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.     

An ant colony algorithm for the multi-compartment vehicle routing problem

We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem associated with collection of recycling waste from households, treated as nodes in a spatial network. For networks where the nodes are concentrated in separate clusters, the use of k-means clustering can greatly improve the efficiency of the solution. The ACS algorithm is extended to model the use of multi-compartment vehicles with kerbside sorting of waste into separate compartments for glass, paper, etc. The algorithm produces high-quality solutions for two-compartment test problems.     

约束法蚁群算法在多目标VRP中的研究

多目标车辆路径问题(MVRP)在物流研究领域具有重要的理论和现实意义,但由于各目标之间的相互联系和制约使得建模和求解具有很大的难度.在众多求解方法中,蚁群算法对解决类似组合优化问题具有明显的优势,蚁群算法已成功应用于一系列单目标优化问题,但对多目标问题的研究还处于起步阶段.侧重结合目标约束法与蚁群算法来研究多目标车辆路径问题,使各优化目标之间形成既彼此独立,又相互联系和制约的机制,最终求得多目标优化意义下的一种平衡解.仿真结果证明该算法具有良好的收敛性和运行效率,对于物流运输的实际运作具有重要的现实意义.     

A multi-phase oscillated variable neighbourhood search algorithm for a real-world open vehicle routing problem

The aim of this study is to solve the newspaper delivery optimization problem for a media delivery company in Turkey by reducing the total cost of carriers. The problem is modelled as an open vehicle routing problem (OVRP), which is a variant of the vehicle routing problem. A variable neighbourhood search-based algorithm is proposed to solve a real-world OVRP. The proposed algorithm is tested with varieties of small and large-scale benchmark suites and a very large-scale real-world problem instance. The results of the proposed algorithm provide either the best known solution or a competitive solution for each of the benchmark instances. The algorithm also improves the real-world company’s solutions by more than 10%.     

带软时间窗的开放式满载车辆路径问题研究

为满足某些生产制造企业的满载运输需求,针对运输任务对车辆具有独占性的特点,分析得到总运输费用的大小取决于车辆的空车行驶费用,在此基础上,将带软时间窗的开放式满载车辆路径问题转化为带软时间窗的多车场开放式车辆路径问题,在非对称图上建立了相应的数学模型,并设计了近邻粒子群算法对模型进行求解。设计算例对算法进行了验证,实验结果表明：该算法可以快速求得软时间窗的开放式满载车辆路径问题的满意解。     

时间依赖型车辆路径问题的一种改进蚁群算法

时间依赖型车辆路径规划问题(TDVRP),是研究路段行程时间随出发时刻变化的路网环境下的车辆路径优化.传统车辆路径问题(VRP)已被证明是NP-hard问题,因此,考虑交通状况时变特征的TDVRP问题求解更为困难.本文设计了一种TDVRP问题的改进蚁群算法,采用基于最小成本的最邻近法(NNC算法)生成蚁群算法的初始可行解,通过局部搜索操作提高可行解的质量,采用最大--最小蚂蚁系统信息素更新策略.测试结果表明,与最邻近算法和遗传算法相比,改进蚁群算法具有更高的效率,能够得到更优的结果;对于大规模TDVRP问题,改进蚁群算法也表现出良好的性能,即使客户节点数量达到1000,算法的优化时间依然在可接受的范围内.     

需求可拆分车辆路径问题的聚类求解算法

针对传统的车辆路径问题通常假设客户的需求不能拆分,即客户的需求由一辆车满足,而实际上通过需求的拆分可使需要的车辆数更少,从而降低配送成本的问题,分析了需求可拆分的车辆路径问题的解的特征,证明了客户需求不宜拆分应满足的条件,设计了符合解的特征的聚类算法,并对其求解.通过实验仿真,将所提出的聚类算法与蚁群算法和禁忌搜索算法进行比较,所得结果表明了所提出的算法可以更有效地求得需求可拆分车辆路径问题的优化解,是解决需求可拆分车辆路径问题的有效方法.     

变路网情况下车辆路径问题建模及应用

受车辆调度中的一类现实需求启发,提出了路网结构可变情况下的车辆路径问题。探讨了路网变动对车辆路径的影响,在描述可变路网的基础上,基于路网、路径双层优化思想,建立了问题优化模型。考虑到路网变化给问题求解带来的复杂性,给出了改进遗传算法与随机递归算法相结合的求解策略。作为模型的直接应用和说明,最后的算例验证了模型和算法的合理性、有效性。     

基于改进蚁群算法的多约束QoS路由方法

高速多媒体网络路由问题是一个多QoS约束的NP一完全问题,提出一种改进蚁群路由算法对该问题进行求解。该算法采取了带记忆的后继节点选择方式,利用蚂蚁已走过的路径启发后继节点的选取;引入了基于目标函数的信息素更新机制,依据目标函数评价蚂蚁路径搜索行为,并根据蚂蚁的表现采取不同的信息素更新策略,提高了算法的寻优能力和收敛速度。仿真实验表明,该算法能快速得到较大程度满足业务QoS要求的路径。     

An enhanced ant colony optimization (EACO) applied to capacitated vehicle routing problem

In this paper, an enhanced ant colony optimization (EACO) is proposed for capacitated vehicle routing problem. The capacitated vehicle routing problem is to service customers with known demands by a homogeneous fleet of fixed capacity vehicles starting from a depot. It plays a major role in the field of logistics and belongs to NP-hard problems. Therefore, it is difficult to solve the capacitated vehicle routing problem directly when solutions increase exponentially with the number of serviced customers. The framework of this paper is to develop an enhanced ant colony optimization for the capacitated vehicle routing problem. It takes the advantages of simulated annealing and ant colony optimization for solving the capacitated vehicle routing problem. In the proposed algorithm, simulated annealing provides a good initial solution for ant colony optimization. Furthermore, an information gain based ant colony optimization is used to ameliorate the search performance. Computational results show that the proposed algorithm is superior to original ant colony optimization and simulated annealing separately reported on fourteen small-scale instances and twenty large-scale instances.     

多中心半开放式送取需求可拆分的车辆路径优化

针对多中心半开放式送取需求可拆分的车辆路径问题,构建了以车辆配送距离最短为目标的多中心半开放式送取需求可拆分的数学模型。设计大变异邻域遗传算法进行求解,采用二维染色体编码及顺序交叉策略,同时运用大变异策略和邻域搜索策略提高算法全局和局部的寻优能力,通过算例对比验证了所提模型与算法的有效性。算例实验表明,大变异邻域遗传算法在求解多中心物流配送车辆路径问题上求解质量较优、求解效率较高、求解结果较为稳定,同时验证了联合配送下多中心半开放式送取需求可拆分的配送模式优于独立配送下单中心送取需求可拆分的配送模式。研究成果不仅拓展了车辆路径问题,还可为相关快递物流企业配送优化提供决策参考。     

中转联盟运输调度的遗传算法研究

介绍中转运输调度问题的优越性。在此基础上建立了优化确定运输调度问题中转点的数学模型,并构造了求解该模型的遗传算法,算法中针对城市货物运输的具体特点,采用较新的交叉算子。实例计算表明:文中提出的模型和算法能够有效地解决AVRP中转点的确定问题。     

平衡装载约束下的车辆路径问题研究

针对车辆三维装载约束下的车辆路径问题（3L-VRP）进行研究,引进车辆的平衡装载约束,综合考虑传统的先进后出、局部支撑、脆弱性等约束,构建平衡装载约束下的车辆路径问题（BL-VRP）模型。针对模型中的平衡约束,提出一种接触面积的装载算法。在此基础上,构建以回溯遗传算法（B-GA）为骨架的多阶段算法框架,对车辆路径优化进行求解。研究结果表明,多阶段算法不仅在解决3L-VRP上好于目前已有算法,同时对BL-VRP表现优秀。提出的多阶段算法为解决BL-VRP问题提供一条参考思路,但在时效性上需要进一步完善。     

基于改进粒子群算法的物流配送车辆调度优化

车辆优化调度是提高物流企业运营效益的重要因素,针对标准粒子群优化算法存在的不足,提出一种改进粒子群算法（IPSO）的物流配送车辆调度优化方法。建立物流配送车辆调度优化的数学模型,将车辆与车辆路径编码成粒子,通过粒子之间的协作找到最优物流配送车辆调度优化方案,并对粒子群算法存在的不足进行了相应的改进,最后给出仿真实验对其性能进行测试。实验结果表明,IPSO算法不仅加快了物流配送车辆调度优化问题求解的速度,而且获得了最优解的概率,具有比其他调度算法更明显的优势。     

嵌套分割算法在随机需求车辆路径问题中的应用研究

对需求量满足二项分布的随机需求车辆路径问题进行了研究,在服务失败时采取允许部分服务的策略,并将嵌套分割算法与扫描算法相结合,给出了一种新的求解随机需求车辆路径问题的两阶段算法,数值试验验证了该算法的有效性。同时,该算法也拓展了车辆路径问题的算法空间。     

新型遗传模拟退火算法求解带VRPTW问题

为了克服现有遗传算法不能有效求解时间窗车辆路径问题的缺陷,提出了一种由遗传算法结合模拟退火算法的混合算法求解该问题,并与遗传算法进行了比较。该算法利用了模拟退火算法具有较强的局部搜索能力的特性,有效地克服了传统遗传算法的“早熟收敛”问题。实验结果表明,该算法具有计算效率高、收敛速度快和求解质量优的特点,是解决车辆路径问题的有效方法。     

基于JPS策略的ACS移动机器人全局路径规划

针对蚁群系统（ACS）算法收敛速度慢、易陷入局部最优、路径转折点数量过多等问题,提出了一种基于跳点搜索（JPS）策略的ACS全局路径规划算法．该算法在迭代前加入一只特殊蚂蚁,利用方向因子引导该蚂蚁始终朝着目标方向前进,并查询是否存在最简路径;在蚂蚁查询下一个节点时,利用JPS算法思想舍去大部分不需要计算的节点．最后,为验证该方法的有效性,使用不同规格的栅格地图进行了仿真实验,仿真结果表明,改进的ACS算法相比于ACS算法,收敛速度加快、收敛时间缩短,且路径更优．最后将算法应用到实际的基于机器人操作系统（ROS）的移动机器人导航实验中,实验结果表明,改进的ACS算法能够有效地解决移动机器人全局路径规划问题,且能明显提升机器人全局路径规划的效率．     

多配送中心粮食物流车辆调度混合蚁群算法

在对多配送中心粮食车辆调度问题进行直观描述的基础上,建立了该问题的数学模型。并在国内外研究现状的基础上,提出了一个混合蚁群算法来求解多配送中心车辆调度问题,设计了蚂蚁转移策略、可行解构造策略和信息素更新策略,采用K邻域来限制蚂蚁的转移目标,并采用LK算法优化策略来优化蚂蚁遍历路径和可行解。给出了一个具有代表性的算例实验结果和结果分析,通过实验表明了此方法对优化多配送中心粮食车辆调度问题的有效性。     

基于划分的蚁群算法求解货物权重车辆路径问题

考虑单产品分销网络中的车辆路径问题(VRP:vehicle routing problem).与以往诸多研究不同的是,建立了一种带货物载重量的VRP模型(weighted VRP),即车辆在两个顾客之间行驶时的载重量也作为影响运输费用的一个因素考虑.因此,需求量较大的顾客拥有较高的车辆运输优先权.在分析了问题性质的基础上,提出一种基于划分策略的蚁群算法PMMAS求解货物权重车辆路径问题,并与其他常用的启发式算法进行比较分析,表明了算法的有效性.     

A multistart adaptive memory-based tabu search algorithm for the heterogeneous fixed fleet open vehicle routing problem

This paper addressed the heterogeneous fixed fleet open vehicle routing problem (HFFOVRP), in which the demands of customers are fulfilled by a fleet of fixed number of vehicles with various capacities and related costs. Moreover, the vehicles start at the depot and terminate at one of the customers. This problem is an important variant of the classical vehicle routing problem and can cover more practical situations in transportation and logistics. We propose a multistart adaptive memory programming metaheuristic with modified tabu search algorithm to solve this new vehicle routing problem. The algorithmic efficiency and effectiveness are experimentally evaluated on a set of generated instances.