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研究生:陳建甫
研究生(外文):chien-fu chen
論文名稱:以基因搜尋法求解多桶格車輛途程問題
論文名稱(外文):Solving Multi-Compartments Vehicle Routing Problems by Genetic Search
指導教授:林高正林高正引用關係
指導教授(外文):Kao-Cheng Lin
學位類別:碩士
校院名稱:南台科技大學
系所名稱:工業管理研究所
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:2006
畢業學年度:95
語文別:中文
論文頁數:76
中文關鍵詞:多桶格車輛途程問題桶格限制先分群再定路線法基因搜尋
外文關鍵詞:Multi-compartments vehicle routing problemCompartment constraintsCluster-first uoute-second heuristicGenetic search
相關次數:
  • 被引用被引用:10
  • 點閱點閱:453
  • 評分評分:
  • 下載下載:110
  • 收藏至我的研究室書目清單書目收藏:1
多桶格車輛途程問題除常見的容量限制與時窗限制外,還具有桶格限制、互斥限制與指定限制,是具有多重限制的困難 NP-hard 問題,因此,不可能存在求解最佳解的多項式演算法,除非 P  NP。對這類問題,實務上通常只要求解答盡可能接近最佳解,故通常採用啟發式解法求解。由於啟發式解法不論是在實務應用、分枝與界限法之界限函數設計、或基因演算法之起始族群建立和遺傳算子設計上均扮演著重要的角色。故本研究將探討如何以先分群再定路線啟發式解法求解多桶格車輛途程問題。而對困難的 NP-hard 問題而言,傳統啟發式解法通常存在著相當的改進空間,因此有必要利用有效的搜尋機制加以改良,故本研究也將探討如何以基因搜尋機制改良多桶格車輛途程問題的啟發式解。而在以基因演算法求解問題時,其參數設定攸關解答的品質與演算收斂速度。最後,本研究將以實驗設計中的反應曲面法進行基因演算的參數設定,並以數值實驗驗證各種方法的求解績效。
In this study, we consider the multi-compartments vehicle routing problem. In addition to the classical capacity and time-window constraints, this problem also has compartment, compatibility, and assignment constraints. It is not only an NP-hard problem in strong sense, but also has multiple constraints. For such a problem, heuristic algorithms can be used in deriving bounding functions for a branch and bound algorithm, solving practical problems, and generating the initial population and designing genetic operators for a genetic algorithm. In this study, We propose a cluster-first route-second heuristic to solve the problem, based on Prim’s minimal spanning tree algorithm at first. Since the problem is NP-hard in strong sense, the solution obtained using a classical heuristic usually can be improved further. Therefore, it is necessary to design an efficient mechanism to improve such solutions. Based on the proposed classical heuristic, we then consider the problem how to improve solutions obtained using a genetic search. Because the parameter setting is important in a genetic algorithm, we also show how to set the parameters using the response surface methodology in the field of experiment design.
中文摘要 iv
英文摘要 v
誌謝辭 vi
圖目錄 ix
表目錄 x
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的與架構 3
1.4 研究範圍 5
1.5 研究步驟與流程 5
1.6 章節簡介 7
第二章 文獻探討 9
2.1 車輛途程問題簡介 9
2.2 具容量限制的車輛途程問題 12
2.3 具時窗限制的車輛途程問題 16
2.4 基因演算法簡介 21
2.5 以基因演算法求解車輛途程問題 24
第三章 啟發式解法 27
3.1 多桶格車輛途程問題 27
3.1.1 產品、客戶與車輛 27
3.1.2 限制條件 28
3.1.3 符號說明 28
3.1.4 目標函數 29
3.2 先分群再定路線的啟發式解法 31
3.2.1 Prim 的最小生成樹演算法 32
3.2.2 以 Prim 演算法為基礎的啟發式解法 34
3.3 數值範例 36
3.4 章節結論 41
第四章 以基因搜尋法改良解答 43
4.1 具基因修改的混合式基因演算法 44
4.2 加入懲罰值的基因演算法 47
4.3 以反應曲面法設定基因搜尋參數 49
第五章 數值實驗 56
5.1 實驗條件 56
5.2 實驗結果 60
5.2.1 臨時派車 60
5.2.2 日常派車 61
5.2.3 計劃性派車 62
5.3 章節結論 63
第六章 結論與後續研究 65
6.1 結論 65
6.2 後續研究與建議 68
參考文獻 70
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2.簡世釗 (2001),時窗與容量限制下車輛途程問題之研究,碩士論文,國立成功大學,工業管理研究所。
3.曾國雄、邱華凱 (2004),模糊決策系統,李允中、王小璠、蘇木春編著,模糊理論及其應用,第11章,全華科技圖書股份有限公司。
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