物流系统中心选址模型简介

1、Facility location models for distribution system design物流系统设计的选址模型.IntroductionTypes of modelsGeneral methods. The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution syste

2、m design. Introduction. Industrial firms must locate fabrication(制造厂) and assembly plants(组装厂) as well as warehouses(仓库). Stores have to be located by retail outlets(零售网点). The ability to manufacture and market its products is dependent in part on the location of the facilities. Similarly, governmen

3、t agencies have to decide about the location of offices, schools, hospitals, fire stations, etc. In every case, the quality of the services depends on the location of the facilities in relation to other facilities.Types of models The problem of locating facilities is not new to the operations resear

4、ch community(运筹学); the challenge of where to best site facilities has inspired a rich, colorful and ever growing body of literature. To cope with the multitude of applications(众多运用) encountered in the business world and in the public sector, an ever expanding family of models has emerged.Facility lo

5、cation models can be broadly classified as follows:The shape or topography of the set of potential plants yields models in the plane, network location models(网络选址模型), and discrete location(离散选址) or mixed-integer programming models(混合正数规划模型), respectively.Objectives(目的函数) may be either of the minsum

6、or the minmax type. Minsum models are designed to minimize average distances while minmax models have to minimize maximum distances. Predominantly(此外), minsum models embrace location problems of private companies while minmax models focus on location problems arising in the public sector.Models with

7、out capacity constraints do not restrict(限制) demand allocation. If capacity constraints for the potential sites have to be obeyed demand has to be allocated carefully. In the latter case we have to examine whether single-sourcing(单来源) or multiple-sourcing(多来源) is essential.Single-stage models(单阶段模型)

8、 focus on distribution systems covering only one stage explicitly. In multi-stage models(多阶段模型) the flow of goods comprising several hierarchical(层次) stages has to be examined.Single-product models(单产品模型) are characterized by the fact that demand, cost and capacity for several products can be aggreg

9、ated to a single homogeneous product. If products are inhomogeneous their effect on the design of the distribution system has to be analyzed, viz. multi-product models(多产品模型) have to be studied.Location models base on the assumption that demand is inelastic(无弹性的), that is, demand is independent of s

10、patial decisions. If demand is elastic(弹性的) the relationship between, e.g., distance and demand has to be taken into account explicitly. In the latter case cost minimization (本钱最小)has to be replaced through, for example, revenue maximization(收益最大).Static models (静态模型)try to optimize system performan

11、ce(性能) for one representative(代表) period. By contrast dynamic models(动态模型) reflect data (cost, demand, capacities, etc.) varying over time within a given planning horizon.In practice model(实际模型) input is usually not known with certainty. Data are based on forecasts and, hence, are likely to be uncer

12、tain. As a consequence, we have either deterministic models (确定模型)if input is (assumed to be) known with certainty or probabilistic models (概率模型) if input is subject to uncertainty.In classical models the quality of demand allocation is measured on isolation for each pair of supply and demand points

13、. Unfortunately, if demand is satisfied through delivery tours(运输,投递) then, for instance, delivery cost cannot be calculated for each pair of supply and demand points separately. Combined location/routing models(选址/道路模型) elaborate on this interrelationship.General methodsAHP (Analytic Hierarchy Proc

14、ess) 层次分析法Fuzzy Clustering 模糊聚类法Cross-median method交叉中值法gravity method重心法P-median method P-中值法Systemic arithmetic 系统模拟法Genetic algorithm (GA) 遗传算法The shortest path method最短途径法Simulated Annealing (SA) 模拟退火算法.The Analytic Hierarchy Process (AHP) is a structured technique for dealing with complex decii

15、sion. Rather than prescribing a correct decision, the AHP helps the decision makers find one that best suits their goal and their understanding of the problem. Based on mathematics and psychology, the AHP was developed by Thomas L. Saaty in the 1970s and has been extensively studied and refined sinc

16、e then. It provides a comprehensive(全面) and rational framework合理的框架 for structuring a decision problem构造化决策问题, for representing and quantifying its elements, for relating those elements to overall goals, and for evaluating alternative solutions. It is used around the world in a wide variety of decis

17、ion situations, in fields such as government, business, industry, healthcare, and education.AHP.Fuzzy ClusteringFuzzy clustering is a class of algorithms for cluster analysis in which the allocation of data points to clusters is not hard (all-or-nothing) but fuzzy in the same sense as fuzzy logic.In

18、 hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster. In fuzzy clustering (also referred to as soft clustering), data elements can belong to more than one cluster, and associated with each element is a set of membership levels(隶属关系). These

19、indicate the strength of the association between that data element and a particular cluster. Fuzzy clustering is a process of assigning these membership levels, and then using them to assign data elements to one or more clusters.gravity method总运费=设备与客户之间的直线间隔(欧几里德间隔)需求量 对上式分别对x，y求偏微分，可以求出下面的一对隐含有最优解

20、的等式，运用这两个等式经过迭代的方法分别对x，y进展求解，即可得最优解。.Cross-median method总费用=设备到需求点的折线间隔(城市间隔)需求量上述目的函数可以用两个互不相关的部分来表述：其中: 最优位置是由如下坐标组成的点:xs是在x方向的一切的权重wi的中值点, ys是在y方向的一切的权重wi的中值点。. The genetic algorithm (GA) is a search heuristic(启发式) that mimics(模拟) the process of natural evolution. This heuristic is routinely used

21、 to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA)(进化算法), which generate solutions(生成处理方案) to optimization problems using techniques inspired by natural evolution, such as inheritance(承继), mutation(突变) , se

22、lection(选择), and crossover(杂交). Genetic algorithm.Simulated Annealing Simulated annealing (SA) is a generic probabilistic metaheuristic(启发式) for the global optimization problem of applied mathematics(运用数学), namely locating a good approximation(逼近) to the global optimum of a given function in a large

23、 search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration(穷举法 ) provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the bes