The r‐interdiction selective multi‐depot vehicle routing problem

The protection of critical facilities has been attracting increasing attention in the past two decades. Critical facilities involve physical assets such as bridges, railways, power plants, hospitals, and transportation hubs among others. In this study we introduce a bilevel optimization problem for the determination of the most critical depots in a vehicle routing context. The problem is modeled as an attacker–defender game (Stackelberg game) from the perspective of an adversary agent (the attacker) who aims to inflict maximum disruption on a routing network. We refer to this problem as the r‐interdiction selective multi‐depot vehicle routing problem (RI‐SMDVRP). The attacker is the decision maker in the upper level problem (ULP) who chooses r depots to interdict with certainty. The defender is the decision maker in the lower level problem (LLP) who optimizes the vehicle routes in the wake of the attack. The defender has to satisfy all customer demand either using the remaining depots or through outsourcing to a third party logistics service provider. The ULP is solved through exhaustive enumeration, which is viable when the cardinality of interdictions does not exceed five among nine depots. For the LLP we implement a tabu search heuristic adapted to the selective multi‐depot VRP. Our results are obtained on a set of RI‐SMDVRP instances synthetically constructed from standard MDVRP test instances.     

一种求解两级车辆路径问题的Memetic 算法

两级车辆路径问题是指物资必须先由中心仓库配送至中转站(第1级),再由中转站配送至客户(第2级)的一种车辆路径问题。针对该NP难问题提出一种Memetic算法通过自底向上的方式进行求解。首先利用改进的最优切割算法MDVRP-Split将客户合理分配至中转站;然后采用局部搜索解决第1级问题,交叉产生的精英个体通过局部搜索改进。标准算例的测试结果表明,所提出算法更注重求解质量与求解效率的平衡,性能优于其他现有的两种算法。     

带时间窗车辆路径问题的多目标文化基因算法

针对物流配送中带时间窗的车辆路径问题,以最小化车辆使用数和行驶距离为目标,建立了多目标数学模型,提出了一种求解该问题的多目标文化基因算法。种群搜索采用遗传算法的进化模式和Pareto排序的选择方式,局部搜索采用禁忌搜索机制和存储池的结构,协调两者得到的Pareto非占优解的关系。与不带局部搜索的多目标遗传算法和单目标文化基因算法的对比实验表明,本文算法的求解质量较高。     

需求可拆分车辆路径问题的禁忌搜索算法

为解决实际配送运输中的车辆路径问题（Vehicle Routing Problem,VRP）,通过改进传统的数学模型,解除每个客户需求只能由l辆车配送的约束,建立改进的可拆分车辆路径问题（Split Delivery VRP,SDVRP）数学模型,并利用禁忌搜索算法（Taboo Search Algorithm,TSA）进行求解．在TSA的设计中,根据SDVRP模型的特点对初始解、邻域搜索和解的评价等进行特殊处理．算例表明,该模型不仅可以解决VRP模型中不允许配送点需求量超出装载量的限制,而且通过相应配送点需求量的拆分和重新组合,可节省车辆数目、缩短路线长度、提高车辆装载率．     

Beam-PSO优化算法在多行程车辆路径问题的应用

针对城市物流配送系统，研究了一类带时间窗和释放时间约束的多行程车辆路径问题。首先，对该运输调度问题进行了描述，构建了以总配送时长最小化为目标的数学模型。其次，为了快速获得问题的满意解，提出了Beam-PSO优化算法。在算法设计中，结合该问题的性质，构建了基于随机键的编解码方法，以克服标准粒子群算法无法直接适用于求解离散问题的不足。同时，设计了基于Beam search优化技术的局部搜索流程，用于强化算法的优化性能。最后，进行了仿真实验，实验结果表明了Beam-PSO优化算法的可行性和有效性。     

Adaptive granular local search heuristic for a dynamic vehicle routing problem

The advance of communication and information technologies based on satellite and wireless networks have allowed transportation companies to benefit from real-time information for dynamic vehicle routing with time windows. During daily operations, we consider the case in which customers can place requests such that their demand and location are stochastic variables. The time windows at customer locations can be violated although lateness costs are incurred. The objective is to define a set of vehicle routes which are dynamically updated to accommodate new customers in order to maximize the expected profit. This is the difference between the total revenue and the sum of lateness costs and costs associated with the total distance traveled. The solution approach makes use of a new constructive heuristic that scatters vehicles in the service area and an adaptive granular local search procedure. The strategies of letting a vehicle wait, positioning a vehicle in a region where customers are likely to appear, and diverting a vehicle away from its current destination are integrated within a granular local search heuristic. The performance of the proposed approach is assessed in test problems based on real-life Brazilian transportation companies.     

An evolutionary algorithm for the multi‐objective pick‐up and delivery pollution‐routing problem

The design of sustainable logistics solutions poses new challenges for the study of vehicle‐routing problems. The design of efficient systems for transporting products via a heterogeneous fleet of vehicles must consider the minimization of cost, emissions of greenhouse gases, and the ability to serve every customer within an available time slot. This phenomenon gives rise to a multi‐objective problem that considers the emission of greenhouse gases, the total traveling time, and the number of customers served. The proposed model is approached with an ε‐constraint technique that allows small instances to be solved and an evolutionary algorithm is proposed to deal with complex instances. Results for small instances show that all the points that approach the Pareto frontier found by the evolutionary algorithm are nondominated by any solution found by the multi‐objective model. For complex instances, nondominated solutions that serve most of the requests are found with low computational requirements.     

模糊需求下时间依赖型车辆路径优化

针对客户需求模糊且有时间窗约束的时间依赖型车辆路径问题(TDVRP),基于先预优化后重调度的思想构建模型.在预优化阶段,依据可信性理论构建模糊机会约束优化模型处理客户点模糊需求;针对不同时间段道路的交通情况,采用Ichoua速度时间依赖函数表征车辆的行驶速度,并设计自适应大规模邻域搜索算法(ALNS)对其求解.在重调度阶段,应用随机模拟算法模拟客户点的真实需求,采用点重调度策略对预优化方案进行调整.通过改进的Solomon算例实验验证模型和算法的有效性.研究成果可丰富TDVRP问题的相关研究,为现实配送方案的优化决策提供理论依据.     

An improved hybrid firefly algorithm for capacitated vehicle routing problem

Firefly algorithm (FA) is a new meta-heuristic which is successfully applied to solve several optimization problems. However, it suffers from a drawback of easily getting stuck at local optima. This paper proposes a new hybrid FA, called CVRP-FA, to solve capacitated vehicle routing problem. In CVRP-FA, FA is integrated with two types of local search and genetic operators to enhance the solution’s quality and accelerate the convergence. The experiments are conducted over 82 benchmark instances. The results demonstrate that CVRP-FA has fast convergence rate and high computational accuracy. It significantly outperforms the other state-of-the-art FA variants in majority of the tested instances.     

基于混合算法的带时间窗车辆路径问题

使用改进蚁群算法结合大规模邻域搜索算法解决带时窗限制的车辆路径问题.首先对蚁群算法信息素及算法结构进行分析及改进,并提出了新的解题策略,由此得到可行解;然后在区域改善部分用邻域搜索算法进一步提高解的性能.给出混合算法计算Solomon100国际标准题库问题的结果,并与同类方法的文献最优解进行比较.     

考虑装卸频率的大规模车辆路径问题研究

通过分析大规模车辆路径问题的特点和求解难点,从我国的配送实践出发,引入装卸频率的概念,从新的视角认识大规模车辆路径问题,建立了考虑装卸频率的车辆路径优化多目标规划模型,并设计了改进的混合遗传算法进行求解。实验结果表明,该算法能够大幅降低企业配送成本和配送的装卸频率,具有实际参考价值和应用前景。     

基于有时间窗车辆路径问题的混合蚁群算法

有时间窗的车辆路径问题是目前组合优化领域研究的热点问题,其归属于NP-hard问题.在对该问题进行分析的基础上,为之建立了数学模型,提出了一种求解该问题的混合蚁群算法.该算法通过在蚁群算法中引AA-interchange变异算子,增强了算法的局部搜索能力,避免了早熟现象.实验结果表明,该算法能有效解决有时间窗的车辆路径问题.     

Agile optimization of a two‐echelon vehicle routing problem with pickup and delivery

In this paper, we consider a vehicle routing problem in which a fleet of homogeneous vehicles, initially located at a depot, has to satisfy customers' demands in a two‐echelon network: first, the vehicles have to visit intermediate nodes (e.g., a retail center or a consolidation center), where they deliver raw materials or bulk products and collect a number of processed items requested by the customers in their route; then, the vehicles proceed to complete their assigned routes, thus delivering the processed items to the final customers before returning to the depot. During this stage, vehicles might visit other intermediate nodes for reloading new items. In some real‐life scenarios, this problem needs to be solved in just a few seconds or even milliseconds, which leads to the concept of “agile optimization.” This might be the case in some rescue operations using drones in humanitarian logistics, where every second can be decisive to save lives. In order to deal with this real‐time two‐echelon vehicle routing problem with pickup and delivery, an original constructive heuristic is proposed. This heuristic is able to provide a feasible and reasonably good solution in just a few milliseconds. The constructive heuristic is extended into a biased‐randomized algorithm using a skewed probability distribution to modify its greedy behavior. This way, parallel runs of the algorithm are able to generate even better results without violating the real‐time constraint. Results show that the proposed methodology generates competitive results in milliseconds, being able to outperform other heuristics from the literature.     

随机需求车辆路径问题及混合变邻域分散搜索算法求解

随机需求车辆路径问题(capacitated vehicle routing problem with stochastic demand,CVRPSD)是对带容量约束车辆路径问题(capacitated vehicle routing problem,CVRP)的扩展,需求不确定的特点使其较CVRP更复杂,对求解方法要求更高.基于先预优化后重调度思想,提出两阶段的混合变邻域分散搜索算法(variable neighborhood scatter search,VNSS)对该问题进行求解:预优化阶段构建随机机会约束规划模型,对客户点随机需求作机会约束确定型等价处理,生成最优预优化方案;重调度阶段采用新的点重优化策略进行线路调整,降低因失败点而产生的额外成本,减少对人工和车辆的占用.算例验证表明,随机机会约束模型和两阶段变邻域分散搜索算法在求解CVRPSD时较为有效,点重优化策略调整效果较佳.     

Fleet-sizing for multi-depot and periodic vehicle routing problems using a modular heuristic algorithm

In this paper, we address the problem of determining the optimal fleet size for three vehicle routing problems, i.e., multi-depot VRP, periodic VRP and multi-depot periodic VRP. In each of these problems, we consider three kinds of constraints that are often found in reality, i.e., vehicle capacity, route duration and budget constraints. To tackle the problems, we propose a new Modular Heuristic Algorithm (MHA) whose exploration and exploitation strategies enable the algorithm to produce promising results. Extensive computational experiments show that MHA performs impressively well, in terms of solution quality and computational time, for the three problem classes.     

基于暂态混沌神经网络的多车调度混合优化算法

探讨车辆调度问题的解决方法．提出一种用于求解带容量约束的多车调度问题（CVRP）的混合优化算法．该算法分为路线划分、构造初始解和改进解3个阶段：第1阶段用模糊C均值聚类算法将所有客户按车容量要求装车;第2阶段用暂态混沌神经网络方法对每条路线排序;第3阶段用禁忌搜索法改进得到的解．最后采用标准问题进行仿真计算,通过与其他算法的比较,说明该算法是求解CVRP问题可行且高效的方法．     

公司班车的协同车辆路径问题

考虑员工上下班时间及早晚高峰期影响班车行驶速度等因素,建立协同车辆路径问题的数学模型.针对蚁群优化算法的缺点,结合具有快速全局搜索能力的遗传算法,并自适应地改变信息素挥发因子,采用混沌搜索产生初始种群可以加速染色体向最优解收敛,平滑机制有助于对搜索空间进行更有效的搜索,构成混合自适应蚁群优化算法.应用该算法和蚁群优化算法对该模型求解,实验证明了构造算法在收敛速度和寻优结果两方面都优于蚁群优化算法.     

有能力约束回收车辆路径问题的食物链算法

介绍了有能力约束逆向物流回收车辆路径问题,设计了求解有能力约束逆向物流回收车辆路径问题的食物链算法;选取文献典型算例进行了仿真求解及比较分析,结果表明设计的食物链算法性能优于遗传算法、粒子群算法和量子进化算法。     

Heuristics for tactical time slot management: a periodic vehicle routing problem view

In this study, we consider a tactical problem where a time slot schedule for delivery service over a given planning horizon must be selected in each zone of a geographical area. A heuristic search evaluates each schedule selection by constructing a corresponding tactical routing plan of minimum cost based on demand and service time estimates. At the end, the schedule selection leading to the best tactical routing plan is selected. The latter can then be used as a blueprint when addressing the operational problem (i.e., when real customer orders are received and operational routes are constructed). We propose two heuristics to address the tactical problem. The first heuristic is a three‐phase approach: a periodic vehicle routing problem (PVRP) is first solved, followed by a repair phase and a final improvement phase where a vehicle routing problem (VRP) with time windows is solved for each period of the planning horizon. The second heuristic tackles the problem as a whole by directly solving a PVRP with time windows. Computational results compare the two heuristics under various settings, based on instances derived from benchmark instances for the VRP with time windows.     

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%.