面向云配送模式的车辆调度问题及算法研究
|
摘要
随着世界经济的快速发展,物流产业作为国民经济中一个重要的服务产业,正在全球范围迅速发展,并逐渐成为国民经济的基础产业和发展动脉,其发展程度已经成为衡量一个国家现代化程度和综合国力的重要标志。然而,对物流配送的研究还主要停留在传统的配送模式上,随着物联网技术和电子商务的快速发展,物流配送模式已经发生了巨大变化,联合配送、动态配送、大规模跨区域配送等新的配送需求出现,传统的物流配送模式已经难以支持现代物流配送的需求。尤其是通信技术与云计算技术的出现,使得大规模跨区域联合配送成为可能,新的物流配送模式即将诞生。
物流配送是现代物流服务供应链中的关键环节,也是开展现代电子商务活动不可缺少的支持部分。对物流配送中车辆调度问题的研究是发展智能交通运输系统、构建综合物流系统和开展电子商务的基础。车辆调度问题经过近五十年的发展研究,己成为运筹学与组合优化领域的研究热点和前沿课题。现实生产和生活中,邮政投递问题、飞机航班安排、铁路车辆编组、码头调运、水运船舶调运、公共汽车调度问题以及电力调度问题等都可以抽象为车辆调度问题。随着电子商务、互联网与通信技术的发展,物流配送与车辆调度问题在各种连锁店、大型商场、快递等领域有广泛的应用前景。因此,对车辆调度问题的深入研究,具有较高的科学意义和工程应用价值。
论文的主要研究内容和创新成果如下:
①在分析云计算模式与社会化物流配送相互关系的的基础上提出云配送模式对云计算的内涵、发展现状以及应用情况展开分析,结合云计算模式与社会化物流配送的相似特征与联系,提出云配送模式的概念,分析云配送模式的特征及与传统物流配送模式的区别与联系。分析配送资源、配送云服务及配送云的相互关系与作用机理,构建面向社会配送的“公有云”配送服务平台,并对平台构建的关键技术展开分析。
②对云配送模式下固定需求的车辆调度问题展开研究
对云配送模式下固定需求的车辆调度问题展开研究,构建面向带有里程约束、载重约束和时间窗约束车辆调度问题的数学模型。在分析对比车辆调度问题求解算法的基础上,提出改进遗传算法对多约束条件下的车辆调度问题进行求解,设计针对车辆调度问题的自然数编码的遗传算法,改进传统交叉与变异操作方式,设计种群扩张机制,增强算法的快速寻优和全局收敛能力。结合实验仿真检验模型和算法的有效性,并对比分析不同约束的车辆调度问题求解结果。
③对云配送模式下动态需求的车辆调度问题展开研究
通过提出时间轴概念,对云配送模式下的动态车辆调度问题展开研究。利用时间轴结合动态信息驱动,记录配送网络发生的信息,将动态车辆调度问题转化为一系列静态车辆调度问题,考虑较为接近实际的约束条件和目标函数,构建具体时刻的车辆调度模型。根据动态车辆调度问题对求解算法时效性要求比较高的特点,提出利用量子理论改进传统遗传算法,设计量子遗传算法,采用量子位多样性的特点设计染色体编码,利用量子门旋转的种群迁移机制提高种群进化效率。结合车辆调度模型设计“初始优化+实时优化”的两阶段求解策略,当动态需求客户提出需求时,利用时间轴标记为不同的时刻,更新配送网络中的信息,实时地进行再优化。结合实验对模型和算法进行了仿真计算,验证算法与模型的有效性。
④对云配送模式下联合配送的车辆调度问题展开研究
对云配送模式下跨区域联合配送中的关键要素展开分析,提出本文对多配送中心、多车型、开放式动态车辆调度问题处理方法,并根据时间轴概念,建立联合配送的动态车辆调度模型。利用量子理论和云模型理论改进遗传算法,采用量子比特位设计遗传算法染色体编码,利用量子门旋转实现种群进化,采用云模型云滴的稳定性与倾向性特点改进交叉概率和变异概率的设置方式,设计云量子遗传算法,并对算法的性能参数、收敛性及计算复杂度进行分析。结合实验对设计的算法和模型进行实验分析。
⑤对智能车辆调度系统的设计与实现展开研究
根据云配送模式下配送任务的复杂性与动态性,设计面向对象的车辆调度系统。分析云配送模式下配送任务的需求和系统开发原则,搭建开放式的智能车辆调度系统框架结构。对系统的功能模块进行分析,提出本系统的业务流程和运行结构模式,并展示智能车辆调度系统的主界面和功能模块。
最后,对全文研究内容进行总结,展望云配送服务运作模式、车辆调度问题及求解算法的研究前景。
With the rapid development of the world's economic and progress of modern science and technology, as an important national service industry, the logistics industry is developing rapidly worldwide, and has become the basic industries and development artery of the national economy. Its level of development has become an important indicator of a country's modernization level and overall national strength. However, the logistics researches also stay in the traditional distribution model. With the rapid development of e-commerce and chain stores, logistics distribution model has changed dramatically, appears joint distribution, dynamic distribution, cross-regional large-scale distribution and so new distribution demand, making the traditional logistics model has been difficult to support the needs of modern logistics distribution. Especially the emergence of communication technology and cloud computing, making large-scale cross-regional joint distribution as possible, the new logistics model will be born.
Logistics distribution is the key to optimize the modern logistics services supply chain, is also essential support part to expanding modern e-commerce activities. The research of vehicle scheduling problem of logistics distribution is the foundation of developing intelligent transport systems, building integrated logistics systems and expanding e-commerce. Through nearly fifty years’development and study, vehicle scheduling problem has become research hot and cutting-edge topics of operations research and combinatorial optimization field. In realistic production and life, postal delivery problems, flight arrangements, railway vehicles marshalling, terminals transporting, shipping vessels transporting, bus scheduling, power scheduling problem and so can be abstracted for the vehicle scheduling problem. With the development of e-commerce, Internet and communication technology, logistics distribution and vehicle scheduling problem have a wide range of application prospect in the field of chain stores, shopping centers, express and so on. Therefore, the in-depth study of vehicle scheduling problem has high scientific significance and value in engineering.
Research contents and innovations of this thesis are as follows:
①The cloud distribution model was proposed combining with the relationship between cloud computing model and social logistics distribution.
The meaning of cloud computing, development status and application were analyzed, the cloud distribution model was proposed combining with similar characteristics and contraction between cloud computing model and social logistics distribution, the characteristics of cloud distribution patterns and differences and similarities with the traditional logistics model were analyzed. The relationship and action mechanism of distribution resources, delivery cloud services and distribution cloud were analyzed, community-oriented distribution of the "public cloud" service platform was built, and the key technology to building platform was analyzed.
②The vehicle scheduling problem with fixed demands under the cloud distribution mode was studied.
By having a research on the vehicle scheduling problem with fixed demands under the cloud distribution mode, a mathematical model of vehicle scheduling problem faced with mileage constraint, heavy constraint and time window constraint was established. Based on analysis and comparison the solving algorithms of vehicle scheduling problem, a improved genetic algorithm for solving the vehicle scheduling problem under multi-constraints was proposed, natural number coding genetic algorithm for vehicle scheduling problem was designed to improve the traditional crossover-mutation operation mode, and population expansion mechanism was designed to strengthen the rapid optimization and the global convergence ability. Experimental was combined to test the model and the effectiveness of the algorithm, and the solving results of vehicle scheduling problem with different constraints was contrasted.
③The vehicle scheduling problem with dynamic demands under the cloud distribution mode was studied.
By proposing the concept of timeline, a research on the vehicle scheduling problem with dynamic demands under the cloud distribution mode was conducted. Timeline and dynamic information driven mechanism was adopted to record the distribution network information, This transforms dynamic vehicle scheduling problem into a series of static vehicle scheduling problems, considering constraint conditions and objective function that more close to the reality, vehicle scheduling model for specific time was established. As vehicle scheduling problem has a high demand in time-sensitive, in this thesis, a method by using quantum theory to improve traditional genetic algorithm was put forwards, designed quantum genetic algorithm, the diversity characteristic of qubits was used to design chromosome coding, and the mechanism of quantum gate spin was adopted to improve the efficiency of the population evolution. Combined with vehicle scheduling model, a“Initial+Real-time”two-stage solution algorithm was design, when requirements proposed by the dynamic demand customer, timeline was used to mark different moments and update the information in the distribution network, then for real-time optimization. Combined with the experimental, simulation computation was used to the designed algorithm to verify the validity of the model and algorithm.
④The vehicle scheduling problem of joint distribution under the model of the clouds of distribution was studied.
By having an analysis on the key factors of interregional joint distribution under the model of clouds of distribution , the method to solve the Multi Vehicles, multi-distribution centers, open and dynamic vehicle scheduling problem was put forward in this thesis,and according to the timeline concept, a joint distribution of dynamic vehicle scheduling model was build. By using quantum theory and cloud model theory, genetic algorithm was improved, the genetic algorithm chromosome structure was designed by adopting the quantum bits , according to quantum gate rotating population evolution was realized, cloud droplets stability and tendentiousness characteristics of the cloud droplet of cloud model was used to improve the set mode of crossover probability and mutation probability, cloud quantum genetic algorithm was designed and the performance parameters, convergence and computing complexity of the algorithm was analyzed. The designed algorithm and model were got analyzed combined with experiment.
⑤The design and implementation of intelligent vehicle scheduling system were studied.
According to the complexity and dynamic nature under the model of the cloud distribution, object-oriented vehicles dispatch system was designed. By developing an analysis on principles of system development and requirements of the distributing task under the model of the clouds distribution, a frame structure of open intelligent vehicle scheduling system was build. Through analyzing function module of the system, the business process and operation model of this system was proposed and intelligent vehicle scheduling system of the interface and function modules were on display.
Finally, there is a summary on the studies of the whole thesis, the study prospect of operation mechanism of cloud distribution mode, as well as vehicle scheduling problem and its solving algorithm was given.
物流配送是现代物流服务供应链中的关键环节,也是开展现代电子商务活动不可缺少的支持部分。对物流配送中车辆调度问题的研究是发展智能交通运输系统、构建综合物流系统和开展电子商务的基础。车辆调度问题经过近五十年的发展研究,己成为运筹学与组合优化领域的研究热点和前沿课题。现实生产和生活中,邮政投递问题、飞机航班安排、铁路车辆编组、码头调运、水运船舶调运、公共汽车调度问题以及电力调度问题等都可以抽象为车辆调度问题。随着电子商务、互联网与通信技术的发展,物流配送与车辆调度问题在各种连锁店、大型商场、快递等领域有广泛的应用前景。因此,对车辆调度问题的深入研究,具有较高的科学意义和工程应用价值。
论文的主要研究内容和创新成果如下:
①在分析云计算模式与社会化物流配送相互关系的的基础上提出云配送模式对云计算的内涵、发展现状以及应用情况展开分析,结合云计算模式与社会化物流配送的相似特征与联系,提出云配送模式的概念,分析云配送模式的特征及与传统物流配送模式的区别与联系。分析配送资源、配送云服务及配送云的相互关系与作用机理,构建面向社会配送的“公有云”配送服务平台,并对平台构建的关键技术展开分析。
②对云配送模式下固定需求的车辆调度问题展开研究
对云配送模式下固定需求的车辆调度问题展开研究,构建面向带有里程约束、载重约束和时间窗约束车辆调度问题的数学模型。在分析对比车辆调度问题求解算法的基础上,提出改进遗传算法对多约束条件下的车辆调度问题进行求解,设计针对车辆调度问题的自然数编码的遗传算法,改进传统交叉与变异操作方式,设计种群扩张机制,增强算法的快速寻优和全局收敛能力。结合实验仿真检验模型和算法的有效性,并对比分析不同约束的车辆调度问题求解结果。
③对云配送模式下动态需求的车辆调度问题展开研究
通过提出时间轴概念,对云配送模式下的动态车辆调度问题展开研究。利用时间轴结合动态信息驱动,记录配送网络发生的信息,将动态车辆调度问题转化为一系列静态车辆调度问题,考虑较为接近实际的约束条件和目标函数,构建具体时刻的车辆调度模型。根据动态车辆调度问题对求解算法时效性要求比较高的特点,提出利用量子理论改进传统遗传算法,设计量子遗传算法,采用量子位多样性的特点设计染色体编码,利用量子门旋转的种群迁移机制提高种群进化效率。结合车辆调度模型设计“初始优化+实时优化”的两阶段求解策略,当动态需求客户提出需求时,利用时间轴标记为不同的时刻,更新配送网络中的信息,实时地进行再优化。结合实验对模型和算法进行了仿真计算,验证算法与模型的有效性。
④对云配送模式下联合配送的车辆调度问题展开研究
对云配送模式下跨区域联合配送中的关键要素展开分析,提出本文对多配送中心、多车型、开放式动态车辆调度问题处理方法,并根据时间轴概念,建立联合配送的动态车辆调度模型。利用量子理论和云模型理论改进遗传算法,采用量子比特位设计遗传算法染色体编码,利用量子门旋转实现种群进化,采用云模型云滴的稳定性与倾向性特点改进交叉概率和变异概率的设置方式,设计云量子遗传算法,并对算法的性能参数、收敛性及计算复杂度进行分析。结合实验对设计的算法和模型进行实验分析。
⑤对智能车辆调度系统的设计与实现展开研究
根据云配送模式下配送任务的复杂性与动态性,设计面向对象的车辆调度系统。分析云配送模式下配送任务的需求和系统开发原则,搭建开放式的智能车辆调度系统框架结构。对系统的功能模块进行分析,提出本系统的业务流程和运行结构模式,并展示智能车辆调度系统的主界面和功能模块。
最后,对全文研究内容进行总结,展望云配送服务运作模式、车辆调度问题及求解算法的研究前景。
With the rapid development of the world's economic and progress of modern science and technology, as an important national service industry, the logistics industry is developing rapidly worldwide, and has become the basic industries and development artery of the national economy. Its level of development has become an important indicator of a country's modernization level and overall national strength. However, the logistics researches also stay in the traditional distribution model. With the rapid development of e-commerce and chain stores, logistics distribution model has changed dramatically, appears joint distribution, dynamic distribution, cross-regional large-scale distribution and so new distribution demand, making the traditional logistics model has been difficult to support the needs of modern logistics distribution. Especially the emergence of communication technology and cloud computing, making large-scale cross-regional joint distribution as possible, the new logistics model will be born.
Logistics distribution is the key to optimize the modern logistics services supply chain, is also essential support part to expanding modern e-commerce activities. The research of vehicle scheduling problem of logistics distribution is the foundation of developing intelligent transport systems, building integrated logistics systems and expanding e-commerce. Through nearly fifty years’development and study, vehicle scheduling problem has become research hot and cutting-edge topics of operations research and combinatorial optimization field. In realistic production and life, postal delivery problems, flight arrangements, railway vehicles marshalling, terminals transporting, shipping vessels transporting, bus scheduling, power scheduling problem and so can be abstracted for the vehicle scheduling problem. With the development of e-commerce, Internet and communication technology, logistics distribution and vehicle scheduling problem have a wide range of application prospect in the field of chain stores, shopping centers, express and so on. Therefore, the in-depth study of vehicle scheduling problem has high scientific significance and value in engineering.
Research contents and innovations of this thesis are as follows:
①The cloud distribution model was proposed combining with the relationship between cloud computing model and social logistics distribution.
The meaning of cloud computing, development status and application were analyzed, the cloud distribution model was proposed combining with similar characteristics and contraction between cloud computing model and social logistics distribution, the characteristics of cloud distribution patterns and differences and similarities with the traditional logistics model were analyzed. The relationship and action mechanism of distribution resources, delivery cloud services and distribution cloud were analyzed, community-oriented distribution of the "public cloud" service platform was built, and the key technology to building platform was analyzed.
②The vehicle scheduling problem with fixed demands under the cloud distribution mode was studied.
By having a research on the vehicle scheduling problem with fixed demands under the cloud distribution mode, a mathematical model of vehicle scheduling problem faced with mileage constraint, heavy constraint and time window constraint was established. Based on analysis and comparison the solving algorithms of vehicle scheduling problem, a improved genetic algorithm for solving the vehicle scheduling problem under multi-constraints was proposed, natural number coding genetic algorithm for vehicle scheduling problem was designed to improve the traditional crossover-mutation operation mode, and population expansion mechanism was designed to strengthen the rapid optimization and the global convergence ability. Experimental was combined to test the model and the effectiveness of the algorithm, and the solving results of vehicle scheduling problem with different constraints was contrasted.
③The vehicle scheduling problem with dynamic demands under the cloud distribution mode was studied.
By proposing the concept of timeline, a research on the vehicle scheduling problem with dynamic demands under the cloud distribution mode was conducted. Timeline and dynamic information driven mechanism was adopted to record the distribution network information, This transforms dynamic vehicle scheduling problem into a series of static vehicle scheduling problems, considering constraint conditions and objective function that more close to the reality, vehicle scheduling model for specific time was established. As vehicle scheduling problem has a high demand in time-sensitive, in this thesis, a method by using quantum theory to improve traditional genetic algorithm was put forwards, designed quantum genetic algorithm, the diversity characteristic of qubits was used to design chromosome coding, and the mechanism of quantum gate spin was adopted to improve the efficiency of the population evolution. Combined with vehicle scheduling model, a“Initial+Real-time”two-stage solution algorithm was design, when requirements proposed by the dynamic demand customer, timeline was used to mark different moments and update the information in the distribution network, then for real-time optimization. Combined with the experimental, simulation computation was used to the designed algorithm to verify the validity of the model and algorithm.
④The vehicle scheduling problem of joint distribution under the model of the clouds of distribution was studied.
By having an analysis on the key factors of interregional joint distribution under the model of clouds of distribution , the method to solve the Multi Vehicles, multi-distribution centers, open and dynamic vehicle scheduling problem was put forward in this thesis,and according to the timeline concept, a joint distribution of dynamic vehicle scheduling model was build. By using quantum theory and cloud model theory, genetic algorithm was improved, the genetic algorithm chromosome structure was designed by adopting the quantum bits , according to quantum gate rotating population evolution was realized, cloud droplets stability and tendentiousness characteristics of the cloud droplet of cloud model was used to improve the set mode of crossover probability and mutation probability, cloud quantum genetic algorithm was designed and the performance parameters, convergence and computing complexity of the algorithm was analyzed. The designed algorithm and model were got analyzed combined with experiment.
⑤The design and implementation of intelligent vehicle scheduling system were studied.
According to the complexity and dynamic nature under the model of the cloud distribution, object-oriented vehicles dispatch system was designed. By developing an analysis on principles of system development and requirements of the distributing task under the model of the clouds distribution, a frame structure of open intelligent vehicle scheduling system was build. Through analyzing function module of the system, the business process and operation model of this system was proposed and intelligent vehicle scheduling system of the interface and function modules were on display.
Finally, there is a summary on the studies of the whole thesis, the study prospect of operation mechanism of cloud distribution mode, as well as vehicle scheduling problem and its solving algorithm was given.
引文
[1] Jepsen M., Petersen B..Spoorendonk S. et al. Subset-row inequalities applied to the vehicle-routing problem with time windows[J].Operations Research,2008, 56(2):497-511.
[2] Repoussis P.P., Tarantilis C.D., Ioannou G. The open vehicle routing problem[J].Journal of the Operational Research Society, 2007, 58:355-367.
[3]张景玲,赵燕伟等.多车型动态需求车辆路径问题建模及优化[J].计算机集成制造系统,2010,16(3):543-550.
[4]邹彤,李宁,孙德宝.不确定车辆数的有时间窗车辆路径问题的遗传算法[J].系统工程理论与实践,2004,24(6):134-138.
[5]刘云忠,宣慧玉.车辆路径问题的模型及算法研究综述[J].管理工程学报,2005,19(1):124-130.
[6] Srinivas P, Chao Z. Stochastic quasi-gradient algorithm for the off-line stochastic dynamic traffic assignment problem[J].Transportation Research Part B,2006,(40):179-206.
[7] Vis LF.A., Koster R.B.M., Savelsbergh M.W.P. Minimum vehicle fleet size under time-window constraints at a container terminal[J]. Transportation Science, 2005, 39(2): 249-260.
[8] Bent R., Hentenryck P.V. A two-stage hybrid local search for the vehicle routing problem[J]. Transportation Science, 2004, 38(4): 515-530.
[9] Braysy O. A reactive variable neighborhood search for the vehicle-routing problem[J]. Informs Journal on Computing, 2003, 15(4): 347-368.
[10] Russell R.A. Hybrid heuristics for the vehicle routing problem with time windows[J].Transportation Science, 2005, 29(2): 156-166.
[11] Alvarenga G.B., Mateus G.R., Tomi G. A genetic and set partitioning two-phase approach for the vehicle routing problem[J]. Computers&Operations Research, 2007, 34:1561-1584.
[12] Chen Z.L., Xu H. Dynamic column generation for dynamic vehicle routing with time windows[J]. Transportation Science, 2006, 40(1): 74-88.
[13] Ghafurian, S; Javadian, N. An ant colony algorithm for solving fixed destination multi-depot multiple traveling salesmen problems[J].Applied Soft Computing, 2011,11(1):1256-1262.
[14] Cui-hua Guan; Yan Cao; Jing Shi. Tabu search algorithm for solving the vehicle routing problem[C].2010 Third International Symposium on Information Processing (ISIP 2010), Qingdao China, 2010:474-587.
[15] Amico M.D., Monaci M., Pagani C. et al. Heuristic approaches for the fleet size and mix vehicle routing problem with time windows[J]. Transportation Science, 2007: 41(4): 516-526.
[16] Ioannou G., Kritikos M.N. A synthesis of assignment and heuristic solutions for vehicle routing with time windows[J]. Journal of the Operational Research Society, 2004, 55:2-11.
[17] Hashimoto H, Yagiura M, Imahori S, et al.Recent progress of local search in handling the time window constraints of the vehicle routing problem[J].4OR-A Quarterly Journal Of Operations Research,2010,8(3):211-238.
[18] Durbin M., Hoffman K. The dance of the thirty-ton trucks: dispatching and scheduling in a dynamic environment[J]. Operations Research, 2008, 56(1): 3-19.
[19] Owner O.O., Ergun O. Allocating costs in a collaborative transportation procurement network[J]. Transportation Science, 2008, 42(2): 146-165.
[20] Lim A., Rodrigues B., Xu Z. Transportation procurement with seasonally varying shipper demand and volume guarantees[J]. Operations Research, 2008, 56(3): 758-771.
[21]郎茂祥,胡思继.用混合遗传算法求解物流配送路径优化问题的研究[J].中国管理科学,2002,10(5):51-56.
[22]宋伟刚,张宏霞等.有时间窗约束非满载车辆调度问题的节约算法[J].东北大学学报,2006,27 (1):65-68.
[23] Perrier N., Langevin A., Amaya C.A. Vehicle routing for urban snow plowing operations[J].Transportation Science, 2008,42(1): 44-56.
[24] Cohn A., Root S., Wang A. et al. Integration of the load-matching and routing problem with equipment balancing for small package carriers[J]. Transportation Science, 2007,41(2):238-252.
[25] Listes O., Dekker R. A scenario aggregation-based approach for determining a robust airlinefleet composition for dynamic capacity allocation[J]. Transportation Science, 2005,39(3):367-382.
[26]张海刚,吴燕翔,顾幸生.基于免疫遗传算法的双向车辆调度问题实现[J].系统工程学报,2007, 22(6):649-654.
[27] Archetti C., Speranza M.G., Savelsbergh M.W.P. An optimization-based heuristic for the split delivery vehicle routing problem[J]. Transportation Science, 2008, 42(1): 22-31.
[28] Mercier A. A theoretical comparison of feasibility cuts for the integrated aircraft-routing and crew-pairing problem[J]. Transportation Science, 2008, 42(1): 87-104.
[29] Ergun O., Kuyzu G., Savelsbergh M. Reducing truckload transportation costs through collaboration[J]. Transportation Science, 2007, 41(2): 206-221.
[30] Babonneau F., Vial J.P. An efficient method to compute traffic assignment problems withelastic demands[J]. Transportation Science, 2008, 42(2): 249-260.
[31] Duchenne E., Laporte G, Semet F. The Undirected m-Peripatetic Salesman Problem:Polyhedral Results and New Algorithms[J]. Operations Research, 2007, 55(5):949-965.
[32] Laporte G., Mercure H.,NobertY. An exact algorithm for the asymmetrical capacitated vehicle routing problem[J].Networks, 1986,16: 33-46.
[33] Christofides N., Mingozzi A., Toth P.. Exact algorithm for the vehicle routing problem based on spanning tree and shortest path relaxations[J]. Mathematical programming, 1981,20: 255-282.
[34] Desrosiers J., Soumis F., Desrochers N..Routing with time windows by column generation[J]. Networks, 1984, 14: 545-565.
[35] [35]Laporte G.,Nobert Y.,Desrochers M..Optimal routing under capacity and distance restrictions[J].Operations Research, 1985,33: 1050-1073.
[36] Laport G.The Vehicle Routing Problem:An Overview of Exact and Approximate Algorithms [J]. European Journal of Operational Research,2002, 59:345-358.
[37] Gendreau M.Hertz A.Laporte GA Tabu Search Heuristics for the Vehicle Routing Problem[J]. Management Science,2004,40:276-1290.
[38] Gendreau M.A Tabu Search Heuristic for the Vehicle Routing Problem with Stochastic Demands and Customers [J].Operation Research,2006,44(3):469-477.
[39] Renaud J.Laporte GA Tabu Search Heuristic for the Multi-depot Vehicle Routing Problem[J]. Computers&Operations Research,2006,23(3):229-235.
[40] Abdolhamid Modares, Samerkae Somhom, Takao Enkawa. A Self-organizing Neural Network Approach for Multiple Traveling Salesman and Vehicle Routing Problems[J].International Transactions in Operational Research,2009,6(6):591-606.
[41] Y. Bassok, R. Ernst, Dynamic Allocations for Multi-product Distribution[J].Transportation Science,2005,29: 256-266 .
[42] Gendreau M, Potvin J-Y Vehicle dispatching with time-dependent travel times[J] .European Jounral of Operational Research,2003,144(2):379-396.
[43] Archetti C., Speranza M.G., Savelsbergh M.W.P. An optimization-based heuristic for the split delivery vehicle routing problem[J]. Transportation Science, 2008, 42(1): 22-31.
[44] Moghaddam R.T., Safaei N., Gholipour Y A h如rid simulated annealing for capacitated vehicle routing problems with the independent route length[J]. Applied Mathematics and Computation,2006, 176: 445-454.
[45]郎茂祥.装卸混合车辆路径问题的模拟退火算法研究[J].系统工程学报,2005, 20(5): 485-491.
[46]任传祥,张海,范跃祖.混合遗传-模拟退火算法在公交智能调度中的应用[J].系统仿真学报,2005,17(9):2075-2081.
[47]百度百科.云计算[EB/OL].[2010-10-22].http://baike.baidu.com/view/1316082.htm.
[48]维基百科.Cloud computing.[EB/OL].[2009-03-10].http://zh.wikipedia.org/zh-cn/Cloud- computing.
[49]中国云计算网.什么是云计算? [EB/OL].[2008-05-14]. http://www.cloud_computing. chian.cn./article/show article asp/article ID=1.
[50] Vaquero L M, Rodero-merino L, etal. A break in the clouds towards a cloud definition[J].ACM SIG-COMM computer communication review,2009,39(1):50-55.
[51] Zhang YX, Zhou YZ.A novel meta OS approach for streaming programs in ubiquitous computing[C].In: Proc. of IEEE the 21st Int’l Conf.on Advanced Information Networking and Applications (AINA 2007).Los Alamitos: IEEE Computer Society,2007:394-403.
[52] Zhang YX, Zhou YZ.Transparent Computing:A new paradigm for pervasive computing[C]. In: Ma JH, Jin H, Yang LT, Tsai JJP, eds.Proc. of the 3rd Int’l Conf. on Ubiquitous Intelligence and Computing (UIC 2006). Berlin, Heidelberg: Springer-Verlag,2006:1-11.
[53] Barroso LA, Dean J, H?lzle U. Web search for a planet: The Google cluster architecture[J]. IEEE Micro, 2003,23(2):22-28.
[54] Brin S, Page L.The anatomy of a large-scale hyper textual Web search engine[J].Computer Networks,1998,30(7):107-117.
[55] Ghemawat S, Gobioff H, Leung ST.The Google file system[C]. In: Proc. of the 19th ACM Symp. on Operating Systems Principles.New York: ACM Press, 2003:29-43.
[56] Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters[C]. In: Proc. of the 6th Symp.on Operating System Design and Implementation.Berkeley:USENIX Association,2004:137-150.
[57] Burrows M. The chubby lock service for loosely-coupled distributed systems[C]. In: Proc. of the 7th USENIX Symp. on Operating Systems Design and Implementation. Berkeley: USENIX Association,2006:335-350.
[58] Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE. Bigtable: A distributed storage system for structured data[C]. In: Proc. of the 7th USENIX Symp. on Operating Systems Design and Implementation.Berkeley:USENIX Association,2006:205-218.
[59] Dean J,Ghemawat S. Distributed programming with Mapreduce[J]. In: Oram A, Wilson G, eds. Beautiful Code. Sebastopol: O’Reilly Media, Inc., 2007:371-384.
[60] Dean J,Ghemawat S. MapReduce: Simplified data processing on large clusters[J].Communications of the ACM,2005,51(1):107-113.
[61] Pike R,Dorward S, Griesemer R,Quinlan S. Interpreting the data: Parallel analysis with Sawzall[J].Scientific Programming Journal,2005,13(4):277-298.
[62] Lamport L. Paxos made simple[J].ACM SIGACT News,2001,32(4):51-58.
[63] Barham P, Dragovic B, Fraser K, Hand S, Harris T, Ho A, Neugebaur R, Pratt I, Warfield A. Xen and the art of virtualization[C].In:Proc. of the 9th ACM Symp.On Operating Systems Principles. New York: Bolton Landing,2003:164-177.
[64] Smith JE, Nair R. Virtual Machines: Versatile Platforms for Systems and Processes. San Francisco[M]: Morgan Kaufmann Publishers,2005.
[65] Clark C, Fraser K, Hansen JG, Jul E, Pratt I, Warfield A. Live migration of virtual machines[C]. In: Proc. of the 2nd Symp. on Networked Systems Design and Implementation. Berkeley: USENIX Association,2005:273-286.
[66] Nelson M, Lim BH, Hutchins G. Fast transparent migration for virtual machines[C]. In: Proc. of the USENIX 2005 Annual Technical Conf. Berkeley: USENIX Association,2005:391-394.
[67] Isard M, Budiu M, Yu Y, Birrell A, Fetterly D. Dryad: Distributed data-parallel programs from sequential building blocks[C]. In: Proc.of the 2nd European Conf. on Computer Systems (EuroSys).,2007:59-72.
[68] DeCandia G, Hastorun D, Jampani M, Kakulapati G, Lakshman A, Pilchin A, Sivasubramanian S, Vosshall P, Vogels W. Dynamo:Amazon’s highly available key-value store[C]. In: Proc. of the 21st ACM Symp. on Operating Systems Principles. New York: ACMPress,2007:205-220.
[69] Chu LK, Tang H, Yang T, Shen K. Optimizing data aggregation for cluster-based Internet services. In: Proc. of the ACM SIGPLAN Symp[M]. On Principles and Practice of Parallel Programming. New York: ACM Press,2003:119-130.
[70] Yang HC, Dasdan A, Hsiao RL, Parker DS. Map-Reduce-Merge: Simplified relational data processing on large clusters[M].In: Proc.of the 2007 ACM SIGMOD Int’l Conf. on Management of Data. New York: ACM Press,2007:1029?1040.
[71]王鹏.走近云计算[M].北京:人民邮电出版社,2009.
[72] Peter Fingar.云计算:21世纪的商业平台[M].北京:电子工业出版社,2009.
[73]唐箭.云计算研究综述[J].内蒙古民族大学学报,2010,6(2):15-16.
[74]陈涛.云计算理论及技术研究[J].重庆交通大学学报(社科版),2009,9(4):104-106.
[75]关增产,吴清烈.大规模定制模式下的客户需求聚类分析与定制优化[J].统计与决策,2009(1):111-113.
[76]关增产.面向大规模定制的服务模块化研究[J].价值工程,2009,(11):99-103.
[77] B?Joseph Pine II.大规模定制:21世纪企业竞争的新前沿[M].人民大学出版社,2000,7.
[78] Fabrizio Salvador, Pablo Martin,De Holan, etal. Cracking thecode of mass customization[J].MIT Sloan Management Review,2009,50(3):70-78.
[79] Richard T G,Charles A W.Maximizing customer value via masscustomized e-consumer services[J].Business Horizons,2007(50):123-132.
[80]百度百科.云物流[EB/OL].[2010-10-30].http://baike.baidu.com/view/3304545.htm.
[81]物流论坛.“云物流”是网商物流瓶颈的解决办法[EB/OL].[2010-1-20]. http://www.tianya.cn/techforum/content/54/547833.shtml.
[82]徐颖.从“云物流”看第四方物流的价值贡献[J].商业流通,2011,3:16.
[83]童夏敏,胡尚杰.电子商务与现代物流的发展[J].中国商贸,2010,5:114-115.
[84]魏同涛.基于ABC的第三方物流企业成本核算方法的研究[D].昆明理工大学,2010.
[85]杨开宇.基于Mufti-Agent物流业务流程组合化建模仿真研究[J].武汉理工大学,2011.
[86] [86]IBM.云计算:随时随地访问IT资源[EB/OL].[2009-11-30]. http://www-01.ibm.com/software/cn/tivoli/solution/cloudcomputing.
[87]陈康,郑纬民.云计算:系统实例与研究现状[J].软件学报,2009,20(5):1337-1348.
[88]服务决定私有云配送模式的重要性[EB/OL].[2010-12-24]. http://www.dostor.com/article/2011/0629/6806881.shtml.
[89] Dantzig G B, Ramser J H. The truck dispatching problem[J]. Management Science,1959,4(6): 80-91.
[90]钟石泉,贺国光.有里程和时间窗约束的一体化车辆调度智能优化[J].系统过程与电子技术,2006,28(2):204-243.
[91]谢秉磊,李军.有时间窗的非满载车辆调度问题的遗传算法[J].系统工程学报,2000,15(3):290-294.
[92]张明善,唐小我.多车场满载货运车辆优化调度的网络流算法[J].系统工程学报,2002,17(3):216-220.
[93]经怀明,张立军.多车型车辆调度问题的建模与仿真[J].计算机仿真,2006,23(4):261-264.
[94] Gronalt M,Hartl RF,Reimann M.New savings based algorithms for time constrained pickup and delivery of full truckloads[J].European Journal of Operational Research,2003,757(3):520-35.
[95] K.C.Tan,Y.H.Chew,L.H.Lee.A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems[J].European Journal of Operational Research,2006(17) 2,855-885.
[96] Homberger J,Gehring H.Two-phase hybrid met heuristic for the vehicle routing problem with time windows[J].European Journal of Operational Research,2005(162):220-238.
[97] Fogel D E.Applying Evolutionary Programming to Selected TSPs[J].Cybem and syst:An Internation Journal,1993,24:27-36.
[98]李军,郭耀煌.物流配送车辆优化调度理论与方法[M].中国物资出版社,2001.
[99]雷,尹传忠.启发式方法确定最短配送路径[J].物流科技,2003,26(95):18-20.
[100]张丽萍,柴跃廷.车辆路径问题的改进遗传算法[J].系统工程理论与实践,2002,17 (8):79-84.
[101] Gendreau M, Laporte G, Seguin. An exact algorithm for the vehicle routing problem with stochastic demands and customers[J].Transportation Science,1995,29(2):14 3-155.
[102] Goldberg D E. Genetic algorithms in search, optimization and machine learning[M].Addison-Wesley press,1989.
[103]谢秉磊,郭耀煌,郭强.动态车辆路径问题:现状与展望[J].系统工程理论方法应用,2002, 11(2):116-121.
[104]张建勇,李军.模糊需求信息条件下的实时动态车辆调度问题研究[J].管理工程学报,2004, 18(4):69-73.
[105]宋远清,李永生,梁慎清.需求随机车辆调度问题的遗传算法研究[J].计算机技术与发展,2009, 19(2):230-233.
[106] Juliane Müller. Approximative solutions to the bicriterion Vehicle Routing Problem with Time Windows[J]. European Journal of Operational Research, 2010,20(2):223–231.
[107] Jǒrn Schǒnberger, Herbert Kopfer. Online decision making and automatic decision model adaptation[J].Computers & Operations Research 2009(36):1740-1750.
[108] Clara Novoa, Robert Storer. An approximate dynamic programming approach for the vehicle routing problem with stochastic demands[J].European Journal of Operational Research, 2009,19(6): 509-515.
[109] Rodrigo Moretti Branchini,ViníciusAmaralArmentano. Adaptive granular local search heuristic for a dynamic vehicle routing problem[J]. Computers & Operations Research,2009(36): 2955- 2968.
[110] Narayanan A, Moore M. Quantum-inspired genetic algorithm[C].IEEE Congress on Evolutionary Computation.Nogaya,1996:61-66.
[111] Han K H, Kim J H. Quant um2inspired evolutionary algorithm for a class of combinatorial optimization[J].IEEE Trans on Evolutionary Computation,2002,6(6):580-593.
[112] Hichem TALBI, Amer DRAA, Mohamed BATOUCHE. A New Quantum-Inspired Genetic Algorithm for Solving the Traveling Salesman Problem[C].IEEE International Conference on Industrial Technology,2004: 1192-1197.
[113]王凌.量子进化算法研究进展[J].控制与决策,2008,23(12):1321-1327.
[114]赵燕伟,彭典军,张景玲,吴斌.有能力约束车辆路径问题的量子进化算法[J].系统工程理论与实践,2009,29(2):159-167.
[115]吕军,冯博琴,李波.多约束条件车辆路径问题的两阶段遗传退火算法[J].西安交通大学学报,2005,39(12):1299-1302.
[116] Han KH, Park KH, Lee CH, et al. Parallel quantum-inspired genetic algorithm for combinatorial optimization problem[J]. IEEE Congress on Evolutionary Computation,2001:1422-1429.
[117]百度百科,时间轴概念. [EB/OL].[2011-3-19].http://baike.baidu.com/view/30505.htm.
[118] Narayanan A,Moore M. Quantum inspired genetic algorithm[C].Pro Of IEEE InternationalConference on Evolutionary Computation:IEEE Press,1996:61-66.
[119]杨淑芳,焦李成,刘芳.量子进化算法[J].工程数学学报,2006,23(3):235-246.
[120] Hey T. Quantum computing:An introduction[J].Computing&Control Engineering Journal,1999,10(3):105-112.
[121]施朝春,王旭,葛显龙.带有时间窗的多配送中心车辆调度问题研究[J].计算机工程与应用,2009,45(34):21-24.
[122]杨淑媛,刘芳.一种基于量子染色体的遗传算法[J].西安电子科技大学学报,2004,31(1): 76-81.
[123]邢文训,谢金星.现代优化计算方法[M].北京.清华大学出版社,2007.
[124] Sariklis D, Powell S. A heuristic method for the open vehicle routing problem[J]. Journal of the Operational Research Society,2000,51(5):564-573.
[125] Tarantilis C D, Kiranoudis C T. Distribution of fresh meat[J]. Journal of Food Engineering,2002,51(1):85-91.
[126] Yu Shiwei, Ding Chang, Zhu Kejun. A hybrid GA-TS algorithm for open vehicle routing optimization of coal mines material[J]. Expert Systems with Applications, 2011(38):10568-10573.
[127] MirHassani S.A., Abolghasemi N.. A particle swarm optimization algorithm for open vehicle routing problem[J]. Expert Systems with Applications, 2011(38):11547-11551.
[128] Emmanouil E. Zachariadis, Chris T. Kiranoudis. An open vehicle routing problem metaheuristic for examining wide solution neighborhoods[J].Computers&Operations Research,2010(37):712-723.
[129] Cao Erbao, Lai Mingyong. The open vehicle routing problem with fuzzy demands[J]. Expert Systems with Applications,2010,3(7):2405-2411.
[130] Repoussis P. P., Tarantilis C. D., Braysy O..A hybrid evolution strategy for the open vehicle routing problem[J].Computers &Operations Research,2010,8(7):443-455.
[131]符卓.带装载能力约束的开放式车辆路径问题及其紧急搜索算法研究[J].系统工程理论与实践,2004,3(3):123-128.
[132]王雪莲,汪波,钟石泉.一类半开放式车辆路径问题及其禁忌算法研究[J].系统仿真学报,2008,20(8):1969-1972.
[133]李延晖,刘向.沿途补货的多车场开放式车辆路径问题及蚁群算法[J].计算机集成制造系统,2008,14(3):557-562.
[134]徐天亮.运输与配送[M].北京.中国物资出版社,2002.
[135]汤浅和夫.物流管理入门[M].北京:中国铁道出版社,1986.
[136]王之泰.现代物流学[M].北京:北京物资学院出版社,1998.
[137]王之泰.现代物流管理[M].北京:中国工人出版社,2001.
[138]崔介何.物流学[M].北京:北京大学出版社,2003.
[139]何明坷.现代物流与配送中心:推动流通创新的趋势[M].北京:人民交通出版社,1997.
[140]石洪波,郎茂祥. JD多车型配送车辆调度问题的模型及其禁忌搜索算法研究[J].长沙交通学院学报,2005,21(3):73-77.
[141]李冰.多车型确定性动态车辆调配问题[J].管理工程学报,2006,20(3):52-56.
[142]陶胤强,牛惠民.带时间窗的多车型多费用车辆路径问题的模型和算法[J].交通运输系统工程与信息,2008,8(1):113-117.
[143]陈美军,张志胜等.多约束下车辆路径问题的蚁群算法研究[J].中国机械工程,2008,19(16):1939-1944.
[144]李建,张永等.第三方物流多车型硬时间窗路线问题研究[J].系统工程学报,2008,23(1):74-80.
[145]熊浩,胡列格.多车型动态车辆调度及其遗传算法[J].系统工程,2009,27(10):21-24.
[146] Dror M, Ball M. inventory-routing: reduction from an annul to a short-period problem[J].Naval logistics,1987,34:891-905.
[147]刘志硕,申金升,关伟.车辆路径问题的混合蚁群算法设计与实现[J].管理科学学报,2007,11(10):15-22.
[148] Brandao J. A tabu search algorithm for the heterogeneous fixed fleet vehicle routing problem[J]. Computers Operations Research,2008,38(1): 140-151.
[149]李德毅.不确定性人工智能[M].北京.国防工业出版社,2005.
[150] Holland J H.Adaptation in Natural and Artificial System[M].Ann Arbor: University of Michigan Press,1975.
[151]金晶,苏勇.一种改进的自适应遗传算法[J].计算机工程与应用,2005,41(18): 64-69.
[152] Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetic algorithm[J]. IEEE Trans on System,Man and Cybernetics,1994,24(4): 656-667.
[153] Smith J E.,Fogarty T C.Operator and parameter adaptation in genetic algorithms[J].Soft Computing,1997,1(2): 81-87.
[154] HERRERA F, LOZANO M. Adaptation of genetic algorithm parameters based on fuzzy logic controller[C]. HERRERA F, VERDEGAY J L. Genetic Algorithms and Soft Computing. Berlin, Germany:Springer-Verlag,1996: 95-125.
[155] HERRERA F, LOZANO M. Adaptive genetic operators based on convolutions with fuzzy behaviors[J]. IEEE Trans on Evolutionary computation,2001,5(2):149-165.
[156]李德毅,孟海军,史雪梅.隶属云和隶属云发生器[J].计算机研究与发展,1995(6): 15-20.
[2] Repoussis P.P., Tarantilis C.D., Ioannou G. The open vehicle routing problem[J].Journal of the Operational Research Society, 2007, 58:355-367.
[3]张景玲,赵燕伟等.多车型动态需求车辆路径问题建模及优化[J].计算机集成制造系统,2010,16(3):543-550.
[4]邹彤,李宁,孙德宝.不确定车辆数的有时间窗车辆路径问题的遗传算法[J].系统工程理论与实践,2004,24(6):134-138.
[5]刘云忠,宣慧玉.车辆路径问题的模型及算法研究综述[J].管理工程学报,2005,19(1):124-130.
[6] Srinivas P, Chao Z. Stochastic quasi-gradient algorithm for the off-line stochastic dynamic traffic assignment problem[J].Transportation Research Part B,2006,(40):179-206.
[7] Vis LF.A., Koster R.B.M., Savelsbergh M.W.P. Minimum vehicle fleet size under time-window constraints at a container terminal[J]. Transportation Science, 2005, 39(2): 249-260.
[8] Bent R., Hentenryck P.V. A two-stage hybrid local search for the vehicle routing problem[J]. Transportation Science, 2004, 38(4): 515-530.
[9] Braysy O. A reactive variable neighborhood search for the vehicle-routing problem[J]. Informs Journal on Computing, 2003, 15(4): 347-368.
[10] Russell R.A. Hybrid heuristics for the vehicle routing problem with time windows[J].Transportation Science, 2005, 29(2): 156-166.
[11] Alvarenga G.B., Mateus G.R., Tomi G. A genetic and set partitioning two-phase approach for the vehicle routing problem[J]. Computers&Operations Research, 2007, 34:1561-1584.
[12] Chen Z.L., Xu H. Dynamic column generation for dynamic vehicle routing with time windows[J]. Transportation Science, 2006, 40(1): 74-88.
[13] Ghafurian, S; Javadian, N. An ant colony algorithm for solving fixed destination multi-depot multiple traveling salesmen problems[J].Applied Soft Computing, 2011,11(1):1256-1262.
[14] Cui-hua Guan; Yan Cao; Jing Shi. Tabu search algorithm for solving the vehicle routing problem[C].2010 Third International Symposium on Information Processing (ISIP 2010), Qingdao China, 2010:474-587.
[15] Amico M.D., Monaci M., Pagani C. et al. Heuristic approaches for the fleet size and mix vehicle routing problem with time windows[J]. Transportation Science, 2007: 41(4): 516-526.
[16] Ioannou G., Kritikos M.N. A synthesis of assignment and heuristic solutions for vehicle routing with time windows[J]. Journal of the Operational Research Society, 2004, 55:2-11.
[17] Hashimoto H, Yagiura M, Imahori S, et al.Recent progress of local search in handling the time window constraints of the vehicle routing problem[J].4OR-A Quarterly Journal Of Operations Research,2010,8(3):211-238.
[18] Durbin M., Hoffman K. The dance of the thirty-ton trucks: dispatching and scheduling in a dynamic environment[J]. Operations Research, 2008, 56(1): 3-19.
[19] Owner O.O., Ergun O. Allocating costs in a collaborative transportation procurement network[J]. Transportation Science, 2008, 42(2): 146-165.
[20] Lim A., Rodrigues B., Xu Z. Transportation procurement with seasonally varying shipper demand and volume guarantees[J]. Operations Research, 2008, 56(3): 758-771.
[21]郎茂祥,胡思继.用混合遗传算法求解物流配送路径优化问题的研究[J].中国管理科学,2002,10(5):51-56.
[22]宋伟刚,张宏霞等.有时间窗约束非满载车辆调度问题的节约算法[J].东北大学学报,2006,27 (1):65-68.
[23] Perrier N., Langevin A., Amaya C.A. Vehicle routing for urban snow plowing operations[J].Transportation Science, 2008,42(1): 44-56.
[24] Cohn A., Root S., Wang A. et al. Integration of the load-matching and routing problem with equipment balancing for small package carriers[J]. Transportation Science, 2007,41(2):238-252.
[25] Listes O., Dekker R. A scenario aggregation-based approach for determining a robust airlinefleet composition for dynamic capacity allocation[J]. Transportation Science, 2005,39(3):367-382.
[26]张海刚,吴燕翔,顾幸生.基于免疫遗传算法的双向车辆调度问题实现[J].系统工程学报,2007, 22(6):649-654.
[27] Archetti C., Speranza M.G., Savelsbergh M.W.P. An optimization-based heuristic for the split delivery vehicle routing problem[J]. Transportation Science, 2008, 42(1): 22-31.
[28] Mercier A. A theoretical comparison of feasibility cuts for the integrated aircraft-routing and crew-pairing problem[J]. Transportation Science, 2008, 42(1): 87-104.
[29] Ergun O., Kuyzu G., Savelsbergh M. Reducing truckload transportation costs through collaboration[J]. Transportation Science, 2007, 41(2): 206-221.
[30] Babonneau F., Vial J.P. An efficient method to compute traffic assignment problems withelastic demands[J]. Transportation Science, 2008, 42(2): 249-260.
[31] Duchenne E., Laporte G, Semet F. The Undirected m-Peripatetic Salesman Problem:Polyhedral Results and New Algorithms[J]. Operations Research, 2007, 55(5):949-965.
[32] Laporte G., Mercure H.,NobertY. An exact algorithm for the asymmetrical capacitated vehicle routing problem[J].Networks, 1986,16: 33-46.
[33] Christofides N., Mingozzi A., Toth P.. Exact algorithm for the vehicle routing problem based on spanning tree and shortest path relaxations[J]. Mathematical programming, 1981,20: 255-282.
[34] Desrosiers J., Soumis F., Desrochers N..Routing with time windows by column generation[J]. Networks, 1984, 14: 545-565.
[35] [35]Laporte G.,Nobert Y.,Desrochers M..Optimal routing under capacity and distance restrictions[J].Operations Research, 1985,33: 1050-1073.
[36] Laport G.The Vehicle Routing Problem:An Overview of Exact and Approximate Algorithms [J]. European Journal of Operational Research,2002, 59:345-358.
[37] Gendreau M.Hertz A.Laporte GA Tabu Search Heuristics for the Vehicle Routing Problem[J]. Management Science,2004,40:276-1290.
[38] Gendreau M.A Tabu Search Heuristic for the Vehicle Routing Problem with Stochastic Demands and Customers [J].Operation Research,2006,44(3):469-477.
[39] Renaud J.Laporte GA Tabu Search Heuristic for the Multi-depot Vehicle Routing Problem[J]. Computers&Operations Research,2006,23(3):229-235.
[40] Abdolhamid Modares, Samerkae Somhom, Takao Enkawa. A Self-organizing Neural Network Approach for Multiple Traveling Salesman and Vehicle Routing Problems[J].International Transactions in Operational Research,2009,6(6):591-606.
[41] Y. Bassok, R. Ernst, Dynamic Allocations for Multi-product Distribution[J].Transportation Science,2005,29: 256-266 .
[42] Gendreau M, Potvin J-Y Vehicle dispatching with time-dependent travel times[J] .European Jounral of Operational Research,2003,144(2):379-396.
[43] Archetti C., Speranza M.G., Savelsbergh M.W.P. An optimization-based heuristic for the split delivery vehicle routing problem[J]. Transportation Science, 2008, 42(1): 22-31.
[44] Moghaddam R.T., Safaei N., Gholipour Y A h如rid simulated annealing for capacitated vehicle routing problems with the independent route length[J]. Applied Mathematics and Computation,2006, 176: 445-454.
[45]郎茂祥.装卸混合车辆路径问题的模拟退火算法研究[J].系统工程学报,2005, 20(5): 485-491.
[46]任传祥,张海,范跃祖.混合遗传-模拟退火算法在公交智能调度中的应用[J].系统仿真学报,2005,17(9):2075-2081.
[47]百度百科.云计算[EB/OL].[2010-10-22].http://baike.baidu.com/view/1316082.htm.
[48]维基百科.Cloud computing.[EB/OL].[2009-03-10].http://zh.wikipedia.org/zh-cn/Cloud- computing.
[49]中国云计算网.什么是云计算? [EB/OL].[2008-05-14]. http://www.cloud_computing. chian.cn./article/show article asp/article ID=1.
[50] Vaquero L M, Rodero-merino L, etal. A break in the clouds towards a cloud definition[J].ACM SIG-COMM computer communication review,2009,39(1):50-55.
[51] Zhang YX, Zhou YZ.A novel meta OS approach for streaming programs in ubiquitous computing[C].In: Proc. of IEEE the 21st Int’l Conf.on Advanced Information Networking and Applications (AINA 2007).Los Alamitos: IEEE Computer Society,2007:394-403.
[52] Zhang YX, Zhou YZ.Transparent Computing:A new paradigm for pervasive computing[C]. In: Ma JH, Jin H, Yang LT, Tsai JJP, eds.Proc. of the 3rd Int’l Conf. on Ubiquitous Intelligence and Computing (UIC 2006). Berlin, Heidelberg: Springer-Verlag,2006:1-11.
[53] Barroso LA, Dean J, H?lzle U. Web search for a planet: The Google cluster architecture[J]. IEEE Micro, 2003,23(2):22-28.
[54] Brin S, Page L.The anatomy of a large-scale hyper textual Web search engine[J].Computer Networks,1998,30(7):107-117.
[55] Ghemawat S, Gobioff H, Leung ST.The Google file system[C]. In: Proc. of the 19th ACM Symp. on Operating Systems Principles.New York: ACM Press, 2003:29-43.
[56] Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters[C]. In: Proc. of the 6th Symp.on Operating System Design and Implementation.Berkeley:USENIX Association,2004:137-150.
[57] Burrows M. The chubby lock service for loosely-coupled distributed systems[C]. In: Proc. of the 7th USENIX Symp. on Operating Systems Design and Implementation. Berkeley: USENIX Association,2006:335-350.
[58] Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE. Bigtable: A distributed storage system for structured data[C]. In: Proc. of the 7th USENIX Symp. on Operating Systems Design and Implementation.Berkeley:USENIX Association,2006:205-218.
[59] Dean J,Ghemawat S. Distributed programming with Mapreduce[J]. In: Oram A, Wilson G, eds. Beautiful Code. Sebastopol: O’Reilly Media, Inc., 2007:371-384.
[60] Dean J,Ghemawat S. MapReduce: Simplified data processing on large clusters[J].Communications of the ACM,2005,51(1):107-113.
[61] Pike R,Dorward S, Griesemer R,Quinlan S. Interpreting the data: Parallel analysis with Sawzall[J].Scientific Programming Journal,2005,13(4):277-298.
[62] Lamport L. Paxos made simple[J].ACM SIGACT News,2001,32(4):51-58.
[63] Barham P, Dragovic B, Fraser K, Hand S, Harris T, Ho A, Neugebaur R, Pratt I, Warfield A. Xen and the art of virtualization[C].In:Proc. of the 9th ACM Symp.On Operating Systems Principles. New York: Bolton Landing,2003:164-177.
[64] Smith JE, Nair R. Virtual Machines: Versatile Platforms for Systems and Processes. San Francisco[M]: Morgan Kaufmann Publishers,2005.
[65] Clark C, Fraser K, Hansen JG, Jul E, Pratt I, Warfield A. Live migration of virtual machines[C]. In: Proc. of the 2nd Symp. on Networked Systems Design and Implementation. Berkeley: USENIX Association,2005:273-286.
[66] Nelson M, Lim BH, Hutchins G. Fast transparent migration for virtual machines[C]. In: Proc. of the USENIX 2005 Annual Technical Conf. Berkeley: USENIX Association,2005:391-394.
[67] Isard M, Budiu M, Yu Y, Birrell A, Fetterly D. Dryad: Distributed data-parallel programs from sequential building blocks[C]. In: Proc.of the 2nd European Conf. on Computer Systems (EuroSys).,2007:59-72.
[68] DeCandia G, Hastorun D, Jampani M, Kakulapati G, Lakshman A, Pilchin A, Sivasubramanian S, Vosshall P, Vogels W. Dynamo:Amazon’s highly available key-value store[C]. In: Proc. of the 21st ACM Symp. on Operating Systems Principles. New York: ACMPress,2007:205-220.
[69] Chu LK, Tang H, Yang T, Shen K. Optimizing data aggregation for cluster-based Internet services. In: Proc. of the ACM SIGPLAN Symp[M]. On Principles and Practice of Parallel Programming. New York: ACM Press,2003:119-130.
[70] Yang HC, Dasdan A, Hsiao RL, Parker DS. Map-Reduce-Merge: Simplified relational data processing on large clusters[M].In: Proc.of the 2007 ACM SIGMOD Int’l Conf. on Management of Data. New York: ACM Press,2007:1029?1040.
[71]王鹏.走近云计算[M].北京:人民邮电出版社,2009.
[72] Peter Fingar.云计算:21世纪的商业平台[M].北京:电子工业出版社,2009.
[73]唐箭.云计算研究综述[J].内蒙古民族大学学报,2010,6(2):15-16.
[74]陈涛.云计算理论及技术研究[J].重庆交通大学学报(社科版),2009,9(4):104-106.
[75]关增产,吴清烈.大规模定制模式下的客户需求聚类分析与定制优化[J].统计与决策,2009(1):111-113.
[76]关增产.面向大规模定制的服务模块化研究[J].价值工程,2009,(11):99-103.
[77] B?Joseph Pine II.大规模定制:21世纪企业竞争的新前沿[M].人民大学出版社,2000,7.
[78] Fabrizio Salvador, Pablo Martin,De Holan, etal. Cracking thecode of mass customization[J].MIT Sloan Management Review,2009,50(3):70-78.
[79] Richard T G,Charles A W.Maximizing customer value via masscustomized e-consumer services[J].Business Horizons,2007(50):123-132.
[80]百度百科.云物流[EB/OL].[2010-10-30].http://baike.baidu.com/view/3304545.htm.
[81]物流论坛.“云物流”是网商物流瓶颈的解决办法[EB/OL].[2010-1-20]. http://www.tianya.cn/techforum/content/54/547833.shtml.
[82]徐颖.从“云物流”看第四方物流的价值贡献[J].商业流通,2011,3:16.
[83]童夏敏,胡尚杰.电子商务与现代物流的发展[J].中国商贸,2010,5:114-115.
[84]魏同涛.基于ABC的第三方物流企业成本核算方法的研究[D].昆明理工大学,2010.
[85]杨开宇.基于Mufti-Agent物流业务流程组合化建模仿真研究[J].武汉理工大学,2011.
[86] [86]IBM.云计算:随时随地访问IT资源[EB/OL].[2009-11-30]. http://www-01.ibm.com/software/cn/tivoli/solution/cloudcomputing.
[87]陈康,郑纬民.云计算:系统实例与研究现状[J].软件学报,2009,20(5):1337-1348.
[88]服务决定私有云配送模式的重要性[EB/OL].[2010-12-24]. http://www.dostor.com/article/2011/0629/6806881.shtml.
[89] Dantzig G B, Ramser J H. The truck dispatching problem[J]. Management Science,1959,4(6): 80-91.
[90]钟石泉,贺国光.有里程和时间窗约束的一体化车辆调度智能优化[J].系统过程与电子技术,2006,28(2):204-243.
[91]谢秉磊,李军.有时间窗的非满载车辆调度问题的遗传算法[J].系统工程学报,2000,15(3):290-294.
[92]张明善,唐小我.多车场满载货运车辆优化调度的网络流算法[J].系统工程学报,2002,17(3):216-220.
[93]经怀明,张立军.多车型车辆调度问题的建模与仿真[J].计算机仿真,2006,23(4):261-264.
[94] Gronalt M,Hartl RF,Reimann M.New savings based algorithms for time constrained pickup and delivery of full truckloads[J].European Journal of Operational Research,2003,757(3):520-35.
[95] K.C.Tan,Y.H.Chew,L.H.Lee.A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems[J].European Journal of Operational Research,2006(17) 2,855-885.
[96] Homberger J,Gehring H.Two-phase hybrid met heuristic for the vehicle routing problem with time windows[J].European Journal of Operational Research,2005(162):220-238.
[97] Fogel D E.Applying Evolutionary Programming to Selected TSPs[J].Cybem and syst:An Internation Journal,1993,24:27-36.
[98]李军,郭耀煌.物流配送车辆优化调度理论与方法[M].中国物资出版社,2001.
[99]雷,尹传忠.启发式方法确定最短配送路径[J].物流科技,2003,26(95):18-20.
[100]张丽萍,柴跃廷.车辆路径问题的改进遗传算法[J].系统工程理论与实践,2002,17 (8):79-84.
[101] Gendreau M, Laporte G, Seguin. An exact algorithm for the vehicle routing problem with stochastic demands and customers[J].Transportation Science,1995,29(2):14 3-155.
[102] Goldberg D E. Genetic algorithms in search, optimization and machine learning[M].Addison-Wesley press,1989.
[103]谢秉磊,郭耀煌,郭强.动态车辆路径问题:现状与展望[J].系统工程理论方法应用,2002, 11(2):116-121.
[104]张建勇,李军.模糊需求信息条件下的实时动态车辆调度问题研究[J].管理工程学报,2004, 18(4):69-73.
[105]宋远清,李永生,梁慎清.需求随机车辆调度问题的遗传算法研究[J].计算机技术与发展,2009, 19(2):230-233.
[106] Juliane Müller. Approximative solutions to the bicriterion Vehicle Routing Problem with Time Windows[J]. European Journal of Operational Research, 2010,20(2):223–231.
[107] Jǒrn Schǒnberger, Herbert Kopfer. Online decision making and automatic decision model adaptation[J].Computers & Operations Research 2009(36):1740-1750.
[108] Clara Novoa, Robert Storer. An approximate dynamic programming approach for the vehicle routing problem with stochastic demands[J].European Journal of Operational Research, 2009,19(6): 509-515.
[109] Rodrigo Moretti Branchini,ViníciusAmaralArmentano. Adaptive granular local search heuristic for a dynamic vehicle routing problem[J]. Computers & Operations Research,2009(36): 2955- 2968.
[110] Narayanan A, Moore M. Quantum-inspired genetic algorithm[C].IEEE Congress on Evolutionary Computation.Nogaya,1996:61-66.
[111] Han K H, Kim J H. Quant um2inspired evolutionary algorithm for a class of combinatorial optimization[J].IEEE Trans on Evolutionary Computation,2002,6(6):580-593.
[112] Hichem TALBI, Amer DRAA, Mohamed BATOUCHE. A New Quantum-Inspired Genetic Algorithm for Solving the Traveling Salesman Problem[C].IEEE International Conference on Industrial Technology,2004: 1192-1197.
[113]王凌.量子进化算法研究进展[J].控制与决策,2008,23(12):1321-1327.
[114]赵燕伟,彭典军,张景玲,吴斌.有能力约束车辆路径问题的量子进化算法[J].系统工程理论与实践,2009,29(2):159-167.
[115]吕军,冯博琴,李波.多约束条件车辆路径问题的两阶段遗传退火算法[J].西安交通大学学报,2005,39(12):1299-1302.
[116] Han KH, Park KH, Lee CH, et al. Parallel quantum-inspired genetic algorithm for combinatorial optimization problem[J]. IEEE Congress on Evolutionary Computation,2001:1422-1429.
[117]百度百科,时间轴概念. [EB/OL].[2011-3-19].http://baike.baidu.com/view/30505.htm.
[118] Narayanan A,Moore M. Quantum inspired genetic algorithm[C].Pro Of IEEE InternationalConference on Evolutionary Computation:IEEE Press,1996:61-66.
[119]杨淑芳,焦李成,刘芳.量子进化算法[J].工程数学学报,2006,23(3):235-246.
[120] Hey T. Quantum computing:An introduction[J].Computing&Control Engineering Journal,1999,10(3):105-112.
[121]施朝春,王旭,葛显龙.带有时间窗的多配送中心车辆调度问题研究[J].计算机工程与应用,2009,45(34):21-24.
[122]杨淑媛,刘芳.一种基于量子染色体的遗传算法[J].西安电子科技大学学报,2004,31(1): 76-81.
[123]邢文训,谢金星.现代优化计算方法[M].北京.清华大学出版社,2007.
[124] Sariklis D, Powell S. A heuristic method for the open vehicle routing problem[J]. Journal of the Operational Research Society,2000,51(5):564-573.
[125] Tarantilis C D, Kiranoudis C T. Distribution of fresh meat[J]. Journal of Food Engineering,2002,51(1):85-91.
[126] Yu Shiwei, Ding Chang, Zhu Kejun. A hybrid GA-TS algorithm for open vehicle routing optimization of coal mines material[J]. Expert Systems with Applications, 2011(38):10568-10573.
[127] MirHassani S.A., Abolghasemi N.. A particle swarm optimization algorithm for open vehicle routing problem[J]. Expert Systems with Applications, 2011(38):11547-11551.
[128] Emmanouil E. Zachariadis, Chris T. Kiranoudis. An open vehicle routing problem metaheuristic for examining wide solution neighborhoods[J].Computers&Operations Research,2010(37):712-723.
[129] Cao Erbao, Lai Mingyong. The open vehicle routing problem with fuzzy demands[J]. Expert Systems with Applications,2010,3(7):2405-2411.
[130] Repoussis P. P., Tarantilis C. D., Braysy O..A hybrid evolution strategy for the open vehicle routing problem[J].Computers &Operations Research,2010,8(7):443-455.
[131]符卓.带装载能力约束的开放式车辆路径问题及其紧急搜索算法研究[J].系统工程理论与实践,2004,3(3):123-128.
[132]王雪莲,汪波,钟石泉.一类半开放式车辆路径问题及其禁忌算法研究[J].系统仿真学报,2008,20(8):1969-1972.
[133]李延晖,刘向.沿途补货的多车场开放式车辆路径问题及蚁群算法[J].计算机集成制造系统,2008,14(3):557-562.
[134]徐天亮.运输与配送[M].北京.中国物资出版社,2002.
[135]汤浅和夫.物流管理入门[M].北京:中国铁道出版社,1986.
[136]王之泰.现代物流学[M].北京:北京物资学院出版社,1998.
[137]王之泰.现代物流管理[M].北京:中国工人出版社,2001.
[138]崔介何.物流学[M].北京:北京大学出版社,2003.
[139]何明坷.现代物流与配送中心:推动流通创新的趋势[M].北京:人民交通出版社,1997.
[140]石洪波,郎茂祥. JD多车型配送车辆调度问题的模型及其禁忌搜索算法研究[J].长沙交通学院学报,2005,21(3):73-77.
[141]李冰.多车型确定性动态车辆调配问题[J].管理工程学报,2006,20(3):52-56.
[142]陶胤强,牛惠民.带时间窗的多车型多费用车辆路径问题的模型和算法[J].交通运输系统工程与信息,2008,8(1):113-117.
[143]陈美军,张志胜等.多约束下车辆路径问题的蚁群算法研究[J].中国机械工程,2008,19(16):1939-1944.
[144]李建,张永等.第三方物流多车型硬时间窗路线问题研究[J].系统工程学报,2008,23(1):74-80.
[145]熊浩,胡列格.多车型动态车辆调度及其遗传算法[J].系统工程,2009,27(10):21-24.
[146] Dror M, Ball M. inventory-routing: reduction from an annul to a short-period problem[J].Naval logistics,1987,34:891-905.
[147]刘志硕,申金升,关伟.车辆路径问题的混合蚁群算法设计与实现[J].管理科学学报,2007,11(10):15-22.
[148] Brandao J. A tabu search algorithm for the heterogeneous fixed fleet vehicle routing problem[J]. Computers Operations Research,2008,38(1): 140-151.
[149]李德毅.不确定性人工智能[M].北京.国防工业出版社,2005.
[150] Holland J H.Adaptation in Natural and Artificial System[M].Ann Arbor: University of Michigan Press,1975.
[151]金晶,苏勇.一种改进的自适应遗传算法[J].计算机工程与应用,2005,41(18): 64-69.
[152] Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetic algorithm[J]. IEEE Trans on System,Man and Cybernetics,1994,24(4): 656-667.
[153] Smith J E.,Fogarty T C.Operator and parameter adaptation in genetic algorithms[J].Soft Computing,1997,1(2): 81-87.
[154] HERRERA F, LOZANO M. Adaptation of genetic algorithm parameters based on fuzzy logic controller[C]. HERRERA F, VERDEGAY J L. Genetic Algorithms and Soft Computing. Berlin, Germany:Springer-Verlag,1996: 95-125.
[155] HERRERA F, LOZANO M. Adaptive genetic operators based on convolutions with fuzzy behaviors[J]. IEEE Trans on Evolutionary computation,2001,5(2):149-165.
[156]李德毅,孟海军,史雪梅.隶属云和隶属云发生器[J].计算机研究与发展,1995(6): 15-20.