FRAMEWORK 4
2.1 Mobile Client Module 5
2.2 Cloud Global Carpool Services module 6
Chapter 3 PROPOSED GENETIC-BASED APPROACH 7
3.1 Evolution Initialization Module 9
3.1.1 Chromosome Representation procedure 9
3.1.2 Population Initialization procedure 11
3.2 Genetic Evolution Module 12
3.2.1 Chromosome Evaluation procedure 12
3.2.2 Chromosome Selection procedure 13
3.2.3 Chromosome Crossover procedure 14
3.2.4 Chromosome Mutation procedure 16
Chapter 4 EXPERIMENTAL RESULTS 17
4.1 The Comparison between GCRM, EA, and RM 17
4.2 Further Analysis on the GCRM Algorithm 21
Chapter 5 CONCLUSIONS 25
REFERENCES 27


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