Location problems

Recent developments for several location problems are surveyed. These include: graph theoretic and combinatorial formulations of the simple plant location problem, the NP-hardness of some p-center problems, worst-case bounds for several polynomial-time heuristics for some p-center problems, and a general solution to a class of one facility network problems with convex cost functions.     

Heuristic Procedures for Solving the Discrete Ordered Median Problem

We present two heuristic methods for solving the Discrete Ordered Median Problem (DOMP), for which no such approaches have been developed so far. The DOMP generalizes classical discrete facility location problems, such as the p-median and p-center. The first procedure proposed in this paper is based on a genetic algorithm developed by Moreno Vega (1996) for p-median and p-center problems. Additionally, a second heuristic approach based on the Variable Neighborhood Search metaheuristic (VNS) proposed by Hansen and Mladenović (1997) for the p-median problem is described. An extensive numerical study is presented to show the efficiency of both heuristics and compare them.     

Robust Optimization for the Connected Facility Location Problem

In this paper we present a robust optimization (RO) model for the Connected Facility Location (ConFL) problem within the framework introduced by Bertsimas and Sim [Bertsimas, D. and M. Sim, Robust discrete optimization and network flows, Mathematical Programming 98 (2003), pp. 49–71], and show how to use a heuristic in conjunction with a lower bounding mechanism to rapidly find high-quality solutions. The use of a heuristic and a lower bound mechanism within this RO approach decreases significantly its computational time and broadens its applicability to other NP-hard problems. Here we present some of our computational results that attest to the efficiency of the approach, particularly on the Robust ConFL problem.     

Validating the Gravity-Based Competitive Location Model Using Inferred Attractiveness

The attractiveness of retail facilities is an essential component of models analyzing competition among retail facilities. In this paper we introduce an innovative method for inferring retail facility attractiveness. Readily available data from secondary sources about customers' buying power and sales volumes obtained by competing retail facilities are used. The gravity-based competitive facility location model is used to predict sales. The attractiveness of the retail facilities are inferred from these data.The procedure is used to confirm the gravity competitive facility location model. Inferred attractiveness results based on empirical data from Orange County, California, were compared with an independent survey with excellent match.     

A 1-center problem on the plane with uniformly distributed demand points

Center problems or minimax facility location problems are among the most active research areas in location theory. In this paper, we find the best unique location for a facility in the plane such that the maximum expected weighted distance to all random demand points is minimized.     

连续设施选址:模型、方法与应用

给定度量空间和该空间中的若干顾客,设施选址为在该度量空间中确定新设施的位置使得某种目标达到最优。连续设施选址是设施选址中的一类重要问题,其中的设施可在度量空间的某连续区域上进行选址。本文对连续设施选址的模型、算法和应用方面的工作进行了综述。文章首先讨论了连续设施选址中几个重要元素,包括新设施个数、距离度量函数、目标函数;然后介绍了连续选址中的几种经典模型和拓展模型;接着概述了求解连续选址问题的常用优化方法和技术,包括共轭对偶、全局优化、不确定优化、变分不等式方法、维诺图;最后介绍了连续设施选址的重要应用并给出了研究展望。     

A Probabilistic Minimax Location Problem on the Plane

In this paper we consider the weighted minimax (1-center) location problem in the plane when the weights are not given but rather drawn from independent uniform distributions. The problem is formulated and analyzed. For certain parameters of the uniform distributions the objective function is proven to be convex and thus can be easily solved by standard software such as the Solver in Excel. Computational experience is reported.     

Location Among Regions with Varying Norms

This paper investigates a new variation in the continuous single facility location problem. Specifically, we address the problem of locating a new facility on a plane with different distance norms on different sides of a boundary line. Special cases and extensions of the problem, where there are more than two regions are also discussed. Finally, by investigating the properties of the models, efficient solution procedures are proposed.     

A modified variable neighborhood search for the discrete ordered median problem

This paper presents a modified Variable Neighborhood Search (VNS) heuristic algorithm for solving the Discrete Ordered Median Problem (DOMP). This heuristic is based on new neighborhoods’ structures that allow an efficient encoding of the solutions of the DOMP avoiding sorting in the evaluation of the objective function at each considered solution. The algorithm is based on a data structure, computed in preprocessing, that organizes the minimal necessary information to update and evaluate solutions in linear time without sorting. In order to investigate the performance, the new algorithm is compared with other heuristic algorithms previously available in the literature for solving DOMP. We report on some computational experiments based on the well-known N-median instances of the ORLIB with up to 900 nodes. The obtained results are comparable or superior to existing algorithms in the literature, both in running times and number of best solutions found.     

Using a facility location algorithm to solve large set covering problems

Erlenkotter has developed an efficient exact (guarantees optimality) algorithm to solve the uncapacitated facility location problem (UFLP). In this paper, we use his algorithm to solve large instances of an important subset of the UFLP; the set covering problem (SCP). In addition, we present further empirical evidence that a heuristic algorithm developed by Vasko and Wilson for the SCP is capable of quickly generating good solutions to large SCP's.     

Algorithms for a Facility Location Problem with Stochastic Customer Demand and Immobile Servers

This paper studies a facility location problem with stochastic customer demand and immobile servers. Motivated by applications to locating bank automated teller machines (ATMs) or Internet mirror sites, these models are developed for situations in which immobile service facilities are congested by stochastic demand originating from nearby customer locations. Customers are assumed to visit the closest open facility. The objective of this problem is to minimize customers' total traveling cost and waiting cost. In addition, there is a restriction on the number of facilities that may be opened and an upper bound on the allowable expected waiting time at a facility. Three heuristic algorithms are developed, including a greedy-dropping procedure, a tabu search approach and an -optimal branch-and-bound method. These methods are compared computationally on a bank location data set from Amherst, New York.     

Residential location and school planning in a tightening urban economy

Facing worse fiscal plight, many municipalities in Sweden must today carefully reexamine their activities. In urban planning, this has resulted in a growing interest in how the urban development could be designed to support and facilitate the efficient use of existing public investments. This paper focuses on the school sector as being one of the most costly. A location-allocation model of the capacitated facility location type is formulated. A set of potential schools consisting of existing and new ones are considered. The school-age children are assigned to a subset of these schools so as to minimize the sum of the capital costs of this subset and the transportation costs of the children. The model is applied to the municipality of Uppsala in Sweden. Different future settlement structures proposed by the planners as well as different housing allocations generated by a separate optimization model are evaluated.     

A BSSS algorithm for the single facility location problem in two regions with different norms

Suppose the plane is divided by a straight line into two regions with different norms. We want to find the location of a single new facility such that the sum of the distances from the existing facilities to this point is minimized. This is in fact a non-convex optimization problem. The main difficulty is caused by finding the distances between points on different sides of the boundary line. In this paper we present a closed form solution for finding these distances. We also show that the optimal solution lies in the rectangular hull of the existing points. Based on these findings then, an efficient big square small square (BSSS) procedure is proposed.     

Global Optimization Issues in Multiparametric Continuous and Mixed-Integer Optimization Problems

In this paper, a number of theoretical and algorithmic issues concerning the solution of parametric nonconvex programs are presented. In particular, the need for defining a suitable overestimating subproblem is discussed in detail. The multiparametric case is also addressed, and a branch and bound (B&B) algorithm for the solution of parametric nonconvex programs is proposed.     

Location Science Research: A Review

This document presents a broad review of facility location and location science research. The goal of this report is not to provide an exhaustive list of location science topics (an undertaking far beyond the scope of a single journal article), but rather to provide the reader with a more general review of the location science research landscape. This document starts with a short introduction to some of the more germane aspects of all location science research.     

考虑回收-补偿约束的卫星城镇生活垃圾中转站选址研究

为进行卫星城镇WSW中转站优化选址,本文结合城镇中转站选址经济、社会、环境负效应等相关影响因素细化分析,确定了城镇中转设施分步优化模型方法。该方法结合规范约束距离改进LSCP模型,于待选点中确定一次覆盖所有收集点的设施备选组合,而后应用综合考虑了建设运营成本、负效应、回收效率的多目标整数规划模型最终确定中转站位置、收纳量与服务分配方案。通过实例分析,验证了本文方法实用性与合理性。     

A D.C. biobjective location model

In this paper we address the biobjective problem of locating a semiobnoxious facility, that must provide service to a given set of demand points and, at the same time, has some negative effect on given regions in the plane. In the model considered, the location of the new facility is selected in such a way that it gives answer to these contradicting aims: minimize the service cost (given by a quite general function of the distances to the demand points) and maximize the distance to the nearest affected region, in order to reduce the negative impact. Instead of addressing the problem following the traditional trend in the literature (i.e., by aggregation of the two objectives into a single one), we will focus our attention in the construction of a finite -dominating set, that is, a finite feasible subset that approximates the Pareto-optimal outcome for the biobjective problem. This approach involves the resolution of univariate d.c. optimization problems, for each of which we show that a d.c. decomposition of its objective can be obtained, allowing us to use standard d.c. optimization techniques.     

Locating a minisum circle in the plane

We consider the problem of locating a circle with respect to existing facilities in the plane such that the sum of weighted distances between the circle and the facilities is minimized, i.e., we approximate a set of given points by a circle regarding the sum of weighted distances. If the radius of the circle is a variable we show that there always exists an optimal circle passing through two of the existing facilities. For the case of a fixed radius we provide characterizations of optimal circles in special cases. Solution procedures are suggested.     

重大突发事件应急设施多重覆盖选址模型及算法

为了解决应对重大突发事件过程中应急需求的多点同时需求和多次需求问题,本文研究了应对重大突发事件的应急服务设施布局中的覆盖问题:针对重大突发事件应急响应的特点,引入最大临界距离和最小临界距离的概念,在阶梯型覆盖质量水平的基础上,建立了多重数量和质量覆盖模型。模型的优化目标是满足需求点的多次覆盖需求和多需求点同时需求的要求条件下,覆盖的人口期望最大,并用改进的遗传算法进行求解;最后给出的算例证明了模型和算法的有效性,从而应急设施的多重覆盖选址模型能够为有效应对重大突发事件的应急设施选址决策提供参考依据。