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研究生:高秀梅
研究生(外文):Hsiu-Mei Kao
論文名稱:蟻群最佳化演算法於時窗限制之車輛途程問題的研究
論文名稱(外文):A Study of Ant Colony Optimization on Vehicle Routing Problem with Time Window
指導教授:梁韵嘉梁韵嘉引用關係
指導教授(外文):Yun-Chia Liang
學位類別:碩士
校院名稱:元智大學
系所名稱:工業工程與管理學系
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:2003
畢業學年度:91
語文別:中文
論文頁數:100
中文關鍵詞:時窗限制車輛途程問題蟻群最佳化演算法
外文關鍵詞:Vehicle Routing Problem with Time WindowAnt Colony Optimization
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蟻群最佳化演算法於時窗限制之車輛途程問題的研究
學生:高秀梅 指導教授:梁韵嘉 博士
元智大學工業工程與管理研究所
摘要
物流配送系統中,物流中心必須在最適當的時間內將正確數量的產品運送到正確的地點以滿足顧客的需求。但隨著客製化需求愈來愈受重視,顧客對於貨品交期時間之要求也愈嚴苛,因此在車輛途程問題中導入時窗因子,即VRPTW(Vehicle Routing Problem with Time Window),將使得車輛途程問題之應用更符合實際需要。
車輛途程問題之求解複雜度很高,屬於NP-Hard問題,以傳統數學模式求解時,須花費冗長的運算時間,因此為了于合理計算時間內獲得理想的解,啟發式方法便成為一較可行之途徑。基於此特性,本研究針對硬性時窗之VRPTW問題,建構蟻群最佳化演算法(ACO)為解題架構,將影響演算成效的因子作測試與分析,例如費洛蒙起始濃度( τ0 )、螞蟻數( N )、局部啟發式函數( η )、區域改善、區域更新、全域更新、以及ACO參數,其中又包括規則決定參數 (q0)、費洛蒙殘留比例(ρ)、及參數α與β等,並透過Solomon(1987)標準題庫的六類題型進行測試與驗證,測試50及100個顧客點之測試結果,以C類的表現為最好,其中在50個顧客點的題型中,又以R112及RC104的表現最佳,與文獻最佳解的距離誤差僅有0.04﹪及0.00﹪;而100個顧客點的題型中,則以R211及RC202的表現較文獻最佳解好,改善率達9.22﹪及5.5﹪。
關鍵字:時窗限制車輛途程問題、蟻群最佳化演算法
A Study of Ant Colony Optimization on Vehicle Routing Problem with Time Window
Student: Hsiu-Mei Kao Advisors:Dr. Yun-Chia Liang
Department of Industrial Engineering and Management
YUAN-ZE University
ABSTRACT
In a logistic distribution system, the right amount of products has to be shipped to the right place from the depot to meet customers’ needs. With customized requirement being valued more, customers are more demanding on merchandise’s on-time delivery. Thus, in order to meet practical needs, time window constraint is considered in this research and, therefore, the focus of this research is VRPTW (Vehicle Routing Problem with Time Window).
Solving vehicle routing problems involves with high complexity. Since the VRPTW is a NP-Hard problem, traditional mathematical programming approach needs lengthy computational time to find the optimal solution. In order to obtain good solutions within acceptable computing time in practice, metaheuristics becomes a better option. In this research, we employ one of metaheuristics - Ant Colony Optimization (ACO) — for VRPTW problem. To investigate the characteristic of ACO on VRPTW, a thorough parameter design are conducted, and then the optimal parameter and algorithm structure is suggested. Based on 56 benchmark problems by Solomon, we test and verify all six categories and test different customer sizes including 50 and 100. The result shows that in C category problem ACO performs the best. In addition, in two of 50-customer problems, R112 and RC104, the proposed algorithm provides the best percentage deviation from the best solution in the literature that is 0.04% and 0.00%, respectively. In two of 100-customer problems, R211 and RC202, ACO improves the known best results in the literature by 9.22% and 5.5%, respectively.
Keywords: Vehicle Routing Problem with Time Window, Ant Colony Optimization.
目 錄
中文摘要 I
英文摘要 II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
一、 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍 4
1.4 研究流程 5
二、 文獻探討 7
2.1 車輛途程問題之定義 7
2.2 時間窗 10
2.3 VRP與VRPTW的求解方法 11
2.3.1 初始解法 14
2.3.2 萬用啟發式演算法 16
2.3.3 傳統交換改善法 18
2.4 相關文獻探討 26
2.4.1 具時窗限制之車輛途程問題相關文獻 26
2.4.2 蟻群最佳化演算法之相關文獻 33
三、 研究方法 38
3.1 VRPTW之問題描述與基本假設 38
3.1.1 問題描述 38
3.1.2 假設條件與限制條件 40
3.2 VRPTW之數學模式建立 41
3.2.1 符號說明 41
3.2.2 VRPTW之數學模式 41
3.3 ACO演算法之架構 45
3.3.1 起源 45
3.3.2 步驟流程 47
3.4 路徑改善程序 54
四、 參數設計與實驗設計 58
4.1 測試題庫與最佳解 58
4.2 ACO演算法之最佳化設計 59
4.2.1 確立演算法之架構 62
4.2.2 ACO參數設定 67
4.2.3費洛蒙初始濃度之設定 71
4.3 例題測試與比較分析 73
五、 結論與建議 81
5.1 結論 81
5.2 未來研究方向 82
參考文獻 83
附錄1 VRPTW標準題庫之題型屬性一覽表(50個顧客點) 94
附錄2 Solomon題庫100個顧客點之最佳解相關資料 95
附錄3 Solomon題庫50個顧客點之最佳解相關資料 97
附錄4 實驗測試數據資料 99
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