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.     

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.     

随机需求车辆路径问题及其启发式算法

在对随机需求信息条件下的车辆路径问题进行策略分析基础上,提出解决此类问题的改进蚁群算法.分析对比不同策略下用蚁群算法优化的结果.其中给出机会约束下决策者的风险喜好对最终目标的影响.通过模拟实际随机需求的方法评价先验路径的优劣.与其它计算方法在同等条件下的比较证明所设计算法的优越性.同时得出对于不同统计特性的随机需求策略的选择方式.     

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.     

基于交叉熵法解决随机用户和需求车辆路径问题

提出一种解决随机用户和需求车辆路径问题（VRPSCD）的方法．针对目标函数的复杂性．设计一种基干Monte-Carlo抽样求解路径期望费用的有效方法;为提高标准交叉熵（CE）法性能，针对用于更新Markov转移矩阵关键路径，根据分位值改变大小，设计了自适应调整方法．计算结果验证了采用该方法解决此问题的鲁棒性和有效性．     

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.     

Memetic Algorithms for Parallel Code Optimization

Discovering the optimum number of processors and the distribution of data on distributed memory parallel computers for a given algorithm is a demanding task. A memetic algorithm (MA) is proposed here to find the best number of processors and the best data distribution method to be used for each stage of a parallel program. Steady state memetic algorithm is compared with transgenerational memetic algorithm using different crossover operators and hill-climbing methods. A self-adaptive MA is also implemented, based on a multimeme strategy. All the experiments are carried out on computationally intensive, communication intensive, and mixed problem instances. The MA performs successfully for the illustrative problem instances.     

A hybrid particle swarm optimization algorithm for the vehicle routing problem

This paper introduces a new hybrid algorithmic nature inspired approach based on particle swarm optimization, for successfully solving one of the most popular supply chain management problems, the vehicle routing problem. The vehicle routing problem is considered one of the most well studied problems in operations research. The proposed algorithm for the solution of the vehicle routing problem, the hybrid particle swarm optimization (HybPSO), combines a particle swarm optimization (PSO) algorithm, the multiple phase neighborhood search–greedy randomized adaptive search procedure (MPNS–GRASP) algorithm, the expanding neighborhood search (ENS) strategy and a path relinking (PR) strategy. The algorithm is suitable for solving very large-scale vehicle routing problems as well as other, more difficult combinatorial optimization problems, within short computational time. It is tested on a set of benchmark instances and produced very satisfactory results. The algorithm is ranked in the fifth place among the 39 most known and effective algorithms in the literature and in the first place among all nature inspired methods that have ever been used for this set of instances.     

An efficient variable neighborhood search heuristic for very large scale vehicle routing problems

In this paper, we present an efficient variable neighborhood search heuristic for the capacitated vehicle routing problem. The objective is to design least cost routes for a fleet of identically capacitated vehicles to service geographically scattered customers with known demands. The variable neighborhood search procedure is used to guide a set of standard improvement heuristics. In addition, a strategy reminiscent of the guided local search metaheuristic is used to help escape local minima. The developed solution method is specifically aimed at solving very large scale real-life vehicle routing problems. To speed up the method and cut down memory usage, new implementation concepts are used. Computational experiments on 32 existing large scale benchmarks, as well as on 20 new very large scale problem instances, demonstrate that the proposed method is fast, competitive and able to find high-quality solutions for problem instances with up to 20,000 customers within reasonable CPU times.     

Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

This paper presents an adaptive memetic algorithm to solve the vehicle routing problem with time windows (VRPTW). It is a well-known NP-hard discrete optimization problem with two objectives—to minimize the number of vehicles serving a set of geographically dispersed customers, and to minimize the total distance traveled in the routing plan. Although memetic algorithms have been proven to be extremely efficient in solving the VRPTW, their main drawback is an unclear tuning of their numerous parameters. Here, we introduce the adaptive memetic algorithm (AMA-VRPTW) for minimizing the total travel distance. In AMA-VRPTW, a population of solutions evolves with time. The parameters of the algorithm, including the selection scheme, population size and the number of child solutions generated for each pair of parents, are adjusted dynamically during the search. We propose a new adaptive selection scheme to balance the exploration and exploitation of the solution space. Extensive experimental study performed on the well-known Solomon’s and Gehring and Homberger’s benchmark sets confirms the efficacy and convergence capabilities of the proposed AMA-VRPTW. We show that it is very competitive compared with other state-of-the-art techniques. Finally, the influence of the proposed adaptive schemes on the AMA-VRPTW behavior and performance is investigated in a thorough sensitivity analysis. This analysis is complemented with the two-tailed Wilcoxon test for verifying the statistical significance of the results.     

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.     

基于Memetic算法的带时间窗车辆路径问题研究*

提出一种模拟文化进化的Memetic算法求解带时间窗的车辆路径问题。设计了一种实数编码方案,将离散的问题转为连续优化问题。采用邻域搜索帮助具备一定学习能力的个体提高寻优速度;采用禁忌搜索帮助部分个体跳出局部最优点,增强全局寻优性能。实验结果表明,该算法可以更有效地求出优化解,是带时间窗车辆路径问题的一种有效求解算法。     

Skewed general variable neighborhood search for the cumulative capacitated vehicle routing problem

The cumulative capacitated vehicle routing problem (CCVRP) is a relatively new version of the classical capacitated vehicle routing problem, and it is equivalent to a traveling repairman problem with capacity constraints and a homogeneous vehicle fleet, which aims to minimize the total arrival time at customers. Many real‐world applications can be modeled by this problem, such as the important application resulting from the humanitarian aid following a natural disaster. In this paper, two heuristics are proposed. The first one is a constructive heuristic to generate an initial solution and the second is the skewed variable neighborhood search (SVNS) heuristic. The SVNS algorithm starts with the initial solution. At each iteration, the perturbation phase and the local search phase are used to improve the solution of the CCVRP, and the distance function in acceptance criteria phase is used to improve the exploration of faraway valleys. This algorithm is applied to a set of benchmarks, and the comparison results show that the proposed algorithms provide better solutions than those reported in the previous literature on memetic algorithms and adaptive large neighborhood search heuristics.     

A memetic algorithm for the capacitated location-routing problem with mixed backhauls

The design of distribution networks is one of the most important problems in supply chain and logistics management. The main elements in designing a distribution network are location and routing decisions. As these elements are interdependent in many distribution networks, the overall system cost can decrease if location and routing decisions are simultaneously tackled. In this paper, we consider a Capacitated Location-Routing Problem with Mixed Backhauls (CLRPMB) which is a general case of the capacitated location-routing problem. CLRPMB is defined as finding locations of the depots and designing vehicle routes in such a way that pickup and delivery demands of each customer must be performed with the same vehicle and the overall cost is minimized. Since CLRPMB is an NP-hard problem, we propose a memetic algorithm to solve the problem. To evaluate the performance of the proposed approach, we conduct an experimental study and compare its results with the lower bounds obtained by the branch-and-cut algorithm on a set of instances derived from the literature. Computational results indicate that the proposed approach is able to find optimal or very good quality solutions in a reasonable computation time.     

考虑客户分类的随机时间车辆路径优化模型与算法

针对一类考虑客户分类、随机旅行时间、随机服务时间及时间窗约束的车辆路径问题构建了机会约束规划模型,该模型考虑两类客户（普通客户与优质客户）并通过添加机会约束条件确保优质客户获得准时服务的概率。同时,设计了变邻域迭代局部搜索算法,并给出了一种基于最小等待时间的初始解生成启发式规则。基于Solomon算例进行了多组仿真实验。仿真实验结果表明,所设计生成初始解的启发式规则是有效的;所给算法能够在短时间内找到确定问题和随机问题的近似最优解;客户比与车辆使用数目呈正相关关系。研究结果对解决资源有限条件下克服随机不确定性因素带来的不利影响、保证客户服务水平等问题有一定的参考意义。     

模糊需求车辆路径问题及其启发式算法

对模糊需求信息条件下的车辆路径问题进行策略分析，提出解决此类问题的改进蚁群算法。采用多蚁群协作，修改信息素更新规则，根据收敛要求动态调整主要参数等对蚁群算法进行改进，应用该方法解决机会约束策略和可能性策略下的模糊需求车辆路径问题。实验结果证明了改进算法对优化模糊需求车辆问题非常有效。     

多车种随机需求车辆路由问题的遗传算法

该文给出了多种类型车辆随机节点需求车辆路由问题(VRP)的遗传算法。将各个节点的需求作为一个随机数,同一条路径上车辆允许路由失败(routingfailure)的次数作为一个可控制的参数,对于节点需求服从给定的已知分布的VRP问题,给出了一些更简单实用的递推公式,并在此基础上通过巧妙地设计交配规则,得到遗传算法,并经实例运算对比,效果很好。     

An adaptive large neighborhood search heuristic for the cumulative capacitated vehicle routing problem

The cumulative capacitated vehicle routing problem (CCVRP) is a variation of the classical capacitated vehicle routing problem in which the objective is the minimization of the sum of arrival times at customers, instead of the total routing cost. This paper presents an adaptive large neighborhood search heuristic for the CCVRP. This algorithm is applied to a set of benchmark instances and compared with two recently published memetic algorithms.     

An effective memetic algorithm for the cumulative capacitated vehicle routing problem

The cumulative capacitated vehicle routing problem (CCVRP) is a transportation problem which occurs when the objective is to minimize the sum of arrival times at customers, instead of the classical route length, subject to vehicle capacity constraints. This type of challenges arises whenever priority is given to the satisfaction of the customer need, e.g. vital goods supply or rescue after a natural disaster. The CCVRP generalizes the NP-hard traveling repairman problem (TRP), by adding capacity constraints and a homogeneous vehicle fleet. This paper presents the first upper and lower bounding procedures for this new problem. The lower bounds are derived from CCVRP properties. Upper bounds are given by a memetic algorithm using non-trivial evaluations of cost variations in the local search. Good results are obtained not only on the CCVRP, but also on the special case of the TRP, outperforming the only TRP metaheuristic published.     

用于求解混合车辆路径问题的混合进化算法

文中研究了具有NP难度的混合车辆路径问题(Mixed Capacitated General Routing Problem,MCGRP),其是在基本车辆路径问题(Vehicle Routing Problem,VRP)的基础上通过添加限载容量约束及弧上的用户需求而衍生的。给定一列车辆数不限的车队,使车辆从站点出发向用户提供服务,服务完用户需求后仍返回站点;规定每辆车的总载重不能超过其载重量,且每个需求只能被一辆车服务且仅服务一次。MCGRP旨在求解每辆车的服务路线,使得在满足以上约束条件的情况下所有车辆的旅行消耗之和最小。混合车辆路径问题具有较高的理论价值和实际应用价值,针对该问题提出了一种高效的混合进化算法。该算法采用基于5种邻域算符的变邻域禁忌搜索来提高解的质量,并通过一种基于路径的交叉算符来继承解的优异性,从而有效地加速算法的收敛。在一组共计23个经典算例上的实验结果表明,该混合进化算法在求解混合车辆路径问题时是非常高效的。