车辆路径问题的粒子群算法研究与应用
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摘要
物流被称为“第三利润源泉”,越来越受到人们的关注,日益成为国民经济的基础产业。运输是物流中的重要环节,占物流成本的60%以上。车辆路径问题主要研究物流配送中车辆线路优化以降低运输成本。该问题是运筹学和组合优化领域中的著名NP问题,在航班调度、列车编组等众多领域都有应用。由于NP问题求解的复杂性,目前车辆路径问题的求解方法主要使用各种智能优化算法。本文主要研究了以下四种模型的车辆路径问题:有能力约束的车辆路径问题,开放式车辆路径问题,基于客户满意度的开放式车辆路径问题,开放式动态网络车辆路径问题。研究了粒子群及其改进算法对上述模型的求解。具体的研究内容如下:
(1)首先介绍了论文的研究背景及意义,给出了车辆路径问题的定义,分析了车辆路径问题的组成要素。然后在对国内外大量文献总结提炼的基础上,从车辆路径问题的模型和求解算法两方面,深入分析了车辆路径问题的国内外研究现状。
(2)系统研究了基于粒子群算法的有能力约束车辆路径问题(CapacityVehicle Routing Problem,CVRP)。提出了整数编码、实数编码两种求解CVRP的方法。在整数编码中,以交换数为基础,对粒子的速度重新定义,并对速度的加、减等操作进行了定义,提出了“换位减”算子作为整数编码的速度计算方法;针对整数编码算法存在的问题,提出了一种实数编码方法求解CVRP,用实数的整数部分表示客户所在的车辆,小数部分表示在该车辆中配送的次序,融合遗传算法的思想,引入交叉算子以增加种群的多样性,详细讨论了粒子群算法的各个参数对算法结果的影响。为了与其他智能优化算法比较,研究了遗传算法、人工鱼群算法在CVRP中的应用。将双种群遗传算法用于CVRP的求解;提出了人工鱼群算法在CVRP中的应用,针对车辆路径问题的特点,定义了鱼群的距离、领域等概念,提出了人工鱼根据自身在鱼群中的排序,自适应选择移动算子的策略。
(3)通过引入虚拟配送中心的概念,建立了开放式车辆路径问题的三下标数学模型。提出了开放式车辆路径问题的粒子群求解方法,将最邻近插入、最远插入、2-Opt、3-Opt等启发式算法作为再优化过程引入粒子群算法,通过这些启发式算法调整线路内和线路间的客户来改进解,从理论上分析了这些算法的计算复杂度。通过实验分析,找出合适的启发式算子,并和其他的算法进行了比较。
(4)以客户满意度为首要优化目标,建立了基于客户满意度的开放式车辆路径问题的数学模型,使用梯形模糊数表示客户满意度。综合考虑距离、等待时间、客户的满意度等因素,定义了广义的距离和节约费用的概念,提出了改进的最邻近法和最廉价插入法,将这两个算法作为初始化和改进算子结合粒子群算法进行优化求解。分析了算法的复杂度,对算法的各个参数进行了讨论,通过实验仿真对这几种方法进行了分析比较。
(5)动态网络车辆路径问题目前研究的热点和难点问题,将动态网络与开放式两个因素结合起来研究车辆路径问题还未见报道。本文针建立了开放式动态网络车辆路径问题的数学模型,提出了一种连续时间依赖函数模型。提出了自适应惯性权重调整的粒子群算法,定义了粒子的“位置比”概念,充分利用粒子的已有知识,动态的调整惯性权重。在算法中,引入公告板策略,根据粒子适应度的高低分类更新粒子状态,对于优秀粒子使用一种新的状态更新公式,以使其跳出局部极值点。对于适应度低的粒子,通过统计其在公告板中出现的频率,用新的粒子替换以保持种群的多样性。通过实验讨论了算法的参数设置,对几种惯性权重方案进行了分析比较,实验结果证明了算法的有效性。
(6)在上述理论工作的基础上,针对第三方物流在国内的迅速发展,而相应的车辆调度软件功能不够完善,开发了智能车辆调度系统。该系统包括智能车辆调度、承运单的管理、电子地图的显示等功能。该系统可以处理有时间窗、有能力约束等多种情况的车辆调度问题,提供遗传算法、粒子群算法等多种优化算法供用户使用。系统在杭州某物流公司应用,取得了良好的效果。
最后,对全文研究工作进行了总结,展望了车辆路径问题的模型和算法研究的前景。
Logistics, which is regarded as "the third profit source", has obvious impact on economic activity of the world day by day, and cause people's more and more attention too. Transportation and delivery is the key part in the logistics, 60% of all the logistics cost. Vehicle routing problem (VRP), which is research on how to plan the vehicles routes in order to save the transportation cost, is the well known NP problem in the field of operation research and combination optimization. It has been applied to many fields, such as flight scheduling, train organizing. VPR have many models because of its many constraints, such as custom, network and vehicle type. For NP complexity, the polynomial method has not been found now, so most researchers concentrate on the research of heuristic method.
The dissertation mainly studied four VRP models: capacity vehicle routing Problem (CVRP), Open Vehicle Routing Problem (OVRP), the Customer Satisfaction Vehicle Routing Problem (CSVRP) and the Time Dependent Vehicle Routing Problem (TDVRP). The main contributions of the paper are as follows:
(1) First the research background and significance of the thesis is introduced, and the definition of vehicle routing problem is given. The composition elements of Vehicle routing problem is analyzed. Then based on summing up a large number of domestic and foreign literatures, survey the status of vehicle routing problem deeply from the model and algorithms. Open vehicle routing problem and time dependent vehicle routing problem as hot issues are discussed detailedly.
(2) The CVRP is deeply and systematically studied based on Particle Swarm Optimization. Two encoding methods: integer encoding and real number encoding are presented. In the integer number encoding, based on the "exchange number", the velocity of the particles is redefined, and various operator of velocity is defined. "Exchange minus operator" is constructed to compute particle's velocity. In the real number encoding, the vehicle is mapped into the integer part of the real number; and the sequence of customers in the vehicle is mapped into the decimal fraction of the real number. The crossover operator is inducted to improve the diversity of the population. The impact of various parameters for the result is discussed detailedly. For the sake of comparing with other intelligent algorithms, Genetic Algorithm and Artificial Fish School Algorithm are studied. Double Genetic Algorithm is presented for CVRP, and two populations is initialization, each has its probability of crossover and mutation, and the two populations exchange the better chromosome. It can break the balance of inter-population in the local minimization, increase the diversity of the population, escape the local minimization. For Artificial Fish School Algorithm, some definitions and rules when the AFS A algorithm is applied into combination optimization problem is presented. The operation rules of AFSA for VRP are presented, method of encoding, behavior evaluation and so on. The computational complexity of these algorihtms are analyzed and the parameters of algorithms are discussed through experiments.
(3) The mathematical model of Open Vehicle Routing Problem is founded, and Particle Swarm Optimization combined the global search and local search algorithms are presented. Several heurist methods are applied into the post-optimization procedure,such as 2-Opt,3-Opt, Nearest Insertion Algortihm. They are used to optimize the inner or outer routes and modify illegal solutions. The computational complexity of these algorihtms are analyzed . In the experiments, the performance of the proposed post-optimization algorithm is analyzed and the particle swarm optimization algorithm is compared with other heuristic methods.
(4) The multi-objective vehicle routing problem to optimize both customer satisfaction and the transportation cost is proposed. The customer satisfaction is denoted by trapezia fuzzy number. The improvement Nearest Neighbor Algorithm and Cheapest Insertion Algorithm are presented. The general cost is defined, which is included customer satisfaction, distance, waiting time and other factors. The two algorithms hybrid PSO is proposed to solve the problem. The complexity of the algorithm and the algorithm's parameters are discussed. Finally, in the experiments, these algorithms are analyzed and compared.
(5) Time dependent vehicle routing problem is more complex than other VRPs, because its travel cost changed with the time. In the paper, the mathematic model of the problem is founded, and a continuous function is presented for the time dependent network model. The auto-adapt parameter of PSO is presented. Each particle's parameterωcan auto-adapt regulate according to the fitness changing. At the same time, call-board policy is introduced in the algorithm, and the particle update its position according to the fitness. For the better particle, a new updating fomula is applied,and for the inactivity particle, they are displaced by the new particles. In the experiment, the parameters of the algorithm are discussed detailedly, and the result show the algorithm is efficiency.
(6)Based on the above the academic research, an intelligent vehicle routing system based on the 3rd party logistics industry is developed. The system include the following functions, such as vehicle scheduling, route planning, delivering order building, electronic map display, and basic information manager. The system supports the models of CVRP, VRPTW, and provides the genetic algorithm, particle swarm optimization and so on. The system has been applied in a 3rd party logistics company, and have favourable economic benefit.
(1)首先介绍了论文的研究背景及意义,给出了车辆路径问题的定义,分析了车辆路径问题的组成要素。然后在对国内外大量文献总结提炼的基础上,从车辆路径问题的模型和求解算法两方面,深入分析了车辆路径问题的国内外研究现状。
(2)系统研究了基于粒子群算法的有能力约束车辆路径问题(CapacityVehicle Routing Problem,CVRP)。提出了整数编码、实数编码两种求解CVRP的方法。在整数编码中,以交换数为基础,对粒子的速度重新定义,并对速度的加、减等操作进行了定义,提出了“换位减”算子作为整数编码的速度计算方法;针对整数编码算法存在的问题,提出了一种实数编码方法求解CVRP,用实数的整数部分表示客户所在的车辆,小数部分表示在该车辆中配送的次序,融合遗传算法的思想,引入交叉算子以增加种群的多样性,详细讨论了粒子群算法的各个参数对算法结果的影响。为了与其他智能优化算法比较,研究了遗传算法、人工鱼群算法在CVRP中的应用。将双种群遗传算法用于CVRP的求解;提出了人工鱼群算法在CVRP中的应用,针对车辆路径问题的特点,定义了鱼群的距离、领域等概念,提出了人工鱼根据自身在鱼群中的排序,自适应选择移动算子的策略。
(3)通过引入虚拟配送中心的概念,建立了开放式车辆路径问题的三下标数学模型。提出了开放式车辆路径问题的粒子群求解方法,将最邻近插入、最远插入、2-Opt、3-Opt等启发式算法作为再优化过程引入粒子群算法,通过这些启发式算法调整线路内和线路间的客户来改进解,从理论上分析了这些算法的计算复杂度。通过实验分析,找出合适的启发式算子,并和其他的算法进行了比较。
(4)以客户满意度为首要优化目标,建立了基于客户满意度的开放式车辆路径问题的数学模型,使用梯形模糊数表示客户满意度。综合考虑距离、等待时间、客户的满意度等因素,定义了广义的距离和节约费用的概念,提出了改进的最邻近法和最廉价插入法,将这两个算法作为初始化和改进算子结合粒子群算法进行优化求解。分析了算法的复杂度,对算法的各个参数进行了讨论,通过实验仿真对这几种方法进行了分析比较。
(5)动态网络车辆路径问题目前研究的热点和难点问题,将动态网络与开放式两个因素结合起来研究车辆路径问题还未见报道。本文针建立了开放式动态网络车辆路径问题的数学模型,提出了一种连续时间依赖函数模型。提出了自适应惯性权重调整的粒子群算法,定义了粒子的“位置比”概念,充分利用粒子的已有知识,动态的调整惯性权重。在算法中,引入公告板策略,根据粒子适应度的高低分类更新粒子状态,对于优秀粒子使用一种新的状态更新公式,以使其跳出局部极值点。对于适应度低的粒子,通过统计其在公告板中出现的频率,用新的粒子替换以保持种群的多样性。通过实验讨论了算法的参数设置,对几种惯性权重方案进行了分析比较,实验结果证明了算法的有效性。
(6)在上述理论工作的基础上,针对第三方物流在国内的迅速发展,而相应的车辆调度软件功能不够完善,开发了智能车辆调度系统。该系统包括智能车辆调度、承运单的管理、电子地图的显示等功能。该系统可以处理有时间窗、有能力约束等多种情况的车辆调度问题,提供遗传算法、粒子群算法等多种优化算法供用户使用。系统在杭州某物流公司应用,取得了良好的效果。
最后,对全文研究工作进行了总结,展望了车辆路径问题的模型和算法研究的前景。
Logistics, which is regarded as "the third profit source", has obvious impact on economic activity of the world day by day, and cause people's more and more attention too. Transportation and delivery is the key part in the logistics, 60% of all the logistics cost. Vehicle routing problem (VRP), which is research on how to plan the vehicles routes in order to save the transportation cost, is the well known NP problem in the field of operation research and combination optimization. It has been applied to many fields, such as flight scheduling, train organizing. VPR have many models because of its many constraints, such as custom, network and vehicle type. For NP complexity, the polynomial method has not been found now, so most researchers concentrate on the research of heuristic method.
The dissertation mainly studied four VRP models: capacity vehicle routing Problem (CVRP), Open Vehicle Routing Problem (OVRP), the Customer Satisfaction Vehicle Routing Problem (CSVRP) and the Time Dependent Vehicle Routing Problem (TDVRP). The main contributions of the paper are as follows:
(1) First the research background and significance of the thesis is introduced, and the definition of vehicle routing problem is given. The composition elements of Vehicle routing problem is analyzed. Then based on summing up a large number of domestic and foreign literatures, survey the status of vehicle routing problem deeply from the model and algorithms. Open vehicle routing problem and time dependent vehicle routing problem as hot issues are discussed detailedly.
(2) The CVRP is deeply and systematically studied based on Particle Swarm Optimization. Two encoding methods: integer encoding and real number encoding are presented. In the integer number encoding, based on the "exchange number", the velocity of the particles is redefined, and various operator of velocity is defined. "Exchange minus operator" is constructed to compute particle's velocity. In the real number encoding, the vehicle is mapped into the integer part of the real number; and the sequence of customers in the vehicle is mapped into the decimal fraction of the real number. The crossover operator is inducted to improve the diversity of the population. The impact of various parameters for the result is discussed detailedly. For the sake of comparing with other intelligent algorithms, Genetic Algorithm and Artificial Fish School Algorithm are studied. Double Genetic Algorithm is presented for CVRP, and two populations is initialization, each has its probability of crossover and mutation, and the two populations exchange the better chromosome. It can break the balance of inter-population in the local minimization, increase the diversity of the population, escape the local minimization. For Artificial Fish School Algorithm, some definitions and rules when the AFS A algorithm is applied into combination optimization problem is presented. The operation rules of AFSA for VRP are presented, method of encoding, behavior evaluation and so on. The computational complexity of these algorihtms are analyzed and the parameters of algorithms are discussed through experiments.
(3) The mathematical model of Open Vehicle Routing Problem is founded, and Particle Swarm Optimization combined the global search and local search algorithms are presented. Several heurist methods are applied into the post-optimization procedure,such as 2-Opt,3-Opt, Nearest Insertion Algortihm. They are used to optimize the inner or outer routes and modify illegal solutions. The computational complexity of these algorihtms are analyzed . In the experiments, the performance of the proposed post-optimization algorithm is analyzed and the particle swarm optimization algorithm is compared with other heuristic methods.
(4) The multi-objective vehicle routing problem to optimize both customer satisfaction and the transportation cost is proposed. The customer satisfaction is denoted by trapezia fuzzy number. The improvement Nearest Neighbor Algorithm and Cheapest Insertion Algorithm are presented. The general cost is defined, which is included customer satisfaction, distance, waiting time and other factors. The two algorithms hybrid PSO is proposed to solve the problem. The complexity of the algorithm and the algorithm's parameters are discussed. Finally, in the experiments, these algorithms are analyzed and compared.
(5) Time dependent vehicle routing problem is more complex than other VRPs, because its travel cost changed with the time. In the paper, the mathematic model of the problem is founded, and a continuous function is presented for the time dependent network model. The auto-adapt parameter of PSO is presented. Each particle's parameterωcan auto-adapt regulate according to the fitness changing. At the same time, call-board policy is introduced in the algorithm, and the particle update its position according to the fitness. For the better particle, a new updating fomula is applied,and for the inactivity particle, they are displaced by the new particles. In the experiment, the parameters of the algorithm are discussed detailedly, and the result show the algorithm is efficiency.
(6)Based on the above the academic research, an intelligent vehicle routing system based on the 3rd party logistics industry is developed. The system include the following functions, such as vehicle scheduling, route planning, delivering order building, electronic map display, and basic information manager. The system supports the models of CVRP, VRPTW, and provides the genetic algorithm, particle swarm optimization and so on. The system has been applied in a 3rd party logistics company, and have favourable economic benefit.
引文
1. Chen, J.F., Wu T.H.Vehicle routing problem with simultaneous deliveries and pickups [J], Journal of the Operational Research Society. 2006, Vol.57, No.5, 579-587.
2. Ahmad Alshamrania, Kamlesh Mathurb, and Ronald H.Ballou. Reverse logistics: simultaneous design of delivery routes and returns strategies [J]. Computers &Operations Research 2007, vol.34, No.2, 595-619.
3. Lu Quan, Dessouky, Maged M. A new insertion-based construction heuristic for solving the pickup and delivery problem with time windows [J]. European Journal of Operational Research, 2006, Vol.175, No.2,672-687.
4. I.M.Chao, B.L.Golden, E.A.Wasil. An improved heuristic for the period vehicle routing problem [J]. Networks, 1995, Vol .26,25-44.
5. Angelelli E. and Speranza M.G... The periodic vehicle routing problem with intermediate facilities [J]. European Journal of Operational Research, 2002, Vol.137, No.2,233-247.
6. Francis, Peter Smilowitz, Karen.Modeling techniques for periodic vehicle routing problems [J] .Transportation Research: Part B, 2006, Vol.40, No10, 872-884.
7. Mingzhou Jin, Kai Liu, Royce O.Bowden. A two-stage algorithm with valid inequalities for the split delivery vehicle routing problem [J]. International Journal of Production Economics, 2006. Vol. 105, No. 1,228-242.
8. Archetti C, Speranza M, Hertz A. A Tabu Search Algorithm for the Split Delivery Vehicle Routing Problem [J]. Transportation Science, 2006, Vol. 40 No1, 64-73.
9. F.J.Arcelus, H.A.Eiselt and Edward Y.H.Lin. Multi-depot vehicle scheduling problems with time windows and waiting costs [J].International Transactions in Operational Research, 1998, Vol.5, No.3,201-210.
10. Benoit Crevier, Jean-Francois Cordeau and Gilbert Laporte. The multi-depot vehicle routing problem with inter-depot routes [J]. European Journal of Operational Research, 2007, Vol.176, No.2, 756-773.
铩?1.Lim,Andrew,Fan Wang.Multi-Depot Vehicle Routing Problem:A One-StageApproach[J].IEEE Transactions on Automation Science & Engineering,2005,Vol.2,No4,397-402.
12.Golden B,Assad A,Levy L.The fleet size and mix vehicle routing problem[J].Computers and Operations Research,1984,Vol.11:49-66.
13.Eunjeong Choia,Dong-Wan Tchab.A column generation approach to theheterogeneous fleet vehicle routing problem[J].Computers & OperationsResearch.2005,online.
14.Tarantilis,Kiranoudis,Vassiliadis.A threshold accepting metaheuristic for theheterogeneous fixed fleet vehicle routing problem[J].European Journal ofOperational Research,2004,Vol.152,No.1,148-158.
15.谢秉磊.随机车辆路径问题研究[D].西南交通大学博士学位论文,2003.
16.Laporte G.,Louveaux EV.An integer L-shaped algorithm for the capacitatedvehicle routing problem with stochastic demands[J].Operations Research,2002,Vol.3,415-423.
17.Laporte G.,Louveaux F.V.,Mereure H..The vehicle routing problem withstochastic travel times[J].Transportation Science,1992,Vol.26,161-170.
18.Bent Russell W.,Van Hentenryck.Scenario-Based Planning for Partially DynamicVehicle Routing with Stochastic Customers[J].Operations Research,2004,Vol.52,No.6,977-987.
19.Teodorovic.D et.al.The fuzzy set approach to the vehicle routing problem whendemand at nodes uncertain[J].Fuzzy sets and system,1996,Vol.82,307-317.
20.祝崇隽,刘民等.针对模糊需求的VRP的两种2-OPT算法[J].电子学报,2001,Vol.29,No.8,1035-1037.
21.Malandraki C.,Daskin MS.Time dependent vehicle routing problems:formulations,properties and heuristic algorithms[J].TransportationScience,1992,Vol.26,No.3,185-200.
22.MalandrakiC,Dial.RB.A restriced dynamic programming heuristic algorithm forthe time dependent traveling salesman problem[J].European Journal ofOperational Research,1996,Vol 90,No.1,45-50.
23. Ichoua S, Gendreau M, Potvin J-Y. Vehicle dispatching with time-dependent travel times [J]. European Journal of Operational Research, 2003, Vol.144, No.2, 379-396.
24. Richard Eglese, Will Maden, Alan Slater. A Road Timetable? to aid Vehicle Routing and scheduling [J]. Computers& Operations Research 2006, Vol.33, No.12, 3508-3519.
25. Johan Oppen, Arne. Arc routing in a node routing environment [J]. Computers & Operations Research, Vol33, No.4,2006,1033-1055.
26. Psaraftis H N. Dynamic Vehicle Routing Problem. In: Golden.B.L, Assad.A, eds. Vehicle Routing: Methods and Studies [M].Amsterdam NorthHolland, 1988. 223-248.
27. Psaraftis H.N.. Dynamic vehicle routing: status and prospects [J].Annals of Operations Research, 1995, Vol.61,143-164.
28. Ali Haghani and Soojung Jung. A dynamic vehicle routing problem with time-dependent travel times [J]. Computers & Operations Research, 2005, Vol.32, No.11, 2959-2986.
29. Eiichi Taniguchi and Hiroshi Shimamoto. Intelligent transportation system based dynamic vehicle routing and scheduling with variable travel times [J]. Transportation Research Part C: Emerging Technologies, 2004, Vol.12, No.3, 235-250.
30. Fuh-hwa Franklin Liu, Sheng-yuan Shen. An overview of a heuristic for vehicle routing problem with time windows [J]. Computers & Industrial Engineering, 1999, Vol.37, No.1, 331-334.
31. Olli Braysy, Michel Gendreau, Vehicle Routing Problem with Time Windows Part I: Route Construction and Local Search Algorithms [J]. Transportation Science, 2005, Vol.39, No.1, 104-118.
32. Olli Braysy, Michel Gendreau. Vehicle Routing Problem with Time Windows Part II: Metaheuristics [J]. Transportation Science, 2005, Vol.39, No.1, 119-139.
33. Gilbert Laportea, Michel Gendreaub, Jean-Yves Potvinb, et. al. Classical and modern heuristics for the vehicle routing problem [J]. Internal Transaction in Operations Research, 2000, Vol.7, 285-300.
34. Garcia, B.L., J.Y.Potvin, J.M.Rousseau. A parallel implementation of the tabu search heuristic for vehicle routing problems with time window constraints [J]. Computers & Operations Research, 1994, Vol. 21,1025-1033.
35. Cordeau, J.F., GLaporte, A.Mercier. A Unified Tabu Search Heuristic for Vehicle Routing Problems with Time Windows [J]. Journal of the Operational Research Society, 2001, Vol.52, 928-936.
36. Lau, H.C., M.Sim, K.M.Teo. Vehicle routing problem with time windows and a limited number of vehicles [J]. European Journal of Operational Research, 2003, Vol. 148, No.3, 559-568.
37. Jose Brandao. A new tabu search algorithm for the vehicle routing problem with backhauls [J]. European Journal of Operational Research, 2006, Vol.173, No.2, 540-555.
38. Taillard,E., P.Badeau, M.Gendreau et.al. A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows [J]. Transportation Science, 1997, Vol.31,170-186.
39. Rochat, Y.and E.Taillard. Probabilistic Diversification and Intensification in Local Search for Vehicle Routing [J]. Journal of Heuristics, 1995, Vol.1,147-167.
40. Homberger.Jorg, Gehring, Hermann. A two-phase hybrid metaheuristic for the vehicle routing problem with time windows [J]. European Journal of Operational Research, 2005, Vol.162 No.1, 220-238.
41. J.Y.Povtin, T. Kervahut. The Vehicle Routing Problem with Time Windows Part I: Tabu Search [J]. INFORMS Journal on Computing, 1996, Vol.8, No.2,158-164.
42. Michel Gendreau, Gilbert Laporte, Christophe Musaraganyi, et.al. A tabu search heuristic for the heterogeneous fleet vehicle routing problem [J]. Computers & Operations Research ,1999,Vol.26,No.12,1153-1173
43. Fermin Alfredo, Roberto Dieguez. A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service [J]. Computers & Operations Research, 2006, Vol.33, No.3, 595-619.
44. Jose Brandao. A tabu search algorithm for the open vehicle routing problem [J]. European Journal of Operational Research, 2004, Vol.157, No.3, 552-564.
45. Dag Haugland,Sin C.Ho,Gilbert Laporte.Designing delivery districts for the vehicle routing problem with stochastic demands[J].European Journal of Operational Research, 2007, Vol.180,No.3,997-1010.
46. Thangiah S.R., K.E.Nygard, P.L.Juell. GIDEON: A genetic algorithm system for vehicle routing with time windows [A]. Proc.7th IEEE Conf. Artificial Intelligence Application[C], Los Alamitos, IEEE Computer Society Press, 1991, 322-328.
47. Berger J., M. Salois and R. Begin. A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows [A], AI'98 Advances in Artificial Intelligence ,Lecture Notes in Artificial Intelligence 1418[C], Vancouver,Springer, 1998,114-127.
48. Potvin, J.Y. and S.Bengio. The Vehicle Routing Problem with Time Windows Part II: Genetic Search [J]. INFORMS Journal on Computing, 1996, Vol.8,165-172.
49. Gehring, H.and J.Homberger. Parallelization of a Two-Phase Metaheuristic for Routing Problems with Time Windows [J]. Journal of Heuristics, 2002, Vol.8, 251-276.
50. Berger, J., M.Barkaoui, O.Braysy. A route-directed hybrid genetic approach for the vehicle routing problem with time windows [J]. Information Systems Operational Research 2003, Vol.41,179-194.
51. Jung, S., B.R.Moon. A hybrid genetic algorithm for the vehicle routing problem with time windows [A]. Proceding Genetic Evolutionary Computing Conference[C], San Francisco, Morgan Kaufmann Publishers, 2002,1309-1316.
52. Bouthillier, G.Crainic. A cooperative parallel metaheuristic for vehicle routing with time windows [J]. Computer & Operation Research, 2005,V61.32, No.7,1685-1708.
53. Enrique Alba and Bernabe Dorronsoro. Computing nine new best-so-far solutions for Capacitated VRP with a cellular Genetic Algorithm [J]. Information Processing Letters, 2006, Vol 98, No 6,225-230.
54. C.M.R.R. Lima, M.C. Goldbarg and E.F.G. Goldbarg. A Memetic Algorithm for the Heterogeneous Fleet Vehicle Routing Problem [J]. Electronic Notes in Discrete Mathematics, 2004, Vol.18,171-176.
55. Jean Berger and Mohamed Barkaoui.A parallel hybrid genetic algorithm for the vehicle routing problem with time windows [J]. Computers & Operations Research, 2004, Vol.31, No.12,2037-2053.
56. Christian Prins.A simple and effective evolutionary algorithm for the vehicle routing problem [J]. Computers & Operations Research, 2004, Vol.31, No.12, 1985-2002.
57. M.Gendrau, G.Laporte, and J.Y.Potvin. Vehicle routing: Modern Heuristics [A].In E.Aarts and J.K.Lenstra, editors, Local Search in Combinatorial Optimization[C], Wiley and Sons, 1997, 311-336.
58. P.Tian, J.Ma, and D.M.Zhang. Application of the simulated annealing algorithm to the combinatorial optimization problem with permutation property: an investigation of generation mechanism [J]. European Journal of Operational Research, 1999, Vol.118, No.1, 81-94.
59. Li, H., A.Lim, J.Huang. Local search with annealing-like restarts to solve the VRPTW [J]. European Journal of Operational Research, 2003, Vol.150, No.1, 115-127.
60. Bent, R., P. Van Hentenryck. A two-stage hybrid local search for the vehicle routing problem with time windows [J]. Transportation Science, 2004, Vol.38, No.4, 515-530.
61. Czech, Z., P.Czarnas. Parallel simulated annealing for the vehicle routing problem with time windows [A]. Proc. 10~(th) European Workshop Parallel Distributed Network-Based Processing[C], Canary Islands, Spain, 2002,376-383.
62. R.Tavakkoli-Moghaddam, N.Safaei, Y.Gholipour. A hybrid simulated annealing for capacitated vehicle routing problems with the independent route length [J]. Applied Mathematics and Computation, 2006, Vol.176, No.2,445-454.
63. B.Bullnheimer, R.F.Hartl, and C.Strauss. Applying the ant system to the vehicle routing problem [A]. IN I. H. Osman S., S.Martello and C. Roucairol, editors, Meta- Heuristics: Advances and Trends in Local Search Paradigms for Optimization[C],KluwerAcadenics, 1998. 109-120.
64. B.Bullnheimer, R.F.Hartl, and C.Struss. An improved ant system algorithm for the vehicle routing problem [J]. Annals of Operations Research, 1999, Vol.89, 319-328.
铩?5.John E.Bella,Patrick R.McMullen.Ant colony optimization techniques for thevehicle routing problem[J].Advanced Engineering Informatics,2004,Vol.18,No.1,41-48.
66.Marc Reimann,Karl Doemer,Richard F.Hartl.D-Ants:Savings Based Ants divideand conquer the vehicle routing problem[J].Computers & Operations Research,2004,Vol.31,No.4,563-591.
67.Doerner KF,Gronalt M,Hartl RF,ea.al.Savings Ants for the vehicle routingproblem[A].In:Cagnoni S et al.,editors.Applications of evolutionary computing[C].Berlin:Springer,2002.11-20.
68.L.M.Gambardella,E.Taillard,and G.Agazzi.MACS-VRPTW:a multiple ant colonysystem for vehicle routing problems with time windows[A].D.Come et.al.Editors.New ideas in optimization[C].London,U.K.:McGraw-Hill,1999,63-76.
69.A.V.Donati,R.Montemanni,N.Casagrande,A.E.Rizzoli,and L.M.Gambardella.Time dependent vehicle routing problem with a multi ant colony system.TechnicalReport TR-17-03,IDSIA,Galleria 2,Manno,6928,Switzerland,2003.
70.R.Montemanni,L.M.Gambardella,A.E.Rizzoli,and A.V.Donati.A newalgorithm for a dynamic vehicle routing problem based on ant colony system.Technical Report TR-23-02,IDSIA,Galleria 2,Manno,6928,Switzerland,2004.
71.李宁,邹丹,孙德宝.车辆路径问题的粒子群算法研究[J].系统工程学报,2004,Vol.19,No.6,596-600.
72.李宁,邹彤,孙德宝.带时间窗车辆路径问题的粒子群算法[J].系统工程理论与实践,2004,Vol.24,No.4,130-135.
73.肖健梅,李军军,王锡淮.求解车辆路径问题的改进微粒群优化算法[J].计算机集成制造系统-CIMS,2005,Vol.11,No.4,577-581.
74.肖健梅,黄有方,李军军,王锡淮.基于离散微粒群优化的物流配送车辆路径问题[J].系统工程,2005,No 4,Vol.23,97-100.
75.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,Vol.22,No.8,79-84.
76.肖鹏,李茂军,张军平.车辆路径问题的单亲遗传算法[J].计算技术与自动化,2000,Vol.19,No.1,26-30.
铩?7.Potvin,J,Dube,D.and Robillard,C.Hybrid Approach to Vehicle Routing UsingNeural Networks and Genetic Algorithm[J].Applied Intelligence,1996,Vol.6,No.3,241-252.
78.Barrie M.Baker and M.A.Ayechew.A genetic algorithm for the vehicle routingproblem[J].Computers & Operations Research,2003,Vol 30,No 5,P787-800.
79.In-Chan Choi,Seong-In Kim and Hak-Soo Kim.A genetic algorithm with a mixedregion search for the asymmetric traveling salesman problem[J].Computers &Operations Research,2003,Vol30,No 5,773-786.
80.Jean Berger and Mohamed Barkaoui.A parallel hybrid genetic algorithm for thevehicle routing problem with time windows[J].Computers & Operations Research,2004,Vol.31,No.12,2037-2053.
81.姜大立,杨西龙.车辆路径问题的遗传算法研究[J].系统工程理论与实践.1999,Vol.19,No.6,44-45.
82.David Mester and Olli Braysy.Active-guided evolution strategies for large-scalecapacitated vehicle routing problems[J].Computers & Operations Research,2007,Vol34,No.10,2964-2975.
83.Mariam Tagmouti,Michel Gendreau,Jean-Yves Potvin.Arc routing problems withtime-dependent service costs[J].European Journal of Operational Research,2007,Vol.181,No.1,30-39
84.Feiyue Li,Bruce Golden,Edward Wasil.A record-to-record travel algorithm forsolving the heterogeneous fleet vehicle routing problem[J],Computers &Operations Research,2007,Vol34,2734-2742
85.李晓磊.一种新型的智能优化方法--人工鱼群算法[D].浙江大学博士学位论文,2003.
86.李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002.Vol.22,No.11,32-38.
87.Jeffrey Dean.Animats and what they can tell us[J].Trends in cognitivesciences.1998,Vol.2 No.1,60-67.
88.李晓磊,钱积新.基于分解协调的人工鱼群优化算法研究[J].电路与系统学报,2003,Vol.8,No.1:1-6.
铩?9.李晓磊,薛云灿,路飞,田国会.基于人工鱼群算法的参数估计方法[J].山东大学学报(工学版),2004,Vol 34,No.3,84-87.
90.李晓磊,冯少辉,钱积新等.基于人工鱼群算法的鲁棒PID控制器参数整定方法研究[J].信息与控制,2004,Vol.33,No.1,112-115.
91.李晓磊,路飞,田国会,钱积新,组合优化问题的人工鱼群算法应用[J].山东大学学报(工学版),2004,Vol.34,No.5,64-67.
92.马建伟,张国立,谢宏等.利用人工鱼群算法优化前向神经网络[J].计算机应用,2004,Vol.24,No.10,21-23.
93.黄光球,苏锦旗.基于人工鱼群算法的高级综合生产计划优化研究[J].微机发展,2005,Vol.15,No.10,49-55.
94.黄光球,陆秋琴,郑彦全.基于鱼群算法的油田多级站定位优化方法研究[J].西安石油大学学报(自然科学版),2006,Vol.21,No.4,98-102.
95.刘耀年,庞松岭,刘岱.基于人工鱼群算法神经网络的电力系统短期负荷预测[J].电工电能新技术,2005,Vol.24,No.4,5-8.
96.李亮,迟世春,林皋.禁忌鱼群算法及其在边坡稳定分析中的应用[J].工程力学,2006.Vol.23.No.3.6-10.
97.Kennedy,J.and Eberhart,R.C.Particle swarm optimization[A],Proceedings ofIEEE International Conference on Neural Networks[C],Piscataway,NJ.1995,1942-1948.
98.薛明志.进化计算与小波分析若干问题研究[D].西安电子科技大学博士学位论文,2004.
99.彭宇.群智能优化算法及理论研究[D].哈尔滨工业大学博士学位论文,2004
100.谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策,2003,Vol.18,No.2,129-134.
101.李宁,付国江,库少平等.粒子群优化算法的发展与展望[J].武汉理工大学学报,2005,Vol 27,No2,26-29.
102.夏桂梅,曾建潮.微粒群算法的研究现状及发展趋势[J].山西师范大学学报,2005,Vol.19,No.1,23-25.
103.陈国初,俞金寿.微粒群优化算法[J].信息与控制,2005,Vol.34,No.3,318-324.
104.张燕,汪镭,康琦等.微粒群优化算法及其改进形式综述[J].计算机工程与应 用,2005,No.2,1-4.
铩?05.刘波,王凌,金以慧,黄德先,微粒群优化算法研究进展[J].化工自动化及仪表,2005,Vol.32,No.3,1-6.
106.杨维,李歧强.粒子群优化算法综述[J].中国工程科学,2004,Vol.6,No.5,87-94
107.Kennedy,J.and Eberhart,R.C.Discrete binary version of the particle swarmalgorithm[A].Proceeding of the IEEE International Conference on Systems[C],Man,Cybernetics,Orlando,IEEE Press,1997,4104-4108.
108.Clerc,Maurice,Discrete Particle Swarm Optimization[M].New OptimizationTechniques in Engineering,Springer Verlag,2004,219-240.
109.Ayed Salmen,Imtiza Ahmad,Sabah AI-Madani,Particle Swarm Optimization fortask assignment problem[J],Microprocessor and Microsystems,2002,Vol.26,No.8,363-371.
110.夏蔚军,吴智铭,张伟等.微粒群优化在Job-shop调度中的应用[J].上海交通大学学报,2005,Vol.39,No.3,381-385.
111.Wang Wanliang,Wu Bin,et.al.Particle Swarm Optimization for Open VehicleRouting Problem[J].Lecture Notes in Artificial Intelligence,2006,No.4114,999-1007.
112.Zhang H,Li XD,Li H,et al.Particle swarm optimization-based schemes forresource-constrained project scheduling[J],Automation in Consctuction,2005,Vol.14,No.3,393-404.
113.Kennedy J.Small worlds and mega-minds:effects of neighborhood topology onparticle swarm performance[A].Proc.1999 IEEE Congress EvolutionaryComputation[C].Piscataway,NJ:IEEE Press,1999:1931-1938.
114.Kennedy,J.Improving Particle Swarm Performance with Cluster Analysis[A].Proceeding of the IEEE Conference on Evolutionary Computation[C],California:IEEE,2000,1507-1512.
115.Hu X,Eberhart R.C,Shi Y.Engineering Optimization with Particle Swarm[A].IEEE Swarm Intelligence Symposium[C],Indianapolis,USA,2003:53-57.
116.Suganthan P N.Particle Swarm Optimiser with Neighborhood Operator[A].Proceeding of the IEEE Congress of Evolutionary Computation[C],IEEE Piscataway 1999,1958-1962.
117.Mohais, AS, Mendes, R, Ward, C, et al. Neighborhood re-structuring in particle swarm optimization [J]. Lecture Notes in Artificial Intelligent, 2005, Vol.3809, 776-785.
118.Thiemo K, Jakob S. Particle Swarm Optimization with Spatial Particle Extension [A]. Proceedings of the Congress on Evolutionary Computation[C], Honolulu, HI: IEEE, 2002,1474-1479.
119.Clerc, M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization [A]. Proceeding of Congress on Evolutionary Computation[C], Washington DC. IEEE, Piscataway, 1999,1215-1218.
120.Xie X F, Zhang W J, Yand Z L. Adaptive Particle Swarm Optimization on Individual Level [A],the 6th Congress on Evolutionary Computation[C], Honolulu, HI:IEEE,2002,1456-1461.
121.Xie X F, Zhang W J, Yand Z L. Dissipative particle swarm optimization [A], the 6~(th) Congress on Evolutionary Computation[C], Honolulu, HI: IEEE,2002,1456-1461.
122.Xie X F, Zhang W J, Yand Z L. Hybrid Particle Swarm Optimization with Mass Extinction[A], IEEE International Conference on Communications, Circuits and Systems and West Sino Expositions[C], Chengdu: IEEE, 2002,1170-1173.
123.Al-Kazemi B, Mohan C K. Multi-phase generalization of the particle swarm optimization algorithm [A], Proceedings of Congress on Evolutionary Computation[C], Honolulu, HI:IEEE, 2002,489-494.
124. Van den Bergh, F. and Engelbrecht, A. P. A new locally convergent particle swarm optimizer [A]. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics[C], 2002,96-101.
125.Zhihua Cui, Jianchao Zeng. A Guaranteed Global Convergence Particle Swarm Optimizer [J], Lecture Notes in Computer Science, 2004,Vol.3066, 762-767.
126.Rodriguez A, Reggia J.A .Extending Self-organizing Particle Systems to Problem Solving [J]. Artificial Life, 2004, Vol.10, No.4,379-395.
127.B.Zhao, C.X. Guo. An improved particle swarm optimization algorithm for unit commitment [J].International Journal of Electrical Power & Energy Systems, Vol. 28, No.7,2006,482-490.
128.Buthainah, A Multi-phase Particle Swarm Optimization [D], Italia Syracuse University, 2002.
129.Xia-Ting Feng, Bing-Rui Chen, et.al. Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm [J].International Journal of Rock Mechanics and Mining Sciences, 2006, Vol.43, No.5,789-801.
130.Meng HY, Zhang XH, Liu SY.A co-evolutionary particle swarm optimization-based method for multiobjective optimization[A] ,Advances Artificial Intelligence[C], Lecture Notes in Artificial Intelligence, 2005,Vol .3809: 349-359.
131.De D, Ray S, Konar A, Chatterjee. An evolutionary SPDE breeding-based hybrid particle swarm optimizer: Application in coordination of robot ants for camera coverage area optimization [A], Pattern Recognition and Machine Intelligence[C], Lecture Notes in Computer Science, 2005, Vol .3776,413-416.
132.Li XD .Adaptively choosing neighborhood bests using species in a particle swarm optimizer for multimodal function optimization[A] ,Genetic and Evolutionary Computation[C], Lecture Notes in Computer Science ,2004,Vol.3102,105-116.
133.Ben Niu, Yunlong Zhu, Xiaoxian He, et al. Multi-population cooperative particle swarm optimization[A], Advances in Artificial Life[C]. Lecture Notes in Computer Science 2005, Vol.3630, 874-883.
134 Jiang Chuanwen, Etorre Bompard. A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization [J]. Mathematics and Computers in Simulation, 2005, Vol.68, No.1, 57-65.
135.L0vbjerg, M., Rasmussen, T.K., Krink, T.. Hybrid particle swarm optimizer with breeding and subpopulations [A]. Proceedings of the Third Genetic and Evolution Composition Conference[C], Morgan Kaufman, 2001, Vol.1,469-476.
136.Grosan C, Abraham A, Han SY, Gelbukh A.Hybrid particle swarm evolutionary algorithm for search and optimization[A], Advance in Artificial intelligence[C], Lecture Notes in Artificial Inteligence,2005, Vol.3789, 623-632.
137.Zhang J, Zhang JR, Li K. A sequential niching technique for particle swarm optimization[A],Advance in Intelligent Computing[C],Lecture Notes in Computer
Science,2005,Vol.3644,390-399.
铩?38.Liang JJ,Qin AK,Suganthan PN,Baskar S.Evaluation of comprehensive learningparticle swarm optimizer[J],Lecture Notes in Computer Science,2004,Vol.3316,230-235.
139.Baskar S,Alphones A,Suganthan PN Liang JJ.Design of Yagi-Uda antennas usingparticle swarm optimization with new learning strategy[A].IEE Proc MicrowavesAntennal Propag[C],2005,Vol,152,340-346.
140.V.L.Huang,P.N.Suganthan,J.J.Liang.Comprehensive Learning Particle SwarmOptimizer for Solving Multi-objective Optimization Problems[J].InternationalJournal of Intelligent Systems,2006,Vol.21,209-226.
141.J.F.Schutte,J.A.Reinbolt,B.J.Fregly,et.al.Parallel global optimization with theparticle swarm algorithm[J].International Journal for Numerical Methods inEngineering,2004,Vol.61,2296-2315.
142.高鹰,姚振坚,谢胜利.基于种群密度的粒子群优化算法[J].系统工程与电子技术,2006,Vol.28,No.6,922-924
143.高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用,2004,Vol.40,No.6,4-6
144.高鹰,谢胜利.混沌粒子群优化算法[J].计算机科学,2004,Vol.31,No.8,13-15
145.高鹰,谢胜利.基于模拟退火的粒子群优化算法[J].计算机工程与应用,2004,Vol.40,No.1,47-50
146.高尚,杨静宇.武器-目标分配问题的粒子群优化算法[J].系统工程与电子技术,2005,Vol.27,No.7,1250-1253.
147.高尚,杨静宇等.基于模拟退火思想的粒子群优化算法[J].计算机应用与软件,2005,Vol.22,No.1,103-105.
148.高尚,韩斌,吴小俊等.求解旅行商问题的混合粒子群优化算法[J].控制与决策,2004,Vol.19,No.11,1286-1289.
149.http://www.maurieeclerc.net.
150.Shi,Y.and Eberhart,R.C.Parameter selection in particle swarm optimization[A],Evolutionary Programming Ⅶ:Proceedings of the Seventh Annual Conference on Evolutionary Programming[C],New York.1998,591-600.
铩?51.http://neo.lcc.uma.es/radi-aeb/WebVRP.
152.Syslo,M.,Deo,N,Kowalski,J.Discrete Optimization Algorithms with PascalPrograms[M],Prentice Hall,Inc,1983.
153.Schrage,L.Formulation and structure of more complex/realistic routing andscheduling problems[J].Networks,1981,Vol.11,229-232.
154.Bodin L.,Golden B.,Assad A..Routing and scheduling of vehicles and crews:thestate of the art[J].Computers and Operations Research,1983,Vol.10,No.2 63-211.
155.Sariklis,D.,Powell,S.A heuristic method for the open vehicle routing problem[J].Journal of the Operational Research Society,2000,Vol.51,564-573.
156.付卓,开放式车辆路径问题及其应用研究[D],中南大学博士论文,2003
157.Fu,Z.,Eglese,R.Li.A new tabu search heuristic.for the open vehicle routingproblem[J].Journal of the Operational Research Society,2005,Vol.57,No.8,267-274.
158.Tarantilis C,Diakoulaki D,Kiranoudis C.Combination of geographicalinformation system and efficient routing algorithms for real life distributionoperations[J].European Journal of Operational Research,2004,Vol.152,No.2,437-53.
159.Tarantilis C,Ioannou G,Kiranoudis C,Prastacos G..A threshold acceptingapproach to the open vehicle routing problem[J].RAIRO Operations Research2004,Vol.38,345-360.
160.Tarantilis C,Ioannou G,Kiranoudis C,Prastacos G..Solving the open vehiclerouting problem via a single parameter metaheuristic algorithm[J].Journal of theOperational Research Society,2005,Vol.56,588-596.
161.Pisinger D,Ropke S.A general heuristic for vehicle routing problems[J].Workingpaper,Department of Computer Science,University of Copenhagen,Copenhagen,Denmark,2005.
162.Feiyue Li,Bruce Golden,and Edward Wasil.The open vehicle routing problem:Algorithms,large-scale test problems,and computational results[J].Computers &Operations Research,2006.8,Online,Elsevier.
铩?63.Gendreau.M,Hertz.A Laporte.G..New Insertion and Postoptimization:Proceduresfor the Traveling Salesman Problem[J].Operations Research,1992,Vol.40,No.6,1086-1093.
164.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.
165.Chen R,and Gen.M.Vehicle routing problem with fuzzy due-time using geneticalgorithms[J].Japanese Journal of Fuzzy Theory and Systems,1995,Vol.7,No.5,1050-1061.
166.贾永基,车辆调度问题优化算法研究[D].上海交通大学博士学位论文,2004.
167.张建勇,模糊信息条件下车辆路径问题研究[D].西南交通大学博士学位论文,2004.
168.张建勇,郭耀煌,李军.基于顾客满意度的车辆优化调度问题研究[J].铁道学报.2003,Vol.25,No.2,15-17.
169.王惠,陈燕.基于遗传算法的多目标的有时间窗的车辆调度[J].计算机应用,2004,Vol.24,No.9,144-146.
170.刘小兰,郝志蜂等.有时间窗的车辆路径问题的近似算法研究[J].计算机集成制造系统-CIMS,2004,Vol.10,No.7,825-831.
171.Wiel R J V,Sahinidis NV..Heuristic bounds and test problem generation for thetime-dependent traveling salesman problem[J].Transportation Science,1995,Vol.29,No.2,167-183.
172.AhnB-Hun,ShinJ-Y.Vehicle-routing with time windows and time-varyingcongestion[J].Journal of the Operational Research Society,1991,Vol.42,No.5,393-400.
173.Hill A V,Benton W C.,Modelling intra-eity time-dependent travel speeds forvehicle scheduling problems[J].Journal of the Operational Research Society,1992,Vol.43,No.4,343-351.
174.Jung S.,A genetic algorithm for vehicle routing problem with time-dependent traveltimes[D].University of Maryland College Park,2000.
175.Yang-Byung Park,A solution of the bicriteria vehicle scheduling problems withtime and area-dependent travel speeds[J].Computers & Industrial Engineering,2000,Vol.38,No.1,173-187.
铩?76.Alberto V.Donati,Roberto Montemanni,Norman Casagrande.Time dependentvehicle routing problem with a multi ant colony system[J].European Journal ofOperational Research,2006,online.
177.Huey-Kuo Chen,Che-Fu Hsueh,Mei-Shiang Chang.The real-time time-dependentvehicle routing problem[J],Transportation Research Part E,2006,Vol.42,No.5,383-408.
178.Liping Fu.Scheduling dial-a-ride Para-transit under time-varying,stochasticcongestion[J].Transportation Research Part B,2002,Vol.36,No.6,485-506.
179.Bernhard.F,Martin.G.Time-Varying Travel Times in Vehicle Routing[J].Transportation Science,2004,Vol.38,No.2,160-173.
180.Jean-Yves Potvin,Ying Xu,Ilham Benyahia.Vehicle routing and scheduling withdynamic travel times[J].Computers & Operations Research,2006,Voi.33,No.41129-1137.
181.Jinxin Yi,Vehicle Routing with Time Windows and Time-Dependent Rewards:AProblem from the American Red Cross[J],Manufacturing & Service OperationsManagement,2003,Vol.5,No.1,74-77.
182.肖增敏,李军,动态网络车辆路径问题:研究现状及展望[J].系统工程,2004,Vol.22,No.7,68-71.
183.肖增敏,动态网络车辆路径问题研究[D].西南交通大学硕士学位论文,2005.
184.吴启迪,汪镭.智能微粒群算法研究及应用[M].江苏教育出版社,2005.
185.张选平,杜玉平,秦国强,覃征.一种动态改变惯性权重的自适应粒子群算法[J].西安交通大学学报,2005,Vol.39,No.10,1039-1042.
186.A.Chatterjee and ESiarry.Nonlinear Inertia Weight Variation for DynamicAdaptation in Particle Swarm Optimization[J].Computers & Operations Research,2006,Vol.33,No.3,859-871.
187.Bo Liu,Ling Wang,Yi-Hui Jin,et.al.Improved particle swarm optimizationcombined with chaos[J].Chaos,Solitons & Fractals,2005,Vol.25,No.5,1261-1271.
188.Jiang Chuan wen,Etorre Bompard.A hybrid method of chaotic particle swarmoptimization and linear interior for reactive power optimisation[J].Mathematicsand Computers in Simulation,2005,Vol.68,No.1,57-65.
铩?89.王俊伟,汪定伟.粒子群算法中惯性权重的实验与分析[J].系统工程学报2005,Vol.20,No.2,194-198.
190.陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究[J].西安交通大学学报,2006,Vol.40,No.1,53-57.
191.Hanshar,Franklin,Ombuki-Berman,Beatrice.Dynamic vehicle routing usinggenetic algorithms[J].Applied Intelligence,2007,Vol 27,No.1,89-99.
192.R.Montemanni,L.M.Gambardella,A.E.Rizzoli.Ant Colony System for a DynamicVehicle Routing Problem[J].Journal of Combinatorial Optimization,2005,Vol.10,No.4,327-343
193.Repoussis,Tarantilis,Ioannou.The open vehicle routing problem with timewindows[J].Journal of the Operational Research Society,2007,Vol.58,No.13,355-367.
194.Aksen,D.Ozyurt,Z.Aras,N.Open vehicle routing problem with driver nodes andtime deadlines[J].Journal of the Operational Research Society,2006,Vol.58,No.9,1223-1234.
195.Xiangyong Li,Peng Tian.An Ant Colony System for the Open Vehicle RoutingProblem[J].Lecture Notes in Computer Science,2006,Vol.4150,356-363
196.陈宝文,宋申民,陈兴林.模糊需求车辆路径问题及其启发式蚁群算法[J].计算机应用,2006,Vol.26,No.11,2639-2642
197.Paola Pellegrini,Daniela Favaretto,Elena Moretti.Multiple Ant ColonyOptimization for a Rich Vehicle Routing Problem:A Case Study[J].Lecture Notesin Computer Science,2007 Vol.4693,627-634.
198.刘志硕,申金升,柴跃廷.基于自适应蚁群算法的车辆路径问题研究[J].控制与决策,2005,Vol.20,No.5,562-566.
199.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,Vol.10,No.3,15-22
200.刘云忠,宣慧玉.动态蚁群算法在带时间窗车辆路径问题中的应用[J].中国工程科学,2005,Vol.7,No.12,35-40
201.张涛,王珊珊,田文馨.车辆可重复利用VRPTW问题的模型和改进蚁群算法[J].系统工程,2007,Vol.25,No.4,20-26
铩?02.Chen Ai-ling,Yang Gen-ke and Wu Zhi-ming.Hybrid discrete particle swarmoptimization algorithm for eapacitated vehicle routing problem[J].Journal ofZhejiang University - Science A,2006,Vol.7,No.3,607-604
203.邱晗光,张旭梅,基于改进粒子群算法的开放式定位-运输路线问题研究[J],中国机械工程,2006,Vol.17,No.22,2359-2361
204.徐杰,黄德先.基于混合粒子群算法的多目标车辆路径研究[J].计算机集成制造系统,2007,Vol.13,No:3,573-579
205.丰伟,李雪芹.基于粒子群算法的多目标车辆调度模型求解[J].系统工程,2007,Vol.25,No.4,15-19
206.陈曦,蒋加伏.带变异粒子群算法在车辆路径问题中的应用[J].计算机与数字工程.2006,Vol.34,No.9,19-21
207.王芳,丁海利,高成修.改进的粒子群优化算法在随机需求车辆路径问题中的应用[J].武汉大学学报(理学版),2007,Vol.53,No.1,41-44.
208.陆琳,谭清美.一类随机需求VRP的混合粒子群算法研究[J],系统工程与电子技术.2006,Vol.28,No.2,244-247
209.Qing Zhu,Limin Qian,Yingchun Li,Shanjun Zhu.An Improved Particle SwarmOptimization Algorithm for Vehicle Routing Problem with Time Windows[C].Evolutionary Computation,2006,IEEE Congress on Vol.16-21,1386-1390
2. Ahmad Alshamrania, Kamlesh Mathurb, and Ronald H.Ballou. Reverse logistics: simultaneous design of delivery routes and returns strategies [J]. Computers &Operations Research 2007, vol.34, No.2, 595-619.
3. Lu Quan, Dessouky, Maged M. A new insertion-based construction heuristic for solving the pickup and delivery problem with time windows [J]. European Journal of Operational Research, 2006, Vol.175, No.2,672-687.
4. I.M.Chao, B.L.Golden, E.A.Wasil. An improved heuristic for the period vehicle routing problem [J]. Networks, 1995, Vol .26,25-44.
5. Angelelli E. and Speranza M.G... The periodic vehicle routing problem with intermediate facilities [J]. European Journal of Operational Research, 2002, Vol.137, No.2,233-247.
6. Francis, Peter Smilowitz, Karen.Modeling techniques for periodic vehicle routing problems [J] .Transportation Research: Part B, 2006, Vol.40, No10, 872-884.
7. Mingzhou Jin, Kai Liu, Royce O.Bowden. A two-stage algorithm with valid inequalities for the split delivery vehicle routing problem [J]. International Journal of Production Economics, 2006. Vol. 105, No. 1,228-242.
8. Archetti C, Speranza M, Hertz A. A Tabu Search Algorithm for the Split Delivery Vehicle Routing Problem [J]. Transportation Science, 2006, Vol. 40 No1, 64-73.
9. F.J.Arcelus, H.A.Eiselt and Edward Y.H.Lin. Multi-depot vehicle scheduling problems with time windows and waiting costs [J].International Transactions in Operational Research, 1998, Vol.5, No.3,201-210.
10. Benoit Crevier, Jean-Francois Cordeau and Gilbert Laporte. The multi-depot vehicle routing problem with inter-depot routes [J]. European Journal of Operational Research, 2007, Vol.176, No.2, 756-773.
铩?1.Lim,Andrew,Fan Wang.Multi-Depot Vehicle Routing Problem:A One-StageApproach[J].IEEE Transactions on Automation Science & Engineering,2005,Vol.2,No4,397-402.
12.Golden B,Assad A,Levy L.The fleet size and mix vehicle routing problem[J].Computers and Operations Research,1984,Vol.11:49-66.
13.Eunjeong Choia,Dong-Wan Tchab.A column generation approach to theheterogeneous fleet vehicle routing problem[J].Computers & OperationsResearch.2005,online.
14.Tarantilis,Kiranoudis,Vassiliadis.A threshold accepting metaheuristic for theheterogeneous fixed fleet vehicle routing problem[J].European Journal ofOperational Research,2004,Vol.152,No.1,148-158.
15.谢秉磊.随机车辆路径问题研究[D].西南交通大学博士学位论文,2003.
16.Laporte G.,Louveaux EV.An integer L-shaped algorithm for the capacitatedvehicle routing problem with stochastic demands[J].Operations Research,2002,Vol.3,415-423.
17.Laporte G.,Louveaux F.V.,Mereure H..The vehicle routing problem withstochastic travel times[J].Transportation Science,1992,Vol.26,161-170.
18.Bent Russell W.,Van Hentenryck.Scenario-Based Planning for Partially DynamicVehicle Routing with Stochastic Customers[J].Operations Research,2004,Vol.52,No.6,977-987.
19.Teodorovic.D et.al.The fuzzy set approach to the vehicle routing problem whendemand at nodes uncertain[J].Fuzzy sets and system,1996,Vol.82,307-317.
20.祝崇隽,刘民等.针对模糊需求的VRP的两种2-OPT算法[J].电子学报,2001,Vol.29,No.8,1035-1037.
21.Malandraki C.,Daskin MS.Time dependent vehicle routing problems:formulations,properties and heuristic algorithms[J].TransportationScience,1992,Vol.26,No.3,185-200.
22.MalandrakiC,Dial.RB.A restriced dynamic programming heuristic algorithm forthe time dependent traveling salesman problem[J].European Journal ofOperational Research,1996,Vol 90,No.1,45-50.
23. Ichoua S, Gendreau M, Potvin J-Y. Vehicle dispatching with time-dependent travel times [J]. European Journal of Operational Research, 2003, Vol.144, No.2, 379-396.
24. Richard Eglese, Will Maden, Alan Slater. A Road Timetable? to aid Vehicle Routing and scheduling [J]. Computers& Operations Research 2006, Vol.33, No.12, 3508-3519.
25. Johan Oppen, Arne. Arc routing in a node routing environment [J]. Computers & Operations Research, Vol33, No.4,2006,1033-1055.
26. Psaraftis H N. Dynamic Vehicle Routing Problem. In: Golden.B.L, Assad.A, eds. Vehicle Routing: Methods and Studies [M].Amsterdam NorthHolland, 1988. 223-248.
27. Psaraftis H.N.. Dynamic vehicle routing: status and prospects [J].Annals of Operations Research, 1995, Vol.61,143-164.
28. Ali Haghani and Soojung Jung. A dynamic vehicle routing problem with time-dependent travel times [J]. Computers & Operations Research, 2005, Vol.32, No.11, 2959-2986.
29. Eiichi Taniguchi and Hiroshi Shimamoto. Intelligent transportation system based dynamic vehicle routing and scheduling with variable travel times [J]. Transportation Research Part C: Emerging Technologies, 2004, Vol.12, No.3, 235-250.
30. Fuh-hwa Franklin Liu, Sheng-yuan Shen. An overview of a heuristic for vehicle routing problem with time windows [J]. Computers & Industrial Engineering, 1999, Vol.37, No.1, 331-334.
31. Olli Braysy, Michel Gendreau, Vehicle Routing Problem with Time Windows Part I: Route Construction and Local Search Algorithms [J]. Transportation Science, 2005, Vol.39, No.1, 104-118.
32. Olli Braysy, Michel Gendreau. Vehicle Routing Problem with Time Windows Part II: Metaheuristics [J]. Transportation Science, 2005, Vol.39, No.1, 119-139.
33. Gilbert Laportea, Michel Gendreaub, Jean-Yves Potvinb, et. al. Classical and modern heuristics for the vehicle routing problem [J]. Internal Transaction in Operations Research, 2000, Vol.7, 285-300.
34. Garcia, B.L., J.Y.Potvin, J.M.Rousseau. A parallel implementation of the tabu search heuristic for vehicle routing problems with time window constraints [J]. Computers & Operations Research, 1994, Vol. 21,1025-1033.
35. Cordeau, J.F., GLaporte, A.Mercier. A Unified Tabu Search Heuristic for Vehicle Routing Problems with Time Windows [J]. Journal of the Operational Research Society, 2001, Vol.52, 928-936.
36. Lau, H.C., M.Sim, K.M.Teo. Vehicle routing problem with time windows and a limited number of vehicles [J]. European Journal of Operational Research, 2003, Vol. 148, No.3, 559-568.
37. Jose Brandao. A new tabu search algorithm for the vehicle routing problem with backhauls [J]. European Journal of Operational Research, 2006, Vol.173, No.2, 540-555.
38. Taillard,E., P.Badeau, M.Gendreau et.al. A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows [J]. Transportation Science, 1997, Vol.31,170-186.
39. Rochat, Y.and E.Taillard. Probabilistic Diversification and Intensification in Local Search for Vehicle Routing [J]. Journal of Heuristics, 1995, Vol.1,147-167.
40. Homberger.Jorg, Gehring, Hermann. A two-phase hybrid metaheuristic for the vehicle routing problem with time windows [J]. European Journal of Operational Research, 2005, Vol.162 No.1, 220-238.
41. J.Y.Povtin, T. Kervahut. The Vehicle Routing Problem with Time Windows Part I: Tabu Search [J]. INFORMS Journal on Computing, 1996, Vol.8, No.2,158-164.
42. Michel Gendreau, Gilbert Laporte, Christophe Musaraganyi, et.al. A tabu search heuristic for the heterogeneous fleet vehicle routing problem [J]. Computers & Operations Research ,1999,Vol.26,No.12,1153-1173
43. Fermin Alfredo, Roberto Dieguez. A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service [J]. Computers & Operations Research, 2006, Vol.33, No.3, 595-619.
44. Jose Brandao. A tabu search algorithm for the open vehicle routing problem [J]. European Journal of Operational Research, 2004, Vol.157, No.3, 552-564.
45. Dag Haugland,Sin C.Ho,Gilbert Laporte.Designing delivery districts for the vehicle routing problem with stochastic demands[J].European Journal of Operational Research, 2007, Vol.180,No.3,997-1010.
46. Thangiah S.R., K.E.Nygard, P.L.Juell. GIDEON: A genetic algorithm system for vehicle routing with time windows [A]. Proc.7th IEEE Conf. Artificial Intelligence Application[C], Los Alamitos, IEEE Computer Society Press, 1991, 322-328.
47. Berger J., M. Salois and R. Begin. A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows [A], AI'98 Advances in Artificial Intelligence ,Lecture Notes in Artificial Intelligence 1418[C], Vancouver,Springer, 1998,114-127.
48. Potvin, J.Y. and S.Bengio. The Vehicle Routing Problem with Time Windows Part II: Genetic Search [J]. INFORMS Journal on Computing, 1996, Vol.8,165-172.
49. Gehring, H.and J.Homberger. Parallelization of a Two-Phase Metaheuristic for Routing Problems with Time Windows [J]. Journal of Heuristics, 2002, Vol.8, 251-276.
50. Berger, J., M.Barkaoui, O.Braysy. A route-directed hybrid genetic approach for the vehicle routing problem with time windows [J]. Information Systems Operational Research 2003, Vol.41,179-194.
51. Jung, S., B.R.Moon. A hybrid genetic algorithm for the vehicle routing problem with time windows [A]. Proceding Genetic Evolutionary Computing Conference[C], San Francisco, Morgan Kaufmann Publishers, 2002,1309-1316.
52. Bouthillier, G.Crainic. A cooperative parallel metaheuristic for vehicle routing with time windows [J]. Computer & Operation Research, 2005,V61.32, No.7,1685-1708.
53. Enrique Alba and Bernabe Dorronsoro. Computing nine new best-so-far solutions for Capacitated VRP with a cellular Genetic Algorithm [J]. Information Processing Letters, 2006, Vol 98, No 6,225-230.
54. C.M.R.R. Lima, M.C. Goldbarg and E.F.G. Goldbarg. A Memetic Algorithm for the Heterogeneous Fleet Vehicle Routing Problem [J]. Electronic Notes in Discrete Mathematics, 2004, Vol.18,171-176.
55. Jean Berger and Mohamed Barkaoui.A parallel hybrid genetic algorithm for the vehicle routing problem with time windows [J]. Computers & Operations Research, 2004, Vol.31, No.12,2037-2053.
56. Christian Prins.A simple and effective evolutionary algorithm for the vehicle routing problem [J]. Computers & Operations Research, 2004, Vol.31, No.12, 1985-2002.
57. M.Gendrau, G.Laporte, and J.Y.Potvin. Vehicle routing: Modern Heuristics [A].In E.Aarts and J.K.Lenstra, editors, Local Search in Combinatorial Optimization[C], Wiley and Sons, 1997, 311-336.
58. P.Tian, J.Ma, and D.M.Zhang. Application of the simulated annealing algorithm to the combinatorial optimization problem with permutation property: an investigation of generation mechanism [J]. European Journal of Operational Research, 1999, Vol.118, No.1, 81-94.
59. Li, H., A.Lim, J.Huang. Local search with annealing-like restarts to solve the VRPTW [J]. European Journal of Operational Research, 2003, Vol.150, No.1, 115-127.
60. Bent, R., P. Van Hentenryck. A two-stage hybrid local search for the vehicle routing problem with time windows [J]. Transportation Science, 2004, Vol.38, No.4, 515-530.
61. Czech, Z., P.Czarnas. Parallel simulated annealing for the vehicle routing problem with time windows [A]. Proc. 10~(th) European Workshop Parallel Distributed Network-Based Processing[C], Canary Islands, Spain, 2002,376-383.
62. R.Tavakkoli-Moghaddam, N.Safaei, Y.Gholipour. A hybrid simulated annealing for capacitated vehicle routing problems with the independent route length [J]. Applied Mathematics and Computation, 2006, Vol.176, No.2,445-454.
63. B.Bullnheimer, R.F.Hartl, and C.Strauss. Applying the ant system to the vehicle routing problem [A]. IN I. H. Osman S., S.Martello and C. Roucairol, editors, Meta- Heuristics: Advances and Trends in Local Search Paradigms for Optimization[C],KluwerAcadenics, 1998. 109-120.
64. B.Bullnheimer, R.F.Hartl, and C.Struss. An improved ant system algorithm for the vehicle routing problem [J]. Annals of Operations Research, 1999, Vol.89, 319-328.
铩?5.John E.Bella,Patrick R.McMullen.Ant colony optimization techniques for thevehicle routing problem[J].Advanced Engineering Informatics,2004,Vol.18,No.1,41-48.
66.Marc Reimann,Karl Doemer,Richard F.Hartl.D-Ants:Savings Based Ants divideand conquer the vehicle routing problem[J].Computers & Operations Research,2004,Vol.31,No.4,563-591.
67.Doerner KF,Gronalt M,Hartl RF,ea.al.Savings Ants for the vehicle routingproblem[A].In:Cagnoni S et al.,editors.Applications of evolutionary computing[C].Berlin:Springer,2002.11-20.
68.L.M.Gambardella,E.Taillard,and G.Agazzi.MACS-VRPTW:a multiple ant colonysystem for vehicle routing problems with time windows[A].D.Come et.al.Editors.New ideas in optimization[C].London,U.K.:McGraw-Hill,1999,63-76.
69.A.V.Donati,R.Montemanni,N.Casagrande,A.E.Rizzoli,and L.M.Gambardella.Time dependent vehicle routing problem with a multi ant colony system.TechnicalReport TR-17-03,IDSIA,Galleria 2,Manno,6928,Switzerland,2003.
70.R.Montemanni,L.M.Gambardella,A.E.Rizzoli,and A.V.Donati.A newalgorithm for a dynamic vehicle routing problem based on ant colony system.Technical Report TR-23-02,IDSIA,Galleria 2,Manno,6928,Switzerland,2004.
71.李宁,邹丹,孙德宝.车辆路径问题的粒子群算法研究[J].系统工程学报,2004,Vol.19,No.6,596-600.
72.李宁,邹彤,孙德宝.带时间窗车辆路径问题的粒子群算法[J].系统工程理论与实践,2004,Vol.24,No.4,130-135.
73.肖健梅,李军军,王锡淮.求解车辆路径问题的改进微粒群优化算法[J].计算机集成制造系统-CIMS,2005,Vol.11,No.4,577-581.
74.肖健梅,黄有方,李军军,王锡淮.基于离散微粒群优化的物流配送车辆路径问题[J].系统工程,2005,No 4,Vol.23,97-100.
75.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,Vol.22,No.8,79-84.
76.肖鹏,李茂军,张军平.车辆路径问题的单亲遗传算法[J].计算技术与自动化,2000,Vol.19,No.1,26-30.
铩?7.Potvin,J,Dube,D.and Robillard,C.Hybrid Approach to Vehicle Routing UsingNeural Networks and Genetic Algorithm[J].Applied Intelligence,1996,Vol.6,No.3,241-252.
78.Barrie M.Baker and M.A.Ayechew.A genetic algorithm for the vehicle routingproblem[J].Computers & Operations Research,2003,Vol 30,No 5,P787-800.
79.In-Chan Choi,Seong-In Kim and Hak-Soo Kim.A genetic algorithm with a mixedregion search for the asymmetric traveling salesman problem[J].Computers &Operations Research,2003,Vol30,No 5,773-786.
80.Jean Berger and Mohamed Barkaoui.A parallel hybrid genetic algorithm for thevehicle routing problem with time windows[J].Computers & Operations Research,2004,Vol.31,No.12,2037-2053.
81.姜大立,杨西龙.车辆路径问题的遗传算法研究[J].系统工程理论与实践.1999,Vol.19,No.6,44-45.
82.David Mester and Olli Braysy.Active-guided evolution strategies for large-scalecapacitated vehicle routing problems[J].Computers & Operations Research,2007,Vol34,No.10,2964-2975.
83.Mariam Tagmouti,Michel Gendreau,Jean-Yves Potvin.Arc routing problems withtime-dependent service costs[J].European Journal of Operational Research,2007,Vol.181,No.1,30-39
84.Feiyue Li,Bruce Golden,Edward Wasil.A record-to-record travel algorithm forsolving the heterogeneous fleet vehicle routing problem[J],Computers &Operations Research,2007,Vol34,2734-2742
85.李晓磊.一种新型的智能优化方法--人工鱼群算法[D].浙江大学博士学位论文,2003.
86.李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002.Vol.22,No.11,32-38.
87.Jeffrey Dean.Animats and what they can tell us[J].Trends in cognitivesciences.1998,Vol.2 No.1,60-67.
88.李晓磊,钱积新.基于分解协调的人工鱼群优化算法研究[J].电路与系统学报,2003,Vol.8,No.1:1-6.
铩?9.李晓磊,薛云灿,路飞,田国会.基于人工鱼群算法的参数估计方法[J].山东大学学报(工学版),2004,Vol 34,No.3,84-87.
90.李晓磊,冯少辉,钱积新等.基于人工鱼群算法的鲁棒PID控制器参数整定方法研究[J].信息与控制,2004,Vol.33,No.1,112-115.
91.李晓磊,路飞,田国会,钱积新,组合优化问题的人工鱼群算法应用[J].山东大学学报(工学版),2004,Vol.34,No.5,64-67.
92.马建伟,张国立,谢宏等.利用人工鱼群算法优化前向神经网络[J].计算机应用,2004,Vol.24,No.10,21-23.
93.黄光球,苏锦旗.基于人工鱼群算法的高级综合生产计划优化研究[J].微机发展,2005,Vol.15,No.10,49-55.
94.黄光球,陆秋琴,郑彦全.基于鱼群算法的油田多级站定位优化方法研究[J].西安石油大学学报(自然科学版),2006,Vol.21,No.4,98-102.
95.刘耀年,庞松岭,刘岱.基于人工鱼群算法神经网络的电力系统短期负荷预测[J].电工电能新技术,2005,Vol.24,No.4,5-8.
96.李亮,迟世春,林皋.禁忌鱼群算法及其在边坡稳定分析中的应用[J].工程力学,2006.Vol.23.No.3.6-10.
97.Kennedy,J.and Eberhart,R.C.Particle swarm optimization[A],Proceedings ofIEEE International Conference on Neural Networks[C],Piscataway,NJ.1995,1942-1948.
98.薛明志.进化计算与小波分析若干问题研究[D].西安电子科技大学博士学位论文,2004.
99.彭宇.群智能优化算法及理论研究[D].哈尔滨工业大学博士学位论文,2004
100.谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策,2003,Vol.18,No.2,129-134.
101.李宁,付国江,库少平等.粒子群优化算法的发展与展望[J].武汉理工大学学报,2005,Vol 27,No2,26-29.
102.夏桂梅,曾建潮.微粒群算法的研究现状及发展趋势[J].山西师范大学学报,2005,Vol.19,No.1,23-25.
103.陈国初,俞金寿.微粒群优化算法[J].信息与控制,2005,Vol.34,No.3,318-324.
104.张燕,汪镭,康琦等.微粒群优化算法及其改进形式综述[J].计算机工程与应 用,2005,No.2,1-4.
铩?05.刘波,王凌,金以慧,黄德先,微粒群优化算法研究进展[J].化工自动化及仪表,2005,Vol.32,No.3,1-6.
106.杨维,李歧强.粒子群优化算法综述[J].中国工程科学,2004,Vol.6,No.5,87-94
107.Kennedy,J.and Eberhart,R.C.Discrete binary version of the particle swarmalgorithm[A].Proceeding of the IEEE International Conference on Systems[C],Man,Cybernetics,Orlando,IEEE Press,1997,4104-4108.
108.Clerc,Maurice,Discrete Particle Swarm Optimization[M].New OptimizationTechniques in Engineering,Springer Verlag,2004,219-240.
109.Ayed Salmen,Imtiza Ahmad,Sabah AI-Madani,Particle Swarm Optimization fortask assignment problem[J],Microprocessor and Microsystems,2002,Vol.26,No.8,363-371.
110.夏蔚军,吴智铭,张伟等.微粒群优化在Job-shop调度中的应用[J].上海交通大学学报,2005,Vol.39,No.3,381-385.
111.Wang Wanliang,Wu Bin,et.al.Particle Swarm Optimization for Open VehicleRouting Problem[J].Lecture Notes in Artificial Intelligence,2006,No.4114,999-1007.
112.Zhang H,Li XD,Li H,et al.Particle swarm optimization-based schemes forresource-constrained project scheduling[J],Automation in Consctuction,2005,Vol.14,No.3,393-404.
113.Kennedy J.Small worlds and mega-minds:effects of neighborhood topology onparticle swarm performance[A].Proc.1999 IEEE Congress EvolutionaryComputation[C].Piscataway,NJ:IEEE Press,1999:1931-1938.
114.Kennedy,J.Improving Particle Swarm Performance with Cluster Analysis[A].Proceeding of the IEEE Conference on Evolutionary Computation[C],California:IEEE,2000,1507-1512.
115.Hu X,Eberhart R.C,Shi Y.Engineering Optimization with Particle Swarm[A].IEEE Swarm Intelligence Symposium[C],Indianapolis,USA,2003:53-57.
116.Suganthan P N.Particle Swarm Optimiser with Neighborhood Operator[A].Proceeding of the IEEE Congress of Evolutionary Computation[C],IEEE Piscataway 1999,1958-1962.
117.Mohais, AS, Mendes, R, Ward, C, et al. Neighborhood re-structuring in particle swarm optimization [J]. Lecture Notes in Artificial Intelligent, 2005, Vol.3809, 776-785.
118.Thiemo K, Jakob S. Particle Swarm Optimization with Spatial Particle Extension [A]. Proceedings of the Congress on Evolutionary Computation[C], Honolulu, HI: IEEE, 2002,1474-1479.
119.Clerc, M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization [A]. Proceeding of Congress on Evolutionary Computation[C], Washington DC. IEEE, Piscataway, 1999,1215-1218.
120.Xie X F, Zhang W J, Yand Z L. Adaptive Particle Swarm Optimization on Individual Level [A],the 6th Congress on Evolutionary Computation[C], Honolulu, HI:IEEE,2002,1456-1461.
121.Xie X F, Zhang W J, Yand Z L. Dissipative particle swarm optimization [A], the 6~(th) Congress on Evolutionary Computation[C], Honolulu, HI: IEEE,2002,1456-1461.
122.Xie X F, Zhang W J, Yand Z L. Hybrid Particle Swarm Optimization with Mass Extinction[A], IEEE International Conference on Communications, Circuits and Systems and West Sino Expositions[C], Chengdu: IEEE, 2002,1170-1173.
123.Al-Kazemi B, Mohan C K. Multi-phase generalization of the particle swarm optimization algorithm [A], Proceedings of Congress on Evolutionary Computation[C], Honolulu, HI:IEEE, 2002,489-494.
124. Van den Bergh, F. and Engelbrecht, A. P. A new locally convergent particle swarm optimizer [A]. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics[C], 2002,96-101.
125.Zhihua Cui, Jianchao Zeng. A Guaranteed Global Convergence Particle Swarm Optimizer [J], Lecture Notes in Computer Science, 2004,Vol.3066, 762-767.
126.Rodriguez A, Reggia J.A .Extending Self-organizing Particle Systems to Problem Solving [J]. Artificial Life, 2004, Vol.10, No.4,379-395.
127.B.Zhao, C.X. Guo. An improved particle swarm optimization algorithm for unit commitment [J].International Journal of Electrical Power & Energy Systems, Vol. 28, No.7,2006,482-490.
128.Buthainah, A Multi-phase Particle Swarm Optimization [D], Italia Syracuse University, 2002.
129.Xia-Ting Feng, Bing-Rui Chen, et.al. Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm [J].International Journal of Rock Mechanics and Mining Sciences, 2006, Vol.43, No.5,789-801.
130.Meng HY, Zhang XH, Liu SY.A co-evolutionary particle swarm optimization-based method for multiobjective optimization[A] ,Advances Artificial Intelligence[C], Lecture Notes in Artificial Intelligence, 2005,Vol .3809: 349-359.
131.De D, Ray S, Konar A, Chatterjee. An evolutionary SPDE breeding-based hybrid particle swarm optimizer: Application in coordination of robot ants for camera coverage area optimization [A], Pattern Recognition and Machine Intelligence[C], Lecture Notes in Computer Science, 2005, Vol .3776,413-416.
132.Li XD .Adaptively choosing neighborhood bests using species in a particle swarm optimizer for multimodal function optimization[A] ,Genetic and Evolutionary Computation[C], Lecture Notes in Computer Science ,2004,Vol.3102,105-116.
133.Ben Niu, Yunlong Zhu, Xiaoxian He, et al. Multi-population cooperative particle swarm optimization[A], Advances in Artificial Life[C]. Lecture Notes in Computer Science 2005, Vol.3630, 874-883.
134 Jiang Chuanwen, Etorre Bompard. A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization [J]. Mathematics and Computers in Simulation, 2005, Vol.68, No.1, 57-65.
135.L0vbjerg, M., Rasmussen, T.K., Krink, T.. Hybrid particle swarm optimizer with breeding and subpopulations [A]. Proceedings of the Third Genetic and Evolution Composition Conference[C], Morgan Kaufman, 2001, Vol.1,469-476.
136.Grosan C, Abraham A, Han SY, Gelbukh A.Hybrid particle swarm evolutionary algorithm for search and optimization[A], Advance in Artificial intelligence[C], Lecture Notes in Artificial Inteligence,2005, Vol.3789, 623-632.
137.Zhang J, Zhang JR, Li K. A sequential niching technique for particle swarm optimization[A],Advance in Intelligent Computing[C],Lecture Notes in Computer
Science,2005,Vol.3644,390-399.
铩?38.Liang JJ,Qin AK,Suganthan PN,Baskar S.Evaluation of comprehensive learningparticle swarm optimizer[J],Lecture Notes in Computer Science,2004,Vol.3316,230-235.
139.Baskar S,Alphones A,Suganthan PN Liang JJ.Design of Yagi-Uda antennas usingparticle swarm optimization with new learning strategy[A].IEE Proc MicrowavesAntennal Propag[C],2005,Vol,152,340-346.
140.V.L.Huang,P.N.Suganthan,J.J.Liang.Comprehensive Learning Particle SwarmOptimizer for Solving Multi-objective Optimization Problems[J].InternationalJournal of Intelligent Systems,2006,Vol.21,209-226.
141.J.F.Schutte,J.A.Reinbolt,B.J.Fregly,et.al.Parallel global optimization with theparticle swarm algorithm[J].International Journal for Numerical Methods inEngineering,2004,Vol.61,2296-2315.
142.高鹰,姚振坚,谢胜利.基于种群密度的粒子群优化算法[J].系统工程与电子技术,2006,Vol.28,No.6,922-924
143.高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用,2004,Vol.40,No.6,4-6
144.高鹰,谢胜利.混沌粒子群优化算法[J].计算机科学,2004,Vol.31,No.8,13-15
145.高鹰,谢胜利.基于模拟退火的粒子群优化算法[J].计算机工程与应用,2004,Vol.40,No.1,47-50
146.高尚,杨静宇.武器-目标分配问题的粒子群优化算法[J].系统工程与电子技术,2005,Vol.27,No.7,1250-1253.
147.高尚,杨静宇等.基于模拟退火思想的粒子群优化算法[J].计算机应用与软件,2005,Vol.22,No.1,103-105.
148.高尚,韩斌,吴小俊等.求解旅行商问题的混合粒子群优化算法[J].控制与决策,2004,Vol.19,No.11,1286-1289.
149.http://www.maurieeclerc.net.
150.Shi,Y.and Eberhart,R.C.Parameter selection in particle swarm optimization[A],Evolutionary Programming Ⅶ:Proceedings of the Seventh Annual Conference on Evolutionary Programming[C],New York.1998,591-600.
铩?51.http://neo.lcc.uma.es/radi-aeb/WebVRP.
152.Syslo,M.,Deo,N,Kowalski,J.Discrete Optimization Algorithms with PascalPrograms[M],Prentice Hall,Inc,1983.
153.Schrage,L.Formulation and structure of more complex/realistic routing andscheduling problems[J].Networks,1981,Vol.11,229-232.
154.Bodin L.,Golden B.,Assad A..Routing and scheduling of vehicles and crews:thestate of the art[J].Computers and Operations Research,1983,Vol.10,No.2 63-211.
155.Sariklis,D.,Powell,S.A heuristic method for the open vehicle routing problem[J].Journal of the Operational Research Society,2000,Vol.51,564-573.
156.付卓,开放式车辆路径问题及其应用研究[D],中南大学博士论文,2003
157.Fu,Z.,Eglese,R.Li.A new tabu search heuristic.for the open vehicle routingproblem[J].Journal of the Operational Research Society,2005,Vol.57,No.8,267-274.
158.Tarantilis C,Diakoulaki D,Kiranoudis C.Combination of geographicalinformation system and efficient routing algorithms for real life distributionoperations[J].European Journal of Operational Research,2004,Vol.152,No.2,437-53.
159.Tarantilis C,Ioannou G,Kiranoudis C,Prastacos G..A threshold acceptingapproach to the open vehicle routing problem[J].RAIRO Operations Research2004,Vol.38,345-360.
160.Tarantilis C,Ioannou G,Kiranoudis C,Prastacos G..Solving the open vehiclerouting problem via a single parameter metaheuristic algorithm[J].Journal of theOperational Research Society,2005,Vol.56,588-596.
161.Pisinger D,Ropke S.A general heuristic for vehicle routing problems[J].Workingpaper,Department of Computer Science,University of Copenhagen,Copenhagen,Denmark,2005.
162.Feiyue Li,Bruce Golden,and Edward Wasil.The open vehicle routing problem:Algorithms,large-scale test problems,and computational results[J].Computers &Operations Research,2006.8,Online,Elsevier.
铩?63.Gendreau.M,Hertz.A Laporte.G..New Insertion and Postoptimization:Proceduresfor the Traveling Salesman Problem[J].Operations Research,1992,Vol.40,No.6,1086-1093.
164.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.
165.Chen R,and Gen.M.Vehicle routing problem with fuzzy due-time using geneticalgorithms[J].Japanese Journal of Fuzzy Theory and Systems,1995,Vol.7,No.5,1050-1061.
166.贾永基,车辆调度问题优化算法研究[D].上海交通大学博士学位论文,2004.
167.张建勇,模糊信息条件下车辆路径问题研究[D].西南交通大学博士学位论文,2004.
168.张建勇,郭耀煌,李军.基于顾客满意度的车辆优化调度问题研究[J].铁道学报.2003,Vol.25,No.2,15-17.
169.王惠,陈燕.基于遗传算法的多目标的有时间窗的车辆调度[J].计算机应用,2004,Vol.24,No.9,144-146.
170.刘小兰,郝志蜂等.有时间窗的车辆路径问题的近似算法研究[J].计算机集成制造系统-CIMS,2004,Vol.10,No.7,825-831.
171.Wiel R J V,Sahinidis NV..Heuristic bounds and test problem generation for thetime-dependent traveling salesman problem[J].Transportation Science,1995,Vol.29,No.2,167-183.
172.AhnB-Hun,ShinJ-Y.Vehicle-routing with time windows and time-varyingcongestion[J].Journal of the Operational Research Society,1991,Vol.42,No.5,393-400.
173.Hill A V,Benton W C.,Modelling intra-eity time-dependent travel speeds forvehicle scheduling problems[J].Journal of the Operational Research Society,1992,Vol.43,No.4,343-351.
174.Jung S.,A genetic algorithm for vehicle routing problem with time-dependent traveltimes[D].University of Maryland College Park,2000.
175.Yang-Byung Park,A solution of the bicriteria vehicle scheduling problems withtime and area-dependent travel speeds[J].Computers & Industrial Engineering,2000,Vol.38,No.1,173-187.
铩?76.Alberto V.Donati,Roberto Montemanni,Norman Casagrande.Time dependentvehicle routing problem with a multi ant colony system[J].European Journal ofOperational Research,2006,online.
177.Huey-Kuo Chen,Che-Fu Hsueh,Mei-Shiang Chang.The real-time time-dependentvehicle routing problem[J],Transportation Research Part E,2006,Vol.42,No.5,383-408.
178.Liping Fu.Scheduling dial-a-ride Para-transit under time-varying,stochasticcongestion[J].Transportation Research Part B,2002,Vol.36,No.6,485-506.
179.Bernhard.F,Martin.G.Time-Varying Travel Times in Vehicle Routing[J].Transportation Science,2004,Vol.38,No.2,160-173.
180.Jean-Yves Potvin,Ying Xu,Ilham Benyahia.Vehicle routing and scheduling withdynamic travel times[J].Computers & Operations Research,2006,Voi.33,No.41129-1137.
181.Jinxin Yi,Vehicle Routing with Time Windows and Time-Dependent Rewards:AProblem from the American Red Cross[J],Manufacturing & Service OperationsManagement,2003,Vol.5,No.1,74-77.
182.肖增敏,李军,动态网络车辆路径问题:研究现状及展望[J].系统工程,2004,Vol.22,No.7,68-71.
183.肖增敏,动态网络车辆路径问题研究[D].西南交通大学硕士学位论文,2005.
184.吴启迪,汪镭.智能微粒群算法研究及应用[M].江苏教育出版社,2005.
185.张选平,杜玉平,秦国强,覃征.一种动态改变惯性权重的自适应粒子群算法[J].西安交通大学学报,2005,Vol.39,No.10,1039-1042.
186.A.Chatterjee and ESiarry.Nonlinear Inertia Weight Variation for DynamicAdaptation in Particle Swarm Optimization[J].Computers & Operations Research,2006,Vol.33,No.3,859-871.
187.Bo Liu,Ling Wang,Yi-Hui Jin,et.al.Improved particle swarm optimizationcombined with chaos[J].Chaos,Solitons & Fractals,2005,Vol.25,No.5,1261-1271.
188.Jiang Chuan wen,Etorre Bompard.A hybrid method of chaotic particle swarmoptimization and linear interior for reactive power optimisation[J].Mathematicsand Computers in Simulation,2005,Vol.68,No.1,57-65.
铩?89.王俊伟,汪定伟.粒子群算法中惯性权重的实验与分析[J].系统工程学报2005,Vol.20,No.2,194-198.
190.陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究[J].西安交通大学学报,2006,Vol.40,No.1,53-57.
191.Hanshar,Franklin,Ombuki-Berman,Beatrice.Dynamic vehicle routing usinggenetic algorithms[J].Applied Intelligence,2007,Vol 27,No.1,89-99.
192.R.Montemanni,L.M.Gambardella,A.E.Rizzoli.Ant Colony System for a DynamicVehicle Routing Problem[J].Journal of Combinatorial Optimization,2005,Vol.10,No.4,327-343
193.Repoussis,Tarantilis,Ioannou.The open vehicle routing problem with timewindows[J].Journal of the Operational Research Society,2007,Vol.58,No.13,355-367.
194.Aksen,D.Ozyurt,Z.Aras,N.Open vehicle routing problem with driver nodes andtime deadlines[J].Journal of the Operational Research Society,2006,Vol.58,No.9,1223-1234.
195.Xiangyong Li,Peng Tian.An Ant Colony System for the Open Vehicle RoutingProblem[J].Lecture Notes in Computer Science,2006,Vol.4150,356-363
196.陈宝文,宋申民,陈兴林.模糊需求车辆路径问题及其启发式蚁群算法[J].计算机应用,2006,Vol.26,No.11,2639-2642
197.Paola Pellegrini,Daniela Favaretto,Elena Moretti.Multiple Ant ColonyOptimization for a Rich Vehicle Routing Problem:A Case Study[J].Lecture Notesin Computer Science,2007 Vol.4693,627-634.
198.刘志硕,申金升,柴跃廷.基于自适应蚁群算法的车辆路径问题研究[J].控制与决策,2005,Vol.20,No.5,562-566.
199.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,Vol.10,No.3,15-22
200.刘云忠,宣慧玉.动态蚁群算法在带时间窗车辆路径问题中的应用[J].中国工程科学,2005,Vol.7,No.12,35-40
201.张涛,王珊珊,田文馨.车辆可重复利用VRPTW问题的模型和改进蚁群算法[J].系统工程,2007,Vol.25,No.4,20-26
铩?02.Chen Ai-ling,Yang Gen-ke and Wu Zhi-ming.Hybrid discrete particle swarmoptimization algorithm for eapacitated vehicle routing problem[J].Journal ofZhejiang University - Science A,2006,Vol.7,No.3,607-604
203.邱晗光,张旭梅,基于改进粒子群算法的开放式定位-运输路线问题研究[J],中国机械工程,2006,Vol.17,No.22,2359-2361
204.徐杰,黄德先.基于混合粒子群算法的多目标车辆路径研究[J].计算机集成制造系统,2007,Vol.13,No:3,573-579
205.丰伟,李雪芹.基于粒子群算法的多目标车辆调度模型求解[J].系统工程,2007,Vol.25,No.4,15-19
206.陈曦,蒋加伏.带变异粒子群算法在车辆路径问题中的应用[J].计算机与数字工程.2006,Vol.34,No.9,19-21
207.王芳,丁海利,高成修.改进的粒子群优化算法在随机需求车辆路径问题中的应用[J].武汉大学学报(理学版),2007,Vol.53,No.1,41-44.
208.陆琳,谭清美.一类随机需求VRP的混合粒子群算法研究[J],系统工程与电子技术.2006,Vol.28,No.2,244-247
209.Qing Zhu,Limin Qian,Yingchun Li,Shanjun Zhu.An Improved Particle SwarmOptimization Algorithm for Vehicle Routing Problem with Time Windows[C].Evolutionary Computation,2006,IEEE Congress on Vol.16-21,1386-1390