三维装载与CVRP 联合多目标优化问题的模型及算法

提出三维装载与CVRP联合多目标优化问题(3LCVRPMO)模型,该模型在三维装载约束下的CVRP问题(3LCVRP)的基础上,考虑了配送车辆数目及路径总距离两个目标函数.在权衡装箱和路径优化两个优化过程的基础上,构建了多阶段/两层混合算法架构(MSOTLH)及其算法,并对路径优化偏好的3LCVRPMO问题进行求解.基于3LCVRP问题相关算例的数据实验结果表明,所提出的3LCVRPMO模型及MSOTLH算法是有效的.     

三阶段优化算法求解带三维装载约束的MDVRP北大核心CSCD

对带三维装载约束的多车场车辆路径问题,以最小化车辆行驶总里程为优化目标,建立问题模型,并提出一种三阶段优化算法进行求解。第一阶段设计带循环平衡的K-medoids聚类算法,将原问题分解成多个带三维装载约束限制的车辆路径子问题。第二阶段提出一种双层结构的超启发式蚁群算法用于求解各子问题,以确定各车辆的配送路径。在该算法中,低层设计9种启发式操作,并将其所构成的排列作为高层个体;同时,高层采用蚁群算法更新高层个体,以引导算法搜索方向。第三阶段以第二阶段所得阶段解作为初始解,设计组合启发式装箱算法对带容积约束的装箱过程进行优化,进而将第二、三阶段确定的解合并为原问题的解。最后,仿真实验和算法比较验证了所提算法的有效性。     

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。     

同时取送货的三维装载约束下车辆路径问题

同时取货和送货的三维装载约束下车辆路径问题（3L-VRPSDP）是经典车辆路径问题（VRP）的一个扩展,在3L-VRPSDP中,顾客可能要求同时取货和送货,而且货物装载要考虑其三维大小、先进后出、底面支撑等装载特性。针对这类问题,提出了基于左底优先和最大接触面优先的装载算法,在装载时要考虑其配送特征;在此基础上,设计了对应的禁忌搜索算法对初始结果进行优化,通过实验验证了算法的有效性。     

求解三维装载约束下车辆路径问题的混合禁忌搜索算法

三维装载约束下车辆路径问题是车辆路径问题集合中极为复杂的问题。针对这一问题,提出了三种混合禁忌搜索算法。该算法首先设计了空间处理方式,通过在初始解构造阶段采用不同的装载规则来实现客户货物的装载,然后引入禁忌搜索算法对解空间进行搜索。最后,扩展了Solomon的标准用例对三种算法进行了实验,实现结果显示提出的算法是求解该问题的有效算法,同时其中一种算法相对而言具有一定的优势。     

多中心联合配送模式下集货需求随机的VRPSDP问题

针对多中心联合配送模式下集货需求随机的同时配集货车辆路径问题(MDVRPSDDSPJD), 构建了两阶段MDVRPSDDSPJD模型. 预优化阶段基于随机机会约束机制以及车载量约束为客户分配车辆, 生成预优化方案; 重优化阶段采用失败点重优化策略对服务失败点重新规划路径. 根据问题特征, 设计了自适应变邻域文化基因算法(Adaptive memetic algorithm and variable neighborhood search, AMAVNS), 针对文化基因算法易早熟、局部搜索能力弱等缺陷, 将变邻域搜索算法的深度搜索能力运用到文化基因算法的局部搜索策略中, 增强算法的局部搜索能力; 提出自适应邻域搜索次数策略和自适应劣解接受机制平衡种群进化所需的广度和深度. 通过多组算例验证了提出模型及算法的有效性. 研究成果不仅深化和拓展了VRP (Vehicle routing problem)相关理论研究, 也为物流企业制定车辆调度计划提供一种科学合理的方法.     

基于禁忌搜索的三维装载车辆路径问题研究

针对三维装载约束下的车辆路径问题(VRP),在考虑车辆容量、三维装载、物品装卸顺序、最小支持面和物品是否易碎等约束的情况下,提出一种求解该问题的禁忌搜索算法,其中包括2种三维装载算法、2种初始解构建算法、禁忌搜索邻域结构以及导向禁忌搜索机制。实验结果表明,该算法能够有效求解三维装载约束的VRP,且求解精度较高。     

农村电商物流下无人机与车辆协同配送路径优化研究

无人机配送正在成为解决物流末端配送难题的重要手段。无人机与车辆协同配送模式克服了无人机配送能力不足、安全性不高的弊端，是无人机参与配送的重要途径之一。针对农村电商物流“最后一公里”配送难、配送贵问题，考虑无人机与车辆协同方式、多无人机多包裹配送等约束，以配送成本最小化为目标构建混合整数规划模型并提出一种两阶段算法对无人机与车辆协同配送路径优化问题进行求解。第一阶段通过带约束的自适应K-means算法确定车辆停靠点范围，第二阶段设计爬山算子与分裂算子改进遗传算法，求得无人机与车辆配送路径。最后，通过算例实验验证了模型和算法的可行性与有效性。研究成果有望为农村电商物流末端配送降本增效提供新思路和参考价值。     

考虑随机旅行时间与二维装载约束的越库配送车辆路径优化

面向越库配送模式下二维装载和车辆路径联合优化,考虑现实配送过程的不确定性因素,提出考虑随机旅行时间和二维装载约束的越库配送车辆路径问题.基于蒙特卡洛模拟与场景分析方法,建立以运输成本、车辆固定成本以及时间窗期望惩罚成本之和最小化为目标的带修正随机规划模型.继而根据问题特征,设计改进的自适应禁忌搜索算法和基于禁忌搜索的多重排序最佳适应装箱算法进行求解.其中,改进的自适应禁忌搜索算法在禁忌搜索算法的基础上引入自适应机制,对不同邻域算子进行动态选择,并提出基于移除-修复策略的多样性机制以增强算法的寻优能力.数值实验表明,所提出的模型与方法能够有效求解考虑随机旅行时间和二维装载约束的越库配送车辆路径问题,自适应与多样性机制能一定程度上增强算法的全局搜索能力.     

基于强化学习的电动车路径优化研究

针对有路径总时长约束、载重量约束和电池容量约束的电动车路径优化问题（EVRP）,考虑其途中可前往充电站充电的情境,构建以最小化路径总长度为目标的数学模型,提出一种基于强化学习的求解算法RL-EVRP。该算法用给定的分布生成训练数据,再通过策略梯度法训练模型,并保证在训练过程中路径合法即可。训练得到的模型可用于解决其他数据同分布的问题,无须重新训练。通过仿真实验及与其他算法的对比,表明RL-EVRP算法求解的路径总长度更短、车辆数更少,也表明强化学习可成功运用于较复杂的组合优化问题中。     

A simheuristic approach for the two-dimensional vehicle routing problem with stochastic travel times

The two-dimensional vehicle routing problem (2L-VRP) is a realistic extension of the classical vehicle routing problem in which customers’ demands are composed by sets of non-stackable items. Examples can be found in real-life applications such as the transportation of furniture or industrial machinery. Often, it is necessary to consider stochastic travel times due to traffic conditions or customers availability. However, there is a lack of works discussing stochastic versions of the 2L-VRP. This paper offers a model of the 2L-VRP with stochastic travel times that also includes penalty costs generated by overtime. To solve this stochastic and non-smooth version of the 2L-VRP, a hybrid simheuristic algorithm is proposed. Our approach combines Monte Carlo simulation, an iterated local search framework, and biased-randomised routing and packing heuristics. Our algorithm is tested on an extensive benchmark, which extends the deterministic one for the 2L-VRP with unrestricted and non-oriented loading.     

面向不同目标偏好的CVRP多目标模型及其求解方法*

为了更有效地求解车辆路径问题、全方位地评估物流运输成本,本文提出了面向不同目标偏好的车载能力约束车辆路径问题(CVRP)的多目标优化模型(MOCVRPFDTP),其包括三种不同的偏好结构：装载与CVRP联合优化、绝对最小车辆数偏好及路径优化偏好。为了求解该模型,本文设计了算法架构及具体算法。在实验中,该模型及其求解方法对CVRP国际标准算例VRPLIB的测试结果,显示了令人满意的性能,并且它更适用于实际CVRP问题的求解。     

A parallel evolutionary algorithm for the vehicle routing problem with heterogeneous fleet

Nowadays genetic algorithms stand as a trend to solve NP-complete and NP-hard problems. In this paper, we present a new hybrid metaheuristic which uses parallel genetic algorithms and scatter search coupled with a decomposition-into-petals procedure for solving a class of vehicle routing and scheduling problems. The parallel genetic algorithm presented is based on the island model and its performance is evaluated for a heterogeneous fleet problem, which is considered a problem much harder to solve than the homogeneous vehicle routing problem.     

垃圾分类收运路径问题的新型混合蚁群算法求解

针对垃圾分类收运路径问题,考虑车辆装载容量约束、硬时间窗约束、装载率对成本的影响等条件下,以最小化运输成本和车辆固定成本为目标建立了数学模型。将考虑时间吻合度因子和车容量利用率因子的改进蚁群算法与混沌电磁场优化算法进行动态融合,并结合2-opt和两点交换的局部搜索方法,提出一种以改进蚁群算法为外部框架,混沌电磁场优化算法为内部模块的新型混合蚁群算法对城市生活垃圾分类收运问题进行求解。根据算法间优势互补的思想,利用两种算法的优点来弥补单个算法的缺陷,使其成功应用于该问题。最后,用车辆路径问题标准测试集和上海市杨浦区的数据作为实例进行测试与对比,验证了模型的正确性以及算法的有效性与优化能力。     

Artificial intelligence heuristics in solving vehicle routing problems with time window constraints

This paper describes the authors’ research on various heuristics in solving vehicle routing problem with time window constraints (VRPTW) to near optimal solutions. VRPTW is NP-hard problem and best solved to near optimum by heuristics. In the vehicle routing problem, a set of geographically dispersed customers with known demands and predefined time windows are to be served by a fleet of vehicles with limited capacity. The optimized routines for each vehicle are scheduled as to achieve the minimal total cost without violating the capacity and time window constraints. In this paper, we explore different hybridizations of artificial intelligence based techniques including simulated annealing, tabu search and genetic algorithm for better performance in VRPTW. All the implemented hybrid heuristics are applied to solve the Solomon's 56 VRPTW with 100-customer instances, and yield 23 solutions competitive to the best solutions published in literature according to the authors’ best knowledge.     

A biased‐randomized algorithm for the two‐dimensional vehicle routing problem with and without item rotations

This paper proposes an efficient algorithm, with a reduced number of parameters, for solving the two‐dimensional loading‐capacitated vehicle routing problem (2L‐CVRP). This problem combines two of the most important issues in logistics, that is, vehicle routing and packing problems. Our approach contemplates unrestricted loading including the possibility of applying 90° rotations to each rectangular‐shaped item while loading it into the vehicle, which is a realistic assumption seldom considered in the existing literature. The algorithm uses a multistart approach that is designed to avoid local minima and also to make the algorithm an easily parallelizable one. At each restart, a biased randomization of a savings‐based routing algorithm is combined with an enhanced version of a classical packing heuristic to produce feasible good solutions for the 2L‐CVRP. The proposed algorithm has been compared with the classical benchmarks for two different 2L‐CVRP variants, that is, with and without item rotations. Experimental results show that our approach outperforms several best‐known solutions from previous work, both in terms of quality and the computational time needed to obtain them.     

强化学习在车辆路径问题中的研究综述

车辆路径问题是物流运输优化中的核心问题,目的是在满足顾客需求下得到一条最低成本的车辆路径规划。但随着物流运输规模的不断增大,车辆路径问题求解难度增加,并且对实时性要求也不断提高,已有的常规算法不再适应实际要求。近年来,基于强化学习算法开始成为求解车辆路径问题的重要方法,在简要回顾常规方法求解车辆路径问题的基础上,重点总结基于强化学习求解车辆路径问题的算法,并将算法按照基于动态规划、基于价值、基于策略的方式进行了分类;最后对该问题未来的研究进行了展望。     

A two-stage heuristic method for vehicle routing problem with split deliveries and pickups

The vehicle routing problem （VRP） is a well-known combinatorial optimization issue in transportation and logistics network systems. There exist several limitations associated with the traditional VRP. Releasing the restricted conditions of traditional VRP has become a research focus in the past few decades. The vehicle routing problem with split deliveries and pickups （VRPSPDP） is particularly proposed to release the constraints on the visiting times per customer and vehicle capacity, that is, to allow the deliveries and pickups for each customer to be simultaneously split more than once. Few studies have focused on the VRPSPDP problem. In this paper we propose a two-stage heuristic method integrating the initial heuristic algorithm and hybrid heuristic algorithm to study the VRPSPDP problem. To validate the proposed algorithm, Solomon benchmark datasets and extended Solomon benchmark datasets were modified to compare with three other popular algorithms. A total of 18 datasets were used to evaluate the effectiveness of the proposed method. The computational results indicated that the proposed algorithm is superior to these three algorithms for VRPSPDP in terms of total travel cost and average loading rate.     

多配送中心车辆路径问题的狼群算法

针对多配送中心动态启用和车辆的合理分配,文章首先建立了以总路径长度最小为目标函数的多配送中心车辆路径问题的数学模型;其次,根据多配送中心车辆路径问题的具体特征,模拟狼群捕食行为设计了求解该问题的狼群算法;最后,应用狼群算法求解测试算例,并将其计算结果与几种常见智能优化算法的计算结果进行比较,验证了狼群算法求解多配送中心车辆路径问题的可行性与有效性。