超启发式分布估计算法求解带软时间窗的同时取送货车辆路径问题

本文针对带软时间窗的同时取送货车辆路径问题(VRPSPDSTW),以最小化车辆行驶总里程和最大化服务准时率为优化目标,提出一种超启发式分布估计算法(HHEDA)进行求解.全局搜索阶段,首先,提出3种启发式规则生成初始个体,以确保初始种群的质量和分散性;其次,根据问题特点,构造3个概率矩阵分别学习和积累优质解的排序信息、...     

带时间窗的同时取送货车辆路径问题建模及模因求解算法

为解决逆向物流背景下的带时间窗的同时取送货车辆路径问题（VRPSPDTW）,根据实际情况建立了相应的车辆路径问题模型,并采用模因算法进行求解。在模型的求解过程中使用引导弹射搜索（GES）生成初始种群,在种群进化的过程中采用边界组合交叉（EAX）产生子代,并采用多种邻域结构对子代进行修复、教育,以提高解的质量和算法的搜索效率。通过在Wang和Chen测试数据集上与遗传算法（GA）、并行模拟退火（p-SA）算法、离散布谷鸟（DCS）算法进行比较,实验结果显示：在小规模算例进行求解时,所提算法全部取得了当前最优解;对标准规模算例进行求解时,所提算法使70%的算例更新或获取了当前最优解,获得的最优求解算例结果与当前最优解相比有超过5%的提升,充分验证了所提算法求解VRPSPDTW的良好性能。     

可行解优先蚁群算法对车辆路径问题的求解

针对车辆路径问题,给出了一种利用蚁群算法求解该问题的新方法。借鉴K-TSP问题的求解方法,优先构造可行解,通过对较优解路径上信息素的增强,最终得到问题的最优解或较优解。实验结果表明,用本方法求解车辆路径问题,简化了求解过程,缩短了求解时间,解决了无可行解的问题。     

多目标同时取送货车辆路径问题的改进蚁群算法

为使同时取送货车辆路径问题(vehicle routing problem with simultaneous pickup and delivery, VRPSPD)的运输成本和各路径间最大长度差最小化,建立同时考虑车辆容量和距离约束的VRPSPD双目标模型,通过软件测试验证了模型准确性.针对问题的特点构造一个嵌入禁忌表、且具有贪婪转移准则的多目标蚁群算法,对蚂蚁产生的解执行多目标迭代局部搜索程序,以在多个邻域上优化该解或产生新的Pareto解.采用响应曲面法拟合算法参数对目标值影响的数学关系,确定最优参数组合.用该算法求得文献中12组Solomon算例的Pareto解集,并以绝对偏向最小化总成本的解与文献中仅最小化总成本的几种算法的计算结果进行比较,结果表明算法可求得权衡各目标且使单一目标近似最优的Pareto解.     

考虑同时取送和时间窗的车辆路径及求解算法

针对带时间窗的同时取送货车辆路径问题（vehicle routing problem with simultaneous pickup-delivery and time windows,VRPSPDTW），构建了以车辆使用成本、车辆行驶距离成本总支出最小化的路径优化数学模型，提出自适应头脑风暴算法（adaptive brain storm optimization,ABSO）进行求解。全局搜索阶段，采用多项惩罚方式扩大搜索区域，并使用聚类及三种路径搜索策略进行全局搜索；局部搜索阶段，将六种破坏-修复算子作为备选集合，进而设计自适应动态选择邻域搜索机制，增强局部搜索效能。选取测试数据集和实际案例对算法性能进行测试，实验结果表明针对小规模标准算例，所提算法全部取得了当前已知最优解；对于大规模标准算例，通过与遗传算法、并行模拟退火算法、离散布谷鸟算法对比，所提算法实验计算结果有7.52%～12.03%的提升；对于实际案例，所提算法在收敛速度和寻优能力方面均展示出优越性，充分验证了所提算法对解决VRPSPDTW问题的有效性。     

考虑动态平衡装卸的点对点取送货车辆路径优化

针对点对点取送货车辆路径优化问题,引入动态平衡、后进先出、三维装载等约束,以总路径最短为优化目标,构建多车多客户应用场景下的动态平衡装卸点对点取送货车辆路径优化模型;基于研究问题的特征,采用启发式插入法确定路径初始方案,设计节点交换和重新定位算子,构造路径邻域方案,并将动态平衡装卸纳入路径迭代过程,运用多重指标定序策略和三分空间策略,设计客户动态平衡装卸检算算法,并提出基于禁忌搜索的点对点取送货车辆路径优化算法,制订多车多客户取送货车辆路径方案的同时编制动态平衡装载方案。最后,通过标准算例验证方法的有效性,计算表明：所提方法能高效解决带动态平衡约束的点对点取送货车辆路径优化问题;在多车多客户应用场景下具有更强的寻优能力,求解效率更高。     

车辆路径问题模型及其智能启发式算法

车辆路径问题是运筹学研究的一个重要分支。描述了 6种典型的车辆路径问题的模型 ,并列举了当前的 9种启发式算法     

双层车辆路径问题的混合启发式算法

针对与城市物流密切相关的双层车辆路径问题(2E-VRP),提出了一种用来求解的混合启发式算法.该算法利用贪心算法的快速性、蚁群算法的搜索多样性以及邻域搜索算法较强的局部寻优能力来提高求解质量,加速算法的收敛性.把该算法应用于22个测试算例和3个大规模的算例,并与同类研究进行了比较.实验结果表明,混合启发式算法不仅能保证较高的精确性,而且具有很高的效率;与精确性最高的同类算法相比,虽然在解的质量方面稍逊,但在求解速度方面表现出了明显的优势.实验结果还显示了构建双层城市物流系统的潜在收益,中转站的设置能够在一定条件下显著提高城市物流效率.     

求解带硬时间窗车辆路径问题的时差插入启发式算法

针对已有求解带硬时间窗车辆路径问题时插入启发式算法结构复杂、参数多、求解效率不高的缺点,提出了求解该问题的时差插入启发式算法。该算法引入时差的概念,将时差作为启发规则的评价指标。相比已有求解该问题的经典启发式算法,该算法有参数个数少、算法结构简单等特点。应用标准测试算例测试表明,所提算法的求解质量优于Solomon的插入启发式算法和Potvin的平行插入启发式算法。     

考虑同时取送货的车机协同路径优化问题

考虑到传统同时取送货问题模式单一,无法应对复杂多变情况的现实需要,研究了一种考虑同时取送货的路径优化问题(vehicle routing problem with drones for simultaneous pickup and delivery, VRPD-SPD)。首先,以车辆与无人机总成本最小为优化目标,建立了考虑无人机单架次访问顺序约束的混合整数线性规划模型。其次,提出了一种基于遗传思想的两阶段启发式算法(two-stage heuristic algorithm based genetic, TSHAG),第一阶段结合贪婪算法和节约算法生成初始解,第二阶段通过改进的遗传算法优化初始解,设计了多元组编码方式来提高解码效率,改进了交叉算子来增加邻域解的搜索空间,设计了新的变异算子来提高算法全局寻优性能。最后,算例实验结果表明了TSHAG算法能够有效地解决VRPD-SPD问题。     

具有同时集送货需求车辆路径问题的混沌量子进化算法研究

针对量子进化算法中旋转角取值的离散性使其解空间的搜索具有跳跃性,提出了基于混沌理论的精英均值计算旋转角算法,并将其应用于具有同时集送货需求车辆路径问题的求解.在理论上分析了解的强可行和弱可行条件的基础上,使用启发式算子对解进行改进.通过仿真实验与其他算法进行了比较,仿真结果表明所提出算法是求解此类问题的有效方法.     

Multi-start simulated annealing heuristic for the location routing problem with simultaneous pickup and delivery

The location routing problem with simultaneous pickup and delivery (LRPSPD) is a new variant of the location routing problem (LRP). The objective of LRPSPD is to minimize the total cost of a distribution system including vehicle traveling cost, depot opening cost, and vehicle fixed cost by locating the depots and determining the vehicle routes to simultaneously satisfy the pickup and the delivery demands of each customer. LRPSPD is NP-hard since its special case, LRP, is NP-hard. Thus, this study proposes a multi-start simulated annealing (MSA) algorithm for solving LRPSPD which incorporates multi-start hill climbing strategy into simulated annealing framework. The MSA algorithm is tested on 360 benchmark instances to verify its performance. Results indicate that the multi-start strategy can significantly enhance the performance of traditional single-start simulated annealing algorithm. Our MSA algorithm is very effective in solving LRPSPD compared to existing solution approaches. It obtained 206 best solutions out of the 360 benchmark instances, including 126 new best solutions.     

集货需求模糊的异型车同时配集货路径优化

针对集货需求模糊的异型车同时配集货车辆路径问题(HFVRPSDDFP),基于先预优化再重优化的思路构建模型.预优化阶段根据可信度理论和车型选取方法为客户点分配车辆,生成配送方案.重优化阶段利用随机模拟算法(SSA)确定客户集货需求,对服务失败的客户点,制定服务策略,将模糊问题转化为确定型的异型车辆路径问题(HFVRP)...     

A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery

This paper proposes a formulation of the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) and a particle swarm optimization (PSO) algorithm for solving it. The formulation is a generalization of three existing VRPSPD formulations. The main PSO algorithm is developed based on GLNPSO, a PSO algorithm with multiple social structures. A random key-based solution representation and decoding method is proposed for implementing PSO for VRPSPD. The solution representation for VRPSPD with n customers and m   vehicles is a (n+2m)$(n+2m)$-dimensional particle. The decoding method starts by transforming the particle to a priority list of customers to enter the route and a priority matrix of vehicles to serve each customer. The vehicle routes are constructed based on the customer priority list and vehicle priority matrix. The proposed algorithm is tested using three benchmark data sets available from the literature. The computational result shows that the proposed method is competitive with other published results for solving VRPSPD. Some new best known solutions of the benchmark problem are also found by the proposed method.     

基于剩余装载能力的蚁群算法求解同时送取货车辆路径问题

建立了带车辆最大行程约束的同时送取货车辆路径问题的混合整数规划模型; 采用了基于排序的蚂蚁系统和最大最小蚂蚁系统的信息素更新策略; 设计了基于车辆剩余装载能力的启发信息策略, 可在满足车辆负载的限制下, 提高车辆的负载利用率; 并在改进阶段使用了节点交换的局部搜索策略,以提高算法收敛速度. 仿真结果表明本文算法能够在可接受的计算时间内得到满意解.     

A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery

Vehicle routing problem (VRP) is an important and well-known combinatorial optimization problem encountered in many transport logistics and distribution systems. The VRP has several variants depending on tasks performed and on some restrictions, such as time windows, multiple vehicles, backhauls, simultaneous delivery and pick-up, etc. In this paper, we consider vehicle routing problem with simultaneous pickup and delivery (VRPSPD). The VRPSPD deals with optimally integrating goods distribution and collection when there are no precedence restrictions on the order in which the operations must be performed. Since the VRPSPD is an NP-hard problem, we present a heuristic solution approach based on particle swarm optimization (PSO) in which a local search is performed by variable neighborhood descent algorithm (VND). Moreover, it implements an annealing-like strategy to preserve the swarm diversity. The effectiveness of the proposed PSO is investigated by an experiment conducted on benchmark problem instances available in the literature. The computational results indicate that the proposed algorithm competes with the heuristic approaches in the literature and improves several best known solutions.     

An ant colony system empowered variable neighborhood search algorithm for the vehicle routing problem with simultaneous pickup and delivery

Along with the progress in computer hardware architecture and computational power, in order to overcome technological bottlenecks, software applications that make use of expert and intelligent systems must race against time where nanoseconds matter in the long-awaited future. This is possible with the integration of excellent solvers to software engineering methodologies that provide optimization-based decision support for planning. Since the logistics market is growing rapidly, the optimization of routing systems is of primary concern that motivates the use of vehicle routing problem (VRP) solvers as software components integrated as an optimization engine. A critical success factor of routing optimization is quality vs. response time performance. Less time-consuming and more efficient automated processes can be achieved by employing stronger solution algorithms. This study aims to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) which is a popular extension of the basic Vehicle Routing Problem arising in real world applications where pickup and delivery operations are simultaneously taken into account to satisfy the vehicle capacity constraint with the objective of total travelled distance minimization. Since the problem is known to be NP-hard, a hybrid metaheuristic algorithm based on an ant colony system (ACS) and a variable neighborhood search (VNS) is developed for its solution. VNS is a powerful optimization algorithm that provides intensive local search. However, it lacks a memory structure. This weakness can be minimized by utilizing long term memory structure of ACS and hence the overall performance of the algorithm can be boosted. In the proposed algorithm, instead of ants, VNS releases pheromones on the edges while ants provide a perturbation mechanism for the integrated algorithm using the pheromone information in order to explore search space further and jump from local optima. The performance of the proposed ACS empowered VNS algorithm is studied on well-known benchmarks test problems taken from the open literature of VRPSPD for comparison purposes. Numerical results confirm that the developed approach is robust and very efficient in terms of both solution quality and CPU time since better results provided in a shorter time on benchmark data sets is a good performance indicator.