圖書館書籍通閱移送之車輛途程問題－巨集啟發式演算法之應用

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姓名

余彥儒(Yen-ju Yu) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

圖書館書籍通閱移送之車輛途程問題－巨集啟發式演算法之應用
(The Library Vehicle Routing Problem with Deliveries and Pickups: Application of Meta-Heuristics)

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摘要(中)

「圖書館書籍通閱移送之車輛途程問題」(LVRP-DP)係車輛於圖書總出發至各間圖書分館進行同時收送書籍之服務，各圖書館間彼此為供給點及需求點，而車輛所運送之書籍具有其特定的起點與迄點關係，求解問題的過程中必須同時處理「車輛路線規劃」之車流問題以及「書籍起迄指派」之書流問題，比單純之車輛途程問題更為複雜，因此屬於運算難度極高之組合數學問題，在過去研究中，最常採用的求解演算法為巨集啟發式演算法。基因演算法 ( Genetic Algorithm, GA) 其基本概念源自達爾文進化論所提出之「物競天擇、適者生存」，所發展而成的巨集啟發式演算法；蜂群最佳化演算法(Bee Colony Optimization, BCO)為根據蜜蜂採集花蜜之行為產生靈感而發展而成的巨集啟發式演算法。因此，本研究分別發展混合式基因演算法及混合式蜂群最佳化演算法，應用於求解LVRP-DP。最後，進行舊金山圖書館系統資料測試並與過去相關研究背景之文獻進行比較，另外，針對台北市立圖書館系統資料進行測試規劃。根據結果發現，本研究針對圖書館書籍通閱移送之車輛途程問題所發展之數學模型與求解演算法考慮層面比國內外現行之圖書館系統之運作方式更為周詳且具有彈性。

摘要(英)

The library vehicle routing problem with delivery and pickup (LVRP-PD) is a problem of finding optimal routes to transport origin-destination paired books in a library system comprising a main library and several library branches. The LVRP-PD is an extension of the traditional vehicle routing problem but is more difficult to solve because books are associated with fixed origin-destination pairs. To solve the LVRP-PD, two meta-heuristics called hybrid genetic algorithm (HGA) and hybrid bee colony optimization algorithm (HBCOA) are proposed. Two real library systems, one in San Francisco and the other in Taipei, are then demonstrated with the two meta-heuristics. The library vehicle routes scheduled by HGA and HBCOA are superior to the existing manual operations and those appeared in the literature in terms of some performance indices. The experiments also show that HGA is a bit better than HBCOA but the superiority is not significant. Hence both proposed solution algorithms have equally good potential for real applications in the future.

關鍵字(中)

★ 混合式基因演算法
★ 書流
★ 同時收送書籍
★ 巨集啟發式演算法
★ 車輛途程問題
★ 圖書館
★ 混合式蜂群最佳化演算法

關鍵字(英)

★ Hybrid Bee Colony Optimization
★ Pickup and Delivery
★ Meta-heuristic
★ Vehicle Routing Problem
★ Library
★ Book Flow
★ Hybrid Genetic Algorithm

論文目次

中文摘要 i
Abstract ii
誌謝 iii
圖目錄 viii
表目錄 xi
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍與內容 2
1.4 研究方法與流程 3
第二章文獻回顧 6
2.1 車輛途程問題相關文獻 6
2.1.1 車輛途程問題 6
2.1.2 VRP求解演算法 10
2.1.3 基因演算法概述 19
2.1.4 蜂群最佳化概述 24
2.2 一般化收送貨問題 29
2.2.1 救災車輛途程問題 31
2.2.2 撥召問題 32
2.3圖書館車輛途程問題 33
2.4 文獻回顧之結論 37
第三章模型建構 38
3.1 問題描述與特性 38
3.2 研究假設 39
3.3 符號說明 40
3.4 數學模型 42
第四章求解演算法 45
4.1 演算法架構 45
4.2 建構車輛初始途程 46
4.2.1 車輛初始途程解 46
4.3 改善車輛初始途程 49
4.3.1 混合式基因演算法 49
4.3.2 蜂群最佳化演算法 55
第五章範例測試與分析 61
5.1 舊金山圖書館系統範例測試 61
5.2舊金山圖書館系統範例之敏感度測試 65
5.2.1 變動使用車輛數 65
5.2.2 變動駕駛間工作時間公平性基準值 68
5.3 台北市立圖書館系統範例測試 72
第六章結論與建議 75
6.1 結論 75
6.2 建議 76
參考文獻 78
附錄A舊金山圖書館系統資料 84
附錄B 台北市立圖書館系統資料 87
附錄C 巨集式啟發演算法相關參數設定 92
C.1 初始解 92
C.2 混合式基因演算法參數設定測試 93
C.3 混合式蜂群最佳化演算法參數設定測試 97
C.4 初始解結合2-OPT、初始解結合巨集啟發式演算法(GA、BCO)及初始解結合混合式巨集啟發式演算法(HGA、HBCO)之求解測試 102
附錄D 目標值權重設定 103
附錄E 車輛路線 105
I. 舊金山圖書館系統範例測試 105
II. 舊金山圖書館系統範例之敏感度分析 110
III. 台北市立圖書館系統範例測試 121

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指導教授

陳惠國(Huey-Kuo Chen)

審核日期

2010-7-19

推文