To solve vehicle routing problem with different fleets, two methodologies are developed. The first methodology adopts twophase strategy. In the first phase, the improved savings method is used to assign customers to a...To solve vehicle routing problem with different fleets, two methodologies are developed. The first methodology adopts twophase strategy. In the first phase, the improved savings method is used to assign customers to appropriate vehicles. In the second phase, the iterated dynasearch algorithm is adopted to route each selected vehicle with the assigned customers. The iterated dynasearch algorithm combines dynasearch algorithm with iterated local search algorithm based on random kicks. The second methodplogy adopts the idea of cyclic transfer which is performed by using dynamic programming algorithm, and the iterated dynasearch algorithm is also embedded in it. The test results show that both methodologies generate better solutions than the traditional method, and the second methodology is superior to the first one.展开更多
Companies are eager to have a smart supply chain especially when they have adynamic system. Industry 4.0 is a concept which concentrates on mobility andreal-time integration. Thus, it can be considered as a necessary ...Companies are eager to have a smart supply chain especially when they have adynamic system. Industry 4.0 is a concept which concentrates on mobility andreal-time integration. Thus, it can be considered as a necessary component thathas to be implemented for a dynamic vehicle routing problem. The aim of thisresearch is to solve large-scale DVRP (LSDVRP) in which the delivery vehiclesmust serve customer demands from a common depot to minimize transit costswhile not exceeding the capacity constraint of each vehicle. In LSDVRP, it isdifficult to get an exact solution and the computational time complexity growsexponentially. To find near-optimal answers for this problem, a hierarchicalapproach consisting of three stages: “clustering, route-construction, routeimprovement”is proposed. The major contribution of this paper is dealing withLSDVRP to propose the three-stage algorithm with better results. The resultsconfirmed that the proposed methodology is applicable.展开更多
Industry 4.0 is a concept that assists companies in developing a modern supply chain(MSC)system when they are faced with a dynamic process.Because Industry 4.0 focuses on mobility and real-time integration,it is a goo...Industry 4.0 is a concept that assists companies in developing a modern supply chain(MSC)system when they are faced with a dynamic process.Because Industry 4.0 focuses on mobility and real-time integration,it is a good framework for a dynamic vehicle routing problem(DVRP).This research works on DVRP.The aim of this research is to minimize transportation cost without exceeding the capacity constraint of each vehicle while serving customer demands from a common depot.Meanwhile,new orders arrive at a specific time into the system while the vehicles are executing the delivery of existing orders.This paper presents a two-stage hybrid algorithm for solving the DVRP.In the first stage,construction algorithms are applied to develop the initial route.In the second stage,improvement algorithms are applied.Experimental results were designed for different sizes of problems.Analysis results show the effectiveness of the proposed algorithm.展开更多
To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,...To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.展开更多
基金The National Natural Science Founda-tion of China ( No.70471039)the National Social Science Foundation of China (No.07BJY038)the Program for New Century Excellent Talents in University (No.NCET-04-0886)
文摘To solve vehicle routing problem with different fleets, two methodologies are developed. The first methodology adopts twophase strategy. In the first phase, the improved savings method is used to assign customers to appropriate vehicles. In the second phase, the iterated dynasearch algorithm is adopted to route each selected vehicle with the assigned customers. The iterated dynasearch algorithm combines dynasearch algorithm with iterated local search algorithm based on random kicks. The second methodplogy adopts the idea of cyclic transfer which is performed by using dynamic programming algorithm, and the iterated dynasearch algorithm is also embedded in it. The test results show that both methodologies generate better solutions than the traditional method, and the second methodology is superior to the first one.
文摘Companies are eager to have a smart supply chain especially when they have adynamic system. Industry 4.0 is a concept which concentrates on mobility andreal-time integration. Thus, it can be considered as a necessary component thathas to be implemented for a dynamic vehicle routing problem. The aim of thisresearch is to solve large-scale DVRP (LSDVRP) in which the delivery vehiclesmust serve customer demands from a common depot to minimize transit costswhile not exceeding the capacity constraint of each vehicle. In LSDVRP, it isdifficult to get an exact solution and the computational time complexity growsexponentially. To find near-optimal answers for this problem, a hierarchicalapproach consisting of three stages: “clustering, route-construction, routeimprovement”is proposed. The major contribution of this paper is dealing withLSDVRP to propose the three-stage algorithm with better results. The resultsconfirmed that the proposed methodology is applicable.
文摘Industry 4.0 is a concept that assists companies in developing a modern supply chain(MSC)system when they are faced with a dynamic process.Because Industry 4.0 focuses on mobility and real-time integration,it is a good framework for a dynamic vehicle routing problem(DVRP).This research works on DVRP.The aim of this research is to minimize transportation cost without exceeding the capacity constraint of each vehicle while serving customer demands from a common depot.Meanwhile,new orders arrive at a specific time into the system while the vehicles are executing the delivery of existing orders.This paper presents a two-stage hybrid algorithm for solving the DVRP.In the first stage,construction algorithms are applied to develop the initial route.In the second stage,improvement algorithms are applied.Experimental results were designed for different sizes of problems.Analysis results show the effectiveness of the proposed algorithm.
基金supported by Natural Science Foundation Project of Gansu Provincial Science and Technology Department(No.1506RJZA084)Gansu Provincial Education Department Scientific Research Fund Grant Project(No.1204-13).
文摘To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.
