基于两阶段改进遗传算法的一类特殊LRP问题研究

提出了一种带有随机开关的两阶段改进遗传算法并应用于集成化物流中的定位 运输路线安排 (LRP)优化问题. 该方法采用随机开关控制遗传算法中的变异运算, 实现了空间的有效搜索, 并且在一定程度上, 避免了“局部最优现象”的发生. 通过计算机仿真实验, 证明了该算法求解LRP问题的有效性和准确性.     

定位—运输路线安排问题的遗传算法研究

定位—运输路线安排问题(LRP)是分销网络设计和物流管理决策中的难题。由于LRP是NP-complete问题,对它的求解方法大多局限于将其分解为定位—分配问题和车辆运输路线安排问题,或者是基于这种分解思想。文章通过对遗传算法(GA)中树编码、免疫遗传算法以及GA阶段进化策略深入地分析和研究,构建了定位—运输路线安排问题的遗传算法,它与以往算法最大的不同点就是并没有基于两阶段求解的思路,而是将LRP的解看作一个整体,从而减小了在进化过程中停滞于局部最优解的概率,提高了GA的计算效率和计算速度。文中详细叙述了针对LRP问题的树编码、交叉、变异、爬山、免疫、合并小路线等各种算子设计过程,并利用一实例来验证算法的可行性。该算法为LRP问题以及相关大规模组合优化问题的求解开辟了一个新的思路,同时也为GA中树编码在实际中应用做了有益的尝试。     

基于模糊优化的物流配送路径(MLRP)问题研究

研究采用嵌入模糊决策规则的遗传算法（即模糊优化方法）求解物流配送多目标定位-运输路线安排问题（MLRP）,重点考虑了时间和运输成本两个目标的MLRP的求解方法.该算法分成3个阶段,首先利用遗传算法对初始种群搜索选择优化配送路径;然后应用配送网络调度算法综合评价来确定配送路径中的关键路径和非关键路径;最后根据模糊决策规则计算其各个调度相应的指标,并对已挑选出来的染色体中的某些位基因进行调整,以提高算法的收敛性.计算机仿真结果证明了将此混合算法用于求解中、小规模物流配送问题的有效性.     

震后过渡阶段应急物流系统的定位运输路线安排问题研究

针对震后过渡阶段中,回收救援物资与保护灾区环境的问题,在满足灾区民众基本生活需求的前提下,提出了一个正逆向结合的应急物流设施定位-运输路线安排问题(LRP)模型.首先,结合回收物资可分批运输的特点,建立以应急系统耗费总时间最小为目标函数的数学模型;然后,利用两阶段启发式算法对模型进行求解;最后,算例分析验证了模型和算法的可行性.实验结果表明,与传统单向LRP模型相比,所提方法的目标函数值减少了51%.所提模型能够有效提高应急物流系统运行效率,并为应急管理部门提供辅助决策支持.     

模糊需求下物流系统CLRIP 问题研究

从物流系统集成的角度出发．考虑到客户需求的模糊性，建立了多仓库单级物流配送系统中的设施选址、车辆运输路线安排、库存控制的集成优化模型．用来解决在给定的多个潜在设施点中选出一系列设施的位置．并确定巡回运输路线．同时基于客户所采用的单时期模糊需求存贮策略确定其最佳订货量，并给出了求解该模型的启发式算法．最后通过实例计算证明了上述模型和算法的有效性．     

定位-运输路线安排问题的改进离散粒子群优化算法

定位-运输路线安排问题(LRP)是集成物流中的一个NP-hard难题,为求解一类特殊的LRP问题,提出改进的离散粒子群优化算法.该方法采用整体优化的思想,将LAP和VRP集成在一起.通过合适的粒子编码方式,并改进粒子的运动方程,引入相应的变异算子和趋同扰动算子等,使得算法的适用性和性能获得了改善.通过仿真实验及与另2个典型算法的比较分析,证明了该算法的有效性.     

物流配送路径多目标优化的聚类-改进遗传算法

探讨运输车辆路线安排调度问题的解决方法，提出一种先用优先级综合聚类分析法将客户分类，再用带有控制开关系统的改进遗传算法求解多目标VRP的优化方法。构造了一种随机开关，以此控制遗传算法中的变异运算，增加了群体的多样性，避免了遗传算法中“局部最优现象”的发生。计算机仿真实验证明了该算法的有效性。     

震后随机动态LRP多目标优化模型及算法*

为了在地震灾区快速配送救灾物资,建立了一个多产品多运输方式的随机动态应急配送中心定位—运输路线安排问题的多目标优化模型,据此得出不同阶段应急配送中心的定位以及救援物资运输路线安排决策方案。根据该模型的特点,提出一种基于动态规划和权重系数变换法的改进遗传算法,并运用罚函数法处理模型中的约束条件。算例分析表明了该模型和算法的有效性,能为应急管理部门提供辅助决策支持。     

航空紧急配送中的随机LRP模型及算法

针对震后紧急响应阶段路网中断和救援物资需求不确定性,建立航空物流中的随机定位—路线安排问题（LRP）模型,据此进行震后应急救援过程中救灾物资集散点和应急配送中心的定位以及救援物资空运路线安排的联合决策。根据该模型的特点,提出了一种改进的遗传算法,采用特定实值编码、罚函数法和物资需求量分割策略处理模型中的约束条件。算例分析结果表明,该模型和算法可以有效解决震后应急物流系统中的应急设施定位—分配和路线安排问题。     

定位—路线问题的遗传禁忌混合优化算法

研究了带软时间窗的定位—路线问题的遗传禁忌混合优化算法,该算法同时兼顾了定位—路线问题中的定位—配给和车辆路线安排两个子问题。给出的遗传算法与禁忌搜索算法的混合策略、遗传编码和相应的遗传操作方式,有效地提高了算法的求解效率和求解质量。最后,通过实验证明了算法的可行性和有效性。     

A biased‐randomized metaheuristic for the capacitated location routing problem

The location routing problem (LRP) involves the three key decision levels in supply chain design, that is, strategic, tactical, and operational levels. It deals with the simultaneous decisions of (a) locating facilities (e.g., depots or warehouses), (b) assigning customers to facilities, and (c) defining routes of vehicles departing from and finishing at each facility to serve the associated customers’ demands. In this paper, a two‐phase metaheuristic procedure is proposed to deal with the capacitated version of the LRP (CLRP). Here, decisions must be made taking into account limited capacities of both facilities and vehicles. In the first phase (selection of promising solutions), we determine the depots to be opened, perform a fast allocation of customers to open depots, and generate a complete CLRP solution using a fast routing heuristic. This phase is executed several times in order to keep the most promising solutions. In the second phase (solution refinement), for each of the selected solutions we apply a perturbation procedure to the customer allocation followed by a more intensive routing heuristic. Computational experiments are carried out using well‐known instances from the literature. Results show that our approach is quite competitive since it offers average gaps below 0.4% with respect to the best‐known solutions (BKSs) for all tested sets in short computational times.     

A two-phase hybrid heuristic algorithm for the capacitated location-routing problem

In this paper, we propose a two-phase hybrid heuristic algorithm to solve the capacitated location-routing problem (CLRP). The CLRP combines depot location and routing decisions. We are given on input a set of identical vehicles (each having a capacity and a fixed cost), a set of depots with restricted capacities and opening costs, and a set of customers with deterministic demands. The problem consists of determining the depots to be opened, the customers and the vehicles to be assigned to each open depot, and the routes to be performed to fulfill the demand of the customers. The objective is to minimize the sum of the costs of the open depots, of the fixed cost associated with the used vehicles, and of the variable traveling costs related to the performed routes. In the proposed hybrid heuristic algorithm, after a Construction phase (first phase), a modified granular tabu search, with different diversification strategies, is applied during the Improvement phase (second phase). In addition, a random perturbation procedure is considered to avoid that the algorithm remains in a local optimum for a given number of iterations. Computational experiments on benchmark instances from the literature show that the proposed algorithm is able to produce, within short computing time, several solutions obtained by the previously published methods and new best known solutions.     

An optimization algorithm for the inventory routing problem with continuous moves

The typical inventory routing problem deals with the repeated distribution of a single product from a single facility with an unlimited supply to a set of customers that can all be reached with out-and-back trips. Unfortunately, this is not always the reality. We focus on the inventory routing problem with continuous moves, which incorporates two important real-life complexities: limited product availabilities at facilities and customers that cannot be served using out-and-back tours. We need to design delivery tours spanning several days, covering huge geographic areas, and involving product pickups at different facilities. We develop an integer programming based optimization algorithm capable of solving small to medium size instances. This optimization algorithm is embedded in local search procedure to improve solutions produced by a randomized greedy heuristic. We demonstrate the effectiveness of this approach in an extensive computational study.     

A two-phase metaheuristic for the cumulative capacitated vehicle routing problem

The cumulative capacitated vehicle routing problem, which aims to minimize the total arrival time at customers, is a relatively new variant of vehicle routing problem. It can be used to model many real-world applications, e.g., the important application arisen from the humanitarian aid after a natural disaster. In this paper, an approach, called two-phase metaheuristic, is proposed to deal with this problem. This algorithm starts from a solution. At each iteration, two interdependent phases use different perturbation and local search operators for solution improvement. The effectiveness of the proposed algorithm is empirically investigated. The comparison results show that the proposed algorithm is promising. Moreover, for nine benchmark instances, the two-phase metaheuristic can find better solutions than those reported in the previous literature.     

Multiscale production routing in multicommodity supply chains with complex production facilities

In this work, we introduce the multiscale production routing problem (MPRP), which considers the coordination of production, inventory, distribution, and routing decisions in multicommodity supply chains with complex continuous production facilities. We propose an MILP model involving two different time grids. While a detailed mode-based production scheduling model captures all critical operational constraints on the fine time grid, vehicle routing is considered in each time period of the coarse time grid. In order to solve large instances of the MPRP, we propose an iterative MILP-based heuristic approach that solves the MILP model with a restricted set of candidate routes at each iteration and dynamically updates the set of candidate routes for the next iteration. The results of an extensive computational study show that the proposed algorithm finds high-quality solutions in reasonable computation times, and in large instances, it significantly outperforms a standard two-phase heuristic approach and a solution strategy involving a one-time heuristic pre-generation of candidate routes. Similar results are achieved in an industrial case study, which considers a real-world industrial gas supply chain.     

An adaptive large neighborhood search heuristic for Two-Echelon Vehicle Routing Problems arising in city logistics

In this paper, we propose an adaptive large neighborhood search heuristic for the Two-Echelon Vehicle Routing Problem (2E-VRP) and the Location Routing Problem (LRP). The 2E-VRP arises in two-level transportation systems such as those encountered in the context of city logistics. In such systems, freight arrives at a major terminal and is shipped through intermediate satellite facilities to the final customers. The LRP can be seen as a special case of the 2E-VRP in which vehicle routing is performed only at the second level. We have developed new neighborhood search operators by exploiting the structure of the two problem classes considered and have also adapted existing operators from the literature. The operators are used in a hierarchical scheme reflecting the multi-level nature of the problem. Computational experiments conducted on several sets of instances from the literature show that our algorithm outperforms existing solution methods for the 2E-VRP and achieves excellent results on the LRP.     

一种多目标不等面积设施布局问题的启发式算法

多目标不等面积设施布局问题（UA-FLP）是将一些不等面积设施放置在车间内进行布局,要求优化多个目标并满足一定的限制条件。以物料搬运成本最小和非物流关系强度最大来建立生产车间的多目标优化模型,并提出一种启发式算法进行求解。算法采用启发式布局更新策略更新构型,通过结合基于自适应步长梯度法的局部搜索机制和启发式设施变形策略来处理设施之间的干涉性约束。为了得到问题的Pareto最优解集,提出了基于Pareto优化的局部搜索和基于小生境技术的全局优化方法。通过两个典型算例对算法性能进行测试,实验结果表明,所提出的启发式算法是求解多目标UA-FLP的有效方法。