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研究生:毛俊彬
研究生(外文):Chun-Pin Mao
論文名稱:應用蟻群最佳化演算法於含時窗限制之旅行推銷員問題
論文名稱(外文):Applying Ant Colony Optimization in Solving the Traveling Salesman Problem with Time Windows
指導教授:鄭啟斌鄭啟斌引用關係
指導教授(外文):Chi-Bin Cheng
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:工業工程與管理系碩士班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:68
中文關鍵詞:含時窗限制之旅行推銷員問題旅行推銷員問題蟻群最佳化
外文關鍵詞:Ant Colony OptimizationTraveling Salesman Problem with Time WindowsTraveling Salesman Problems
相關次數:
  • 被引用被引用:19
  • 點閱點閱:667
  • 評分評分:
  • 下載下載:120
  • 收藏至我的研究室書目清單書目收藏:1
含時窗限制之旅行推銷員問題定義為:找到一組最小成本路徑,使所有城市皆被服務一次,且必須符合各個城市之時窗限制。在實務上有許多重要問題,如生產排程與車輛途程等皆為含時窗限制之旅行推銷員問題之應用。學者Savelsberg(1985) 證實含時窗限制之旅行推銷員問題屬於NP-complete,若以最佳化解法求解缺乏效率,因此如何發展近似解法快速求得較佳解愈來愈受到重視。近年來由自然界現象所啟迪之蟻群最佳化演算法,已被證實在求解旅行推銷員問題有良好之績效。本研究利用蟻群最佳化求解旅行推銷員問題之優點,並在局部啟發式函數上作修改,使其適用於含時窗限制之旅行推銷員問題。經例題測試與比較,結果顯示本研究演算法在窄時間窗例題上有不錯之成效,且可在小節點數少的例題上得到最佳解。
The traveling salesman problem with time windows (TSPTW) is a problem of finding a minimum cost tour where all cities must be visited exactly once within their requesting time windows. This problem has important applications in practice such as scheduling and routing problems. Savelsberg (1985) showed that simply finding a feasible solution of TSPTW is NP-complete. Traditional optimization algorithms generally need exponential computation time in solving such a problem. Thus, the development of approximate algorithms has received more and more attention in recent years. Ant colony optimization (ACO) is one of the most recent methods inspired by biological behavior for developing approximate algorithms. It has been shown to be efficient to solve traveling salesman problems. In this research, a modified meta-heuristic based on ACO is applied to solve the TSPTW. Testing results on benchmark instances demonstrate that the proposed approach performs well on problem instances with narrower time windows; in particular, optimum solutions are found for some small-scale problems.
摘要 I
ABSTRACT II
誌謝 III
目錄 IV
表目錄 VII
圖目錄 IX
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 3
1.3研究步驟與流程 4
第二章 文獻探討 7
2.1旅行推銷員問題 7
2.2時間窗 11
2.3TSP與TSPTW之求解方法 13
2.4蟻群最佳化 18
第三章 研究方法 23
3.1問題描述及基本假設 23
3.1.1 問題描述 23
3.1.2 基本假設 26
3.2演算法架構 27
3.2.1 蟻群系統 28
3.2.2 型局部啟發式函數 31
3.2.3 演算法步驟流程 34
第四章 參數設定與例題測試 38
4.1測試題庫 38
4.2參數設定 39
4.2.1螞蟻數量 39
4.2.2狀態轉移規則決策參數 40
4.2.3費洛蒙衰退參數 41
4.2.4 型函數控制參數 42
4.2.5 型函數控制參數 42
4.2.6局部啟發式函數比重參數 43
4.2.7局部啟發式函數比重參數 44
4.3例題測試與比較分析 45
第五章 結論與建議 54
5.1結論 54
5.2建議 55
參考文獻 56
附錄一 67
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