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研究生:劉宜青
研究生(外文):Yi-ChingLiu
論文名稱:以模擬最佳化求解公共自行車共享系統之初始車輛配置策略
論文名稱(外文):A Simulation Study on the Optimal Initial Bike Deployment for Public Bike Sharing Systems
指導教授:王逸琳
指導教授(外文):I-Lin Wang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工業與資訊管理學系專班
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:63
中文關鍵詞:自行車租借系統啟發式演算法模擬最佳化排序與選擇程序
外文關鍵詞:Bike Sharing SystemsRanking and SelectionOptimization via SimulationParticle Swarm Optimization
相關次數:
  • 被引用被引用:17
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近年來,因為國際油價格高漲不下與溫室效應的影響,全球積極提倡節能減碳的活動,鼓勵民眾使用零污染或低汙染之交通運輸工具,以減少交通工具帶給環境的衝擊,而為了能使環境能夠永續發展,標榜綠色交通的自行車租借系統,則於近幾年來在世界各大都會區蔚為風尚。以環保概念所設置的自行車租借系統,標榜騎乘者自助租還,倘若營運單位未針對各租借站配置適當的初始自行車總量,則租借者可能面臨「無車可借」或「無位可還」的情況,因而降低系統服務品質與使用率,甚而導致整個系統的失敗。

針對每日隨時變動的租還需求與不同的租借情境,本研究採用以一啟發式演算法結合模擬最佳化(Optimization via Simulation;OvS)的排序與選擇程序(Ranking and Selection;R&S)方式。在系統數龐大的空間中,搜尋最適解之集合,再以兩階段的選擇機制,先刪除表現值較差的系統,再從剩下的系統增加抽樣數,計算其平均績效表現值,並且考慮租借系統中之各站租借需求比例,從模擬實驗中找出在信心水準之下具有最佳期望績效的初始車輛配置方式與運補車數量,以減少顧客總等待時間或人次,進而提升租借系統整體服務水準。
In order to catch the dynamic traffic of the real-world bike sharing systems in practice, this paper presents simulation models that mimic the movements of bikers generated based on historical rental data. Using these simulation models as a core, we further show how to calculate the optimal initial bike deployment for each rental site by techniques of Ranking and Selection in the fields of Optimization via Simulation. By integrating the procedures of ranking and selection into the Particle Swarm Optimization algorithmic framework, we can converge to a good solution within shorter time. In addition, we also evaluate the performance of the estimated initial bike deployment obtained by a simplified linear programming model, and conclude that our proposed technique gives better deployment, and should be useful for supporting the logistics decision in practice.
目錄
摘要 i
Abstract ii
致謝 iii
目錄 iv
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 4
1.3 論文架構 5
第二章 文獻探討 6
2.1自行車租借系統建置策略 6
2.1.1確定型資料之租借系統 7
2.1.2不確定型資料之租借系統 8
2.2自行車車輛配置與運補 9
2.3模擬最佳化 11
2.4排序與選擇程序 14
2.4.1無差異程序 15
2.4.2子集合選擇程序 16
2.5粒子群演算法 18
2.6小結 21
第三章 公共自行車租借系統之初始車輛配置 22
3.1模擬最佳化之研究方法 22
3.2問題描述與假設 24
3.3自行車租借系統之模擬模式 25
3.3.1無自行車運補策略模式 26
3.3.2簡單自行車運補策略模式 28
3.4以PSO結合R&S求解租借站之初始車輛配置問題 31
3.4.1研究架構 31
3.4.2排序與選擇程序之NSGS 32
3.4.3範例模式說明 37
3.5以需求比例求解租借站之初始車輛配置問題 41
3.6小結 42
第四章 模擬資料與數值分析 43
4.1模擬之情境設定與資料產生 43
4.1.1模擬之情境設定 44
4.1.2模擬之初始資料 45
4.2數值分析 47
4.2.1租借情境之skewed distrubution 47
4.2.1.1 NR模擬模式 48
4.2.1.2 SR模擬模式 51
4.3小結 55
第五章 結論與未來研究方向 56
5.1結論與貢獻 56
5.2未來研究方向 58
參考文獻 59

表目錄
表3.1:租借站之資訊 38
表3.2:租借站距離之資訊(單位:公尺) 38
表3.3:租借者之需求資訊 39

圖目錄
圖1.1:YouBike微笑單車租借站點分佈圖 3
圖1.2:C-Bike租借站點分佈圖 3
圖2.1:貨櫃調度決策流程 10
圖2.2:模擬最佳化之模型 12
圖2.3:模擬最佳化方法 12
圖2.4:模擬時間控制方法 14
圖2.5:粒子群演算法之流程圖 20
圖3.1:NR模式之租借者抵達租借站點租車流程虛擬碼 27
圖3.2:NR模式之租借者抵達租借站點還車流程虛擬碼 28
圖3.3:SR模式之自行車租借系統執行運補作業流程虛擬碼 29
圖3.4:SR模式之指派運補車去租借站放置自行車流程虛擬碼 30
圖3.5:SR模式之指派運補車去租借站拿取自行車流程虛擬碼 30
圖3.6:NR模式結合NSGS程序之Screening虛擬碼 34
圖3.7:NR模式結合NSGS程序之Screening流程圖 35
圖3.8:NR模式結合NSGS程序之Selection虛擬碼 36
圖3.9:NR模式結合NSGS程序之Selection流程圖 37
圖4.1:法國巴黎Velib自行車租借系統規模示意圖 44
圖4.2:NR模式之租車等待人數(需求比例1:1)49
圖4.3:NR模式之租車等待人數(需求比例3:1)49
圖4.4:NR模式之還車等待人數(需求比例1:1)50
圖4.5:NR模式之還車等待人數(需求比例3:1)50
圖4.6:SR模式之租車等待人數(需求比例1:1)52
圖4.7:SR模式之租車等待人數(需求比例3:1)52
圖4.8:SR模式之還車等待人數(需求比例1:1)53
圖4.9:SR模式之還車等待人數(需求比例3:1)53
圖4.10:運補車之績效表現值 54
圖4.11:模擬方法之運補績效表現值 54
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