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.     

A new formulation for the conditional -median and -center problems

In this paper we discuss the conditional p-median and p-center problems on a network. Demand nodes are served by the closest facility whether existing or new. The formulation presented in this paper provided better results than those obtained by the best known formulation.     

Analysis of Decomposition Algorithms with Benders Cuts for p-Median Problem

In this paper the algorithms for solving the p-median problem based on the Benders decomposition are investigated. A family of problems hard for solving with such algorithms is constructed and then generalized to a special NP-hard case of the p-median problem. It is shown that the effectiveness of the considered algorithms depends on the choice of the optimal values of the dual variables used in Benders cuts. In particular, the depth of the cuts can be equal to one.     

A branch-and-cut method for the obnoxious p-median problem

The obnoxious p-median (OpM) problem is the repulsive counterpart of the ore known attractive p-median problem. Given a set I of cities and a set J of possible locations for obnoxious plants, a p-cardinality subset Q of J is sought, such that the sum of the distances between each city of I and the nearest obnoxious site in Q is maximised. We formulate (OpM) as a {0,1} linear programming problem and propose three families of valid inequalities whose separation problem is polynomial. We describe a branch-and-cut approach based on these inequalities and apply it to a set of instances found in the location literature. The computational results presented show the effectiveness of these inequalities for (OpM). The work of the first author has been partially supported by the Coordinated Project C.A.M.P.O. and that of the third author by a short mobility grant, both of the Italian National Research Council.     

A polynomial method for the pos/neg weighted 3-median problem on a tree

Let a connected undirected graph G  =  (V, E) be given. In the classical p-median problem we want to find a set X containing p points in G such that the sum of weighted distances from X to all vertices in V is minimized. We consider the semi-obnoxious case where every vertex has either a positive or negative weight. In this case we have two different objective functions: the sum of the minimum weighted distances from X to all vertices and the sum of the weighted minimum distances. In this paper we show that for the case p = 3 an optimal solution for the second model in a tree can be found in O(n ⁵) time. If the 3-median is restricted to vertices or if the tree is a path then the complexity can be reduced to O(n ³). This research has partially been supported by the Spezialforschungsbereich F 003 “Optimierung und Kontrolle”, Projektbereich Diskrete Optimierung.     

Cooperative Parallel Variable Neighborhood Search for the p-Median

We propose a cooperative multi-search method for the Variable Neighborhood Search (VNS) meta-heuristic based on the central-memory mechanism that has been successfully applied to a number of difficult combinatorial problems. In this approach, several independent VNS meta-heuristics cooperate by asynchronously exchanging information about the best solutions identified so far, thus conserving the simplicity of the original, sequential VNS ideas. The p-median problem (PM) serves as test case. Extensive experimentations have been conducted on the classical TSPLIB benchmark problem instances with up to 11948 customers and 1000 medians, without any particular calibration of the parallel method. The results indicate that, compared to sequential VNS, the cooperative strategy yields significant gains in terms of computation time without a loss in solution quality.     

Discrete space location-allocation solutions from genetic algorithms

Genetic algorithms are adaptive sampling strategies based on information processing models from population genetics. Because they are able to sample a population broadly, they have the potential to out-perform existing heuristics for certain difficult classes of location problems. This paper describes reproductive plans in the context of adaptive optimization methods, and details the three genetic operators which are the core of the reproductive design. An algorithm is presented to illustrate applications to discrete-space location problems, particularly thep-median. The algorithm is unlikely to compete in terms of efficiency with existingp-median heuristics. However, it is highly general and can be fine-tuned to maximize computational efficiency for any specific problem class.     

A Cut and Branch Approach for the Capacitated <Emphasis Type="Italic">p</Emphasis>-Median Problem Based on Fenchel Cutting Planes

The capacitated p-median problem (CPMP) consists of finding p nodes (the median nodes) minimizing the total distance to the other nodes of the graph, with the constraint that the total demand of the nodes assigned to each median does not exceed its given capacity. In this paper we propose a cutting plane algorithm, based on Fenchel cuts, which allows us to considerably reduce the integrality gap of hard CPMP instances. The formulation strengthened with Fenchel cuts is solved by a commercial MIP solver. Computational results show that this approach is effective in solving hard instances or considerably reducing their integrality gap.      

COBRA: A New Formulation of the Classic p-Median Location Problem

The p-median problem was first formulated as an integer-linear programming problem by ReVelle and Swain (1970) and further revised by Rosing, ReVelle and Rosing-Vogelaar (1979). These two forms have withstood the test of time, as they have been used by virtually everyone since then. We prove that a property associated with geographical proximity makes it possible to eliminate many of the model variables through a substitution process. This new substitution technique has resulted in the elimination of up to 60% of the variables needed in either of these classic model formulations.     

A new heuristic approach for the P-median problem

The Euclidean p-median problem is concerned with the decision of the locations for public service centres. Existing methods for the planar Euclidean p-median problems are capable of efficiently solving problems of relatively small scale. This paper proposes two new heuristic algorithms aiming at problems of large scale. Firstly, to reflect the different degrees of proximity to optimality, a new kind of local optimum called level-m optimum is defined. For a level-m optimum of a p-median problem, where m<p, each of its subsets containing m of the p partitions is a global optimum of the corresponding m-median subproblem. Starting from a conventional local optimum, the first new algorithm efficiently improves it to a level-2 optimum by applying an existing exact algorithm for solving the 2-median problem. The second new algorithm further improves it to a level-3 optimum by applying a new exact algorithm for solving the 3-median problem. Comparison based on experimental results confirms that the proposed algorithms are superior to the existing heuristics, especially in terms of solution quality.     

A Genetic Algorithm for Solving a Capacitated p-Median Problem

Facility-location problems have several applications, such as telecommunications, industrial transportation and distribution. One of the most well-known facility-location problems is the p-median problem. This work addresses an application of the capacitated p-median problem to a real-world problem. We propose a genetic algorithm (GA) to solve the capacitated p-median problem. The proposed GA uses not only conventional genetic operators, but also a new heuristic “hypermutation” operator suggested in this work. The proposed GA is compared with a tabu search algorithm.     

A method for solving to optimality uncapacitated location problems

In this paper we develop a method for solving to optimality a general 0–1 formulation for uncapacitated location problems. This is a 3-stage method that solves large problems in reasonable computing times.The 3-stage method is composed of a primal-dual algorithm, a subgradient optimization to solve a Lagrangean dual and a branch-and-bound algorithm. It has a hierarchical structure, with a given stage being activated only if the optimal solution could not be identified in the preceding stage.The proposed method was used in the solution of three well-known uncapacitated location problems: the simple plant location problem, thep-median problem and the fixed-chargep-median problem. Computational results are given for problems of up to the size 200 customers ×200 potential facility sites.     

An ant colony algorithm for the pos/neg weighted p-median problem

Let a graph G = (V, E) with vertex set V and edge set E be given. The classical graph version of the p-median problem asks for a subset of cardinality p, so that the (weighted) sum of the minimum distances from X to all other vertices in V is minimized. We consider the semi-obnoxious case, where every vertex has either a positive or a negative weight. This gives rise to two different objective functions, namely the weighted sum of the minimum distances from X to the vertices in V\X and, differently, the sum over the minimum weighted distances from X to V\X. In this paper an Ant Colony algorithm with a tabu restriction is designed for both problems. Computational results show its superiority with respect to a previously investigated variable neighborhood search and a tabu search heuristic.This research has partially been supported by the Spezialforschungsbereich F 003 “Optimierung und Kontrolle”, Projektbereich Diskrete Optimierung.     

On the choice of aggregation points for continuousp-median problems: A case for the gravity centre

In large scale location-allocation studies it is necessary to use data-aggregation in order to obtain solvable models. A detailed analysis is given of the errors induced by this aggregation in the evaluation of thep-median objective function. Then it is studied how to choose the points at which to aggregate given groups of demand points so as to minimise this aggregation error. Forp-median problems with euclidean distances, arguments are given in favour of the centre of gravity of the groups. These arguments turn out to be much stronger for rectangular distance. Aggregating at the centroid also leads to much higher precision bounds on the errors for rectangular distance. Some numerical results are obtained validating the theoretical developments. This research was partially done while the author was on visit at the Laboratoire d’Analyse Appliquée et Optimisation at the Université de Bourgogne, Dijon, France. Thanks to E. Carrizosa, B. Rayco and four anonymous referees for many thoughtful remarks.     

Solving the p-Median Problem with a Semi-Lagrangian Relaxation

Lagrangian relaxation is commonly used in combinatorial optimization to generate lower bounds for a minimization problem. We study a modified Lagrangian relaxation which generates an optimal integer solution. We call it semi-Lagrangian relaxation and illustrate its practical value by solving large-scale instances of the p-median problem. This work was partially supported by the Fonds National Suisse de la Recherche Scientifique, grant 12-57093.99 and the Spanish government, MCYT subsidy dpi2002-03330.     

The fuzzy p-median problem: A global analysis of the solutions

We apply fuzzy techniques to incorporate external data into p-median problems. So we can detect certain solutions that would be discarded by usual crisp and fuzzy algorithms but that contrasted with this additional information can be advantageous. This usually reveals a pathology of the model and hence our methods provide some fuzzy validation criteria for p-median models.     

Discrete stochastic location models

In this paper, we study how the two classical location models, the simple plant location problem and thep-median problem, are transformed in a two-stage stochastic program with recourse when uncertainty on demands, variable production and transportation costs, and selling prices is introduced. We also discuss the relation between the stochastic version of the SPLP and the stochastic version of thep-median.     

Heuristic Methods for Large Centroid Clustering Problems

This article presents new heuristic methods for solving a class of hard centroid clustering problems including the p-median, the sum-of-squares clustering and the multi-source Weber problems. Centroid clustering is to partition a set of entities into a given number of subsets and to find the location of a centre for each subset in such a way that a dissimilarity measure between the entities and the centres is minimized. The first method proposed is a candidate list search that produces good solutions in a short amount of time if the number of centres in the problem is not too large. The second method is a general local optimization approach that finds very good solutions. The third method is designed for problems with a large number of centres; it decomposes the problem into subproblems that are solved independently. Numerical results show that these methods are efficient—dozens of best solutions known to problem instances of the literature have been improved—and fast, handling problem instances with more than 85,000 entities and 15,000 centres—much larger than those solved in the literature. The expected complexity of these new procedures is discussed and shown to be comparable to that of an existing method which is known to be very fast.     

A Hybrid Heuristic for the p-Median Problem

Given n customers and a set F of m potential facilities, the p-median problem consists in finding a subset of F with p facilities such that the cost of serving all customers is minimized. This is a well-known NP-complete problem with important applications in location science and classification (clustering). We present a multistart hybrid heuristic that combines elements of several traditional metaheuristics to find near-optimal solutions to this problem. Empirical results on instances from the literature attest the robustness of the algorithm, which performs at least as well as other methods, and often better in terms of both running time and solution quality. In all cases the solutions obtained by our method were within 0.1% of the best known upper bounds.