军事物流系统中定位-运输路线安排模型及算法

考虑到战时物资需求的紧迫性和保障资源的有限性,从决策者的角度出发,以军事物流系统总体供应时间最短为目标,构建了两级军事配送网络的定位-运输路线安排模型,并给出一种启发式算法.算法分为两个阶段,首先利用蚁群算法和线性规划的方法解决运输路线安排问题,然后运用贪婪搜索算法解决军事物流配送中心选址问题.最终,将两种算法结合起来进行逐步搜索,从而得到模型的解,并运用实例说明了算法的有效性和可行性.     

人工蜂群算法在LRIP机会约束模型中的应用

针对现有研究中未考虑配送阶段客户随机需求的问题,本文采用在一定置信区间上满足客户需求的方法,描述这种客户需求不确定的约束,在此基础上,建立了选址-路径-库存问题(Location-Routing-Inventory Problem,LRIP)的机会约束模型。提出人工蜂群算法(Artificial Bee Colony algorithm,ABC)对该问题模型进行优化求解。结合问题特征和邻域知识,提出了一种基于矩阵的编码方法,构造了启发式初始化方法,设计了2种基于矩阵编码的交换策略,在此基础上构造了5种蜂群搜索算子。通过仿真实验,分析比较了初始化方法和5种搜索策略;同时将人工蜂群算法与两阶段法进行了比较,优化结果证明人工蜂群算法是求解LRIP问题的有效方法。     

满意度最大条件下的战时保障补给站选址问题研究

设施选址是装备保障系统构建过程中的重要决策步骤。文章首先分析了现代战争中装备保障设施尤其是终端补给站选址问题对形成装备保障快速反应能力的重要性,提出了以作战区域内所有作战单位的保障满意度最大为目标的有限补给站选址优化模型,作战单位对选址方案的保障满意度是从所需的资源数量和保障反应及时性两个方面进行考虑的。之后基于拉格朗日松弛算法设计了模型的启发式求解算法,实验结果表明该算法有效。     

两阶段模糊生产计划期望值模型

在现实的生产系统中,生产计划问题常常是-个确定的线性规划问题.但是,在许多的实际情况中,由于生产系统中不确定性因素的影响,带有常系数的线性规划模型不能合理地描述现实的决策环境.为了准确有效地描述生产决策环境,本文提出一类新的带有模糊参数的两阶段生产计划期望值模型并且讨论模型的一些基本性质.然后,讨论补偿函数的逼近并且设计-个基了:逼近方法、神经网络和遗传算法的启发式算法来求解这个两阶段模糊生产计划模型.最后,给出一个数值例子来表明所设计算法的可行性和有效性.     

基于两阶段启发式算法的物流配送选址-路径问题研究

为了解决配送中心选址与带时间窗的多中心车辆路径优化组合决策问题,利用双层规划法建立了配送中心选址与车辆路径安排的多目标整数规划模型,针对该模型的特点,采用两阶段启发式算法进行了求解。首先,通过基于聚集度的启发式算法对客户进行分类,确定了备选配送中心的服务范围;然后,基于双层规划法,以配送中心选址成本最小作为上层规划目标,以车辆配送成本最小作为下层规划目标,建立了多目标整数规划模型;最后,利用改进的蚁群算法进行了求解。通过分析实例数据和Barreto Benchmark算例的实验结果,验证了该模型的有效性和可行性。     

带时效性约束的配送中心多源选址问题研究

应用启发式算法求解带时效性约束的多源选址问题.分析物流配送的时效性问题,建立带时效性约束的配送中心多源选址模型.构造两步启发式算法:1)借助传统迭代算法,求解物流服务分配矩阵,把多源选址问题转化为单源选址问题;2)基于M ATLAB函数,设计优化程序,计算带时效性约束的单源选址模型.并给出算例,验证模型和算法的可行性.研究表明两步启发式算法是求解带时效性约束的物流配送中心多源连续选址问题的有效算法.     

不确定箱量下内河集装箱班轮航线动态配载决策

内河集装箱班轮运输中海关抽检可导致外贸箱箱量不断发生变化,班轮航线配载需要动态决策。基于滚动调度策略,将当前港口的配载决策按随机事件划分为多个阶段,以最小化班轮堆栈占用数量和相邻阶段间配载计划偏差为目标,构建单港口单阶段的配载决策模型,进而滚动实现班轮航线动态配载决策。基于大邻域搜索思想设计一种包含整数规划、破坏器与修复器的精确启发式算法,实现港口多阶段滚动配载。基于真实场景的算例研究表明,在优化堆栈占用数量方面,模型与算法之间差异不大,但在考虑相邻阶段间配载计划偏差时,算法的求解结果要优于模型。因此,模型与算法可用来辅助实现不确定箱量下内河集装箱班轮航线动态配载决策,且算法表现更优,可实现配载计划对不确定箱量的鲁棒吸收。     

物流中心选址模型及一种启发式算法

本建立了特流系统分析中的一个物流中心选址模型，它属非线性混合0－1规划。为克服求解上的困难，首先利用其特殊结构，建立了一个分解－过滤模式，进而对导出的可行子问题给出了一种实用的启发式算法。     

考虑资源约束和数量折扣的联合补货-选址库存协同优化研究

本文构建了一种基于联合补货策略的配送中心选址-库存协同优化新模型,该模型允许缺货,有资源约束且考虑数量折扣;同时设计了一种融合模拟退火思想的双种群独立进化的自适应差分算法(Adaptive Simulated Annealing Differential Algorithm,ASADE)对该模型进行求解,并通过算例与自适应差分算法、改进的蛙跳算法进行对比,证实了ASADE算法的有效性。最后进行了敏感性分析,讨论相关参数变动对总成本的影响,可为管理者更好决策提供有益的依据。     

考虑居民选择行为的应急避难场所选址问题研究

突发事件发生后,应急管理部门将面临选择启用哪些应急避难场所供居民避难的决策。与其它设施选址不同,处于有限理性状态下的居民可选择是否去避难以及去哪个避难所避难。为使未被服务的居民数量最小化,考虑居民的有限理性选择行为至关重要。为此,考虑居民避难距离及避难所吸引力等因素,基于竞争选址理论构建避难所对居民的效用函数。在此基础上,运用多项logit模型刻画居民的有限理性选择行为,建立基于有限理性选择行为的选址模型,并与基于效用最优选择行为和基于系统最优原则的选址模型进行对比以验证模型的合理性。为了高效地求解模型,设计了模拟退火算法和遗传算法两种启发式算法。算例分析结果验证了模型和算法的可行性和有效性,并揭示了居民理性程度、选址个数及财政预算均对选址结果有重要影响。     

考虑服务水平的逆向物流网络优化决策

针对电子产品的售后维修服务问题,建立了一个同时考虑成本和服务质量的多目标逆向物流网络优化模型;该问题是多目标的NP-hard问题,应用NSGA-II算法和多目标模拟退火算法(MOSA)两种多目标进化算法,对模型进行求解并对其求解的效果进行比较分析;多组算例测试结果表明,NSGA-II相比MOSA更具优势。     

回载可分的闭环供应链配送中的多车辆运输策略

本文从车辆路径的角度研究了具有一个配送中心、多台车辆结合前向物流配送和逆向物流回载的闭环供应链运输策略,考虑回收产品的不同形态和可分批运输的特点,引入库存限制和成本惩罚,建立并分析了问题的数学模型．通过引入参数2σ原则构造了先分组后组内运用基于TSP的插入算法进行优化调整的启发式求解方法．算例分析表明该策略是合理有效的．     

Selective capacitated location-routing problem with incentive-dependent returns in designing used products collection network

This paper addresses a generalization of the capacitated location-routing problem (CLRP) arising in the design of a collection network for a company engaged in collecting used products from customer zones. The company offers customers a financial incentive per unit of used products. This incentive determines the quantity of used products which are returned by customers. Moreover, it is not necessary for the company to visit all customer zones or to collect all returns in each visited customer zone. The objective is to simultaneously find the location of collection centers, the routes of vehicles, the value of incentive offered and the amount of used products collected from customer zones, so as to maximize the company's overall profit. We develop two mixed integer linear programming formulations of the problem and a heuristic algorithm based on iterated local search. Extensive computational experiments on this problem demonstrate the effectiveness of the proposed algorithm.     

Logistics network design for product recovery in fuzzy environment

The design of product recovery network is one of the important and challenging problems in the field of reverse logistics. Some models have been formatted by researchers under deterministic environment. However, uncertainty is inherent during the process of the practical product recovery. In order to deal with uncertainty, this paper employs a fuzzy programming tool to design the product recovery network. Based on different criteria, three types of optimization models are proposed and some properties of them are investigated. To solve the proposed models, we design a hybrid intelligent algorithm which integrates fuzzy simulation and genetic algorithm. Finally, several numerical examples are presented to illustrate the effectiveness of the proposed models and algorithm.     

A genetic algorithm for a single product network design model with lead time and safety stock considerations

We consider a two-stage supply chain with a production facility that replenishes a single product at retailers. The objective is to locate distribution centers in the network such that the sum of facility location, pipeline inventory, and safety stock costs is minimized. We explicitly model the relationship between the flows in the network, lead times, and safety stock levels. We use genetic algorithms to solve the model and compare their performance to that of a Lagrangian heuristic developed in earlier work. A novel chromosome representation that combines binary vectors with random keys provides solutions of similar quality to those from the Lagrangian heuristic. The model is then extended to incorporate arbitrary demand variance at the retailers. This modification destroys the structure upon which the Lagrangian heuristic is based, but is easily incorporated into the genetic algorithm. The genetic algorithm yields significantly better solutions than a greedy heuristic for this modification and has reasonable computational requirements.     

An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty

In this work the design of a reverse distribution network is studied. Most of the proposed models on the subject are case based and, for that reason, they lack generality. In this paper we try to overcome this limitation and a generalized model is proposed. It contemplates the design of a generic reverse logistics network where capacity limits, multi-product management and uncertainty on product demands and returns are considered. A mixed integer formulation is developed which is solved using standard B&B techniques. The model is applied to an illustrative case.     

A new bidirectional search algorithm with shortened postprocessing

For finding a shortest path in a network bidirectional A^∗ is a widely known algorithm. This algorithm distinguishes between the main phase and the postprocessing phase. The version of bidirectional A^∗ that is considered the most appropriate in literature hitherto, uses a so-called balanced heuristic estimate. This type of heuristic is chosen, as it accounts for a short postprocessing phase. In this paper, we do not restrict ourselves any longer to balanced heuristics. First, we introduce an algorithm containing a new method for the postprocessing phase, reducing this phase considerably for non-balanced heuristics. For a balanced heuristic the new algorithm is nearly equivalent to the existing versions of bidirectional A^∗. An obvious choice for a non-balanced heuristic turns out to be superior in terms of storage space and computation time. Second, we show that the main phase on its own, when using this non-balanced heuristic estimate, is a useful algorithm, which provides us quickly with a feasible approximation.     

考虑退货不确定性的多层次多站点逆向物流网络选址优化研究

在退货量不确定情况下,为追求电商企业逆向物流网络成本最小化,建立了多层次多站点的混合整数规划模型。将目标区域进行网格化处理,通过球上距离公式计算相邻备选站点、不同层次备选点间的运输距离,使用蒙特卡罗法模拟退货量,并设计了双染色体编码的遗传算法进行求解。算例验证了模型算法的可行性,并且得出了逆向物流网络成本最小时,快递站点和退货处理中心的选址,快递站点的选址与退货量大小存在对应关系,研究客户退货量会使物流网络选址得到优化。     

A multi-period network design problem for cellular telecommunication systems

Mathematical Programming models for multi-period network design problems, which arise in cellular telecommunication systems are presented. The underlying network topologies range from a simple star to complex multi-layer Steiner-like networks. Linear programming, Lagrangian relaxation, and branch-and-cut heuristics are proposed and a polynomial-bounded heuristic based on an interior point linear programming implementation is described. Extensive computational results are presented on a number of randomly generated problem sets and the performance of the heuristic(s) are compared with an optimal branch-and-bound algorithm.