應用行產生法求解考量交通壅塞時間與 軟時間窗格之車輛途程問題;Using Column Generation to Solve the Vehicle Routing Problem with Soft Time Window and Traffic Congestion Time

NCUIR > School of Management at National Central University > Graduate Institute of Industrial Management > Electronic Thesis & Dissertation > Item 987654321/64933

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/64933

Title:	應用行產生法求解考量交通壅塞時間與軟時間窗格之車輛途程問題;Using Column Generation to Solve the Vehicle Routing Problem with Soft Time Window and Traffic Congestion Time
Authors:	江承璋;Chiang,Cheng-Chang
Contributors:	工業管理研究所
Keywords:	車輛途程問題;軟時間窗格;交通壅塞時間;行產生法;Vehicle routing problem;Soft time window;Traffic congestion;Column generation
Date:	2014-07-28
Issue Date:	2014-10-15 14:34:25 (UTC+8)
Publisher:	國立中央大學
Abstract:	這篇論文主要探討是應用行產生法求解考量交通壅塞時間與軟時間窗格之車輛途程問題，在這問題中當車輛無法在各顧客特定時間窗格內服務顧客即會產生一懲罰成本加入目標式，並在特定的時間區間產生交通壅塞的尖峰時段，以上兩種性質都增加了求解可行解的困難度，在解法上我們利用行產生法的求解模式進行求解並利用動態規劃對子問題進行求解，在線性規劃中"行"用來表示路徑，並建立一子問題用來產生新的路徑進一步求出可行路徑及最佳解再利用狀態空間鬆弛法來求解，最後我們利用Solomom的例題進行測試。;This paper discusses a column generation for the Vehicle Routing Problem subject to soft time window restriction. In this problem, earliness or lateness is allowed although a penalty is incurred and added to solution cost whenever a vehicle serves a customer outside of his time window, and generate traffic congestion within a specific time. Both of them increase the complications of the problem and hard to find feasible solution. We present a column generation algorithm which is the exact optimization algorithm for this problem and then the branch and price help us optimization the result. In the linear programming column represents the route, and construct subproblem to generate new route for improve the answer and use state-space relaxation for dynamic programs to solve it and then we calculate different size of hard time windows problem that is from “Solomon benchmark problems”.
Appears in Collections:	[Graduate Institute of Industrial Management] Electronic Thesis & Dissertation

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