装配式建筑配送中带时间窗VRP问题研究
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摘要
近年来,带时间窗的车辆路径问题(vehicle routing problem with time window,VRPTW)得到了广泛关注和研究.装配式建筑是近年来发展的一种新型建筑类型,预制构件配送过程中会带来诸多复杂工程问题.本文以装配式建筑配送为研究背景,分析了当前VRPTW相关文献的研究现状,建立基于VRPTW的扩展模型,并采用智能优化算法进行求解.以经典的SOLOMN算例作为扩展,随机生成18个不同结构的算例进行测试,实验结果验证了本文所提出算法的有效性.
During recent years,the vehicle routing problem with time window(VRPTW)has gained more and more researches and focuses.Additionally,the prefabricated building has become a typical building type,where the delivery process of the prefabricated precast units can be considered as a complex engineering optimization problem.Considering the background of the prefabricated building,in this study,a literature review of the recent development of VRPTW is firstly presented.Then,an extended VRPTW model is formulated.Next,based on the intelligent optimization algorithms,a novel heuristics is designed to solve the considered problem.Based on the classical SOLOMN instances,eighteen instances with different features are randomly generated to test the proposed algorithm.Then,the efficiency and effectiveness of the proposed algorithm is verified compared with other efficient algorithms.
During recent years,the vehicle routing problem with time window(VRPTW)has gained more and more researches and focuses.Additionally,the prefabricated building has become a typical building type,where the delivery process of the prefabricated precast units can be considered as a complex engineering optimization problem.Considering the background of the prefabricated building,in this study,a literature review of the recent development of VRPTW is firstly presented.Then,an extended VRPTW model is formulated.Next,based on the intelligent optimization algorithms,a novel heuristics is designed to solve the considered problem.Based on the classical SOLOMN instances,eighteen instances with different features are randomly generated to test the proposed algorithm.Then,the efficiency and effectiveness of the proposed algorithm is verified compared with other efficient algorithms.
引文
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[2]Podgorelec V.A survey of genetic algorithms for solving multi depot vehicle routing problem[J].Applied Soft Computing Journal,2015,27(C):519-532.
[3]Neves-Moreira F,da Silva D P,Guimar2es L,et al.The time window assignment vehicle routing problem with product dependent deliveries[J].Transportation Research Part E:Logistics and Transportation Review,2018,116:163-183.
[4]BRANDO,José.Iterated local search algorithm with ejection chains for the open vehicle routing problem with time windows[J].Computers&Industrial Engineering,2018,120:146-159.
[5]Niu Y,Yang Z,Chen P,et al.Optimizing the green open vehicle routing problem with time windows by minimizing comprehensive routing cost[J].Journal of Cleaner Production,2018,171:962-971.
[6]Koch H,Bortfeldt A,Wscher G.A hybrid algorithm for the vehicle routing problem with backhauls,time windows and three-dimensional loading constraints[J].OR Spectrum,2018,18(1):1-47.
[7]Reil S,Bortfeldt A,M9nch L.Heuristics for vehicle routing problems with backhauls,time windows,and 3Dloading constraints[J].European Journal of Operational Research,2018,266(3),877-894.
[8]Liu R,Tao Y,Xie X.An adaptive large neighborhood search heuristic for the vehicle routing problem with time windows and synchronized visits[J].Computers&Operations Research,2018,118(25):30-41.
[9]Hojabri H,Gendreau M,Potvin J Y,et al.Large neighborhood search with constraint programming for a vehicle routing problem with synchronization constraints[J].Computers&Operations Research,2018,92:87-97.
[10]Miranda D M,Branke J,Concei92o S V.Algorithms for the multi-objective vehicle routing problem with hard time windows and stochastic travel time and service time[J].Applied Soft Computing,2018,70:66-79.
[11]Paolucci M,Anghinolfi D,Tonelli F.Field services design and management of natural gas distribution networks:a class of vehicle routing problem with time windows approach[J].International Journal of Production Research,2018,56(3):1154-1170.
[12]Shi Y,Boudouh T,Grunder O.A hybrid genetic algorithm for a home health care routing problem with time window and fuzzy demand[J].Expert Systems with Applications,2017,72:160-176.
[13]刘宏令,刘俊娥.京津冀装配式建筑预制构件配送中心选址研究[J].工程经济,2018,28(8):58-62.
[14]李萍萍.装配式PC构件配送成本优化研究[D].西安:西安建筑科技大学,2016.
[15]彭星.建筑施工企业部品部件物流配送中心动态选址研究[D].重庆:重庆大学,2017.
[16]Karaboga D,Basturk B.A powerful and efficient algorithm for numerical function optimization:artificial bee colony(ABC)algorithm[J].Journal of Global Optimization,2007,39(3):459-471.
[17]李俊青,王永,桑红燕,等.人工蜂群优化及其在资源管理中的应用[J].聊城大学学报(自然科学版),2017,30(3):86-90.
[18]李俊青.求解分布式装配式建筑逆向物流问题的离散人工蜂群算法[J].聊城大学学报(自然科学版),2018,31(2):102-110.
[19]Li J,Duan P,Sang H,et al.An efficient optimization algorithm for resource-constrained steelmaking scheduling problems[J].IEEE Access,2018,125(3):35-39.
[20]Li J,Sang H,Han Y,et al.Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions[J].Journal of Cleaner Production,2018,18:584-598.
[21]Li J,Pan Q,Duan P,et al.Solving multi-area environmental/economic dispatch by Pareto-based chemical-reaction optimization algorithm[J].IEEE/CAA Journal of Automatica Sinica,2017,178(4):125-130.
[22]Li J Q,Wang J D,Pan Q K,et al.A hybrid artificial bee colony for optimizing a reverse logistics network system[J].Soft Computing,2017,21(20):6001-6018.