Multi-facility Weber Problem Research Articles

Single-Source Capacitated Multi-Facility Weber Problem—An iterative two phase heuristic algorithm

Multi-Facility Weber Problem (MFWP), also known as continuous location–allocation problem, entails determining the locations of a predefined number of facilities in a planar space and their related customer allocations. In this paper, we focus on a new variant of the problem known as Single-Source Capacitated MFWP (SSCMFWP). To tackle the problem efficiently and effectively, an iterative two-phase heuristic algorithm is put forward. At the phase I, we aim to determine proper locations for facilities, and during the phase II, assignment of customers to these facilities is pursued. As an alternative solution method, a simulated annealing (SA) algorithm is also proposed for carrying out the phase I. The proposed algorithms are validated on a comprehensive set of test instances taken from the literature. The proposed iterative two-phase algorithm produces superior results when assessed against the proposed SA algorithm as well as a general MINLP Solver known as BARON. The latter is applied to produce optimal solutions for small sized instances and generate upper bound for medium ones.

Efficient approximate solution methods for the multi-commodity capacitated multi-facility Weber problem

The capacitated multi-facility Weber problem is concerned with locating I capacitated facilities in the plane to satisfy the demand of J customers with the minimum total transportation cost of a single commodity. This is a nonconvex optimization problem and difficult to solve. In this work, we focus on a multi-commodity extension and consider the situation where K distinct commodities are shipped subject to capacity constraints between each customer and facility pair. Customer locations, demands and capacities for each commodity, and bundle restrictions are known a priori. The transportation costs, which are proportional to the distance between customers and facilities, depend on the commodity type. We address several location–allocation and discrete approximation heuristics using different strategies. Based on the obtained computational results we can say that the alternate solution of location and allocation problems is a very efficient strategy; but the discrete approximation has excellent accuracy.

A continuation approach for the capacitated multi-facility weber problem based on nonlinear SOCP reformulation

We propose a primal-dual continuation approach for the capacitated multi-facility Weber problem (CMFWP) based on its nonlinear second-order cone program (SOCP) reformulation. The main idea of the approach is to reformulate the CMFWP as a nonlinear SOCP with a nonconvex objective function, and then introduce a logarithmic barrier term and a quadratic proximal term into the objective to construct a sequence of convexified subproblems. By this, this class of nondifferentiable and nonconvex optimization problems is converted into the solution of a sequence of nonlinear convex SOCPs. In this paper, we employ the semismooth Newton method proposed in Kanzow et al. (SIAM Journal of Optimization 20:297---320, 2009) to solve the KKT system of the resulting convex SOCPs. Preliminary numerical results are reported for eighteen test instances, which indicate that the continuation approach is promising to find a satisfying suboptimal solution, even a global optimal solution for some test problems.

The multi-commodity capacitated multi-facility Weber problem: heuristics and confidence intervals

The Capacitated Multi-facility Weber Problem (CMWP) is concerned with locating I capacitated facilities so as to satisfy the demand of J customers with the minimum total transportation cost of a single commodity. This is a non-convex optimization problem and is difficult to solve. This work focuses on a multi-commodity extension and considers the situation where K distinct commodities are shipped to the customers subject to capacity and demand constraints. Customer locations, demands, and capacities for each commodity are known a priori. The transportation costs, which are proportional to the distance between customers and facilities, depend on the commodity type. A mathematical programming formulation of the problem is presented and two alternate location-allocation heuristics and a discrete approximation method are proposed and subsequently used to statistically estimate confidence intervals on the optimal objective function values. Computational experiments on standard and randomly generated test instances are also presented.

A location–allocation heuristic for the capacitated multi-facility Weber problem with probabilistic customer locations

Abstract The capacitated multi-facility Weber problem is concerned with locating m facilities in the Euclidean plane, and allocating their capacities to n customers at minimum total cost. The deterministic version of the problem, which assumes that customer locations and demands are known with certainty, is a non-convex optimization problem and difficult to solve. In this work, we focus on a probabilistic extension and consider the situation where the customer locations are randomly distributed according to a bivariate distribution. We first present a mathematical programming formulation, which is even more difficult than its deterministic version. We then propose an alternate location–allocation local search heuristic generalizing the ideas used originally for the deterministic problem. In its original form, the applicability of the heuristic depends on the calculation of the expected distances between the facilities and customers, which can be done for only very few distance and probability density function combinations. We therefore propose approximation methods which make the method applicable for any distance function and bivariate location distribution.

Solving probabilistic multi-facility Weber problem by vector quantization

The multi-facility Weber problem is concerned with locating in the plane m facilities having unlimited capacities and allocating them to n customers at minimum total cost. The deterministic version is a non-convex optimization problem and difficult to solve exactly. In this work we focus on a probabilistic extension and consider the situation where the customer locations are randomly distributed. For this problem, we propose new heuristics based on the principle of vector quantization which are capable of computing good quality solutions for general distance functions and customer location distributions.

Efficient heuristics for the rectilinear distance capacitated multi-facility Weber problem

In this paper, we consider the capacitated multi-facility Weber problem with rectilinear distance. This problem is concerned with locating m capacitated facilities in the Euclidean plane to satisfy the demand of n customers with the minimum total transportation cost. The demand and location of each customer are known a priori and the transportation cost between customers and facilities is proportional to the rectilinear distance separating them. We first give a new mixed integer linear programming formulation of the problem by making use of a well-known necessary condition for the optimal facility locations. We then propose new heuristic solution methods based on this formulation. Computational results on benchmark instances indicate that the new methods can provide very good solutions within a reasonable amount of computation time.

Solving the uncapacitated multi-facility Weber problem by vector quantization and self-organizing maps

The uncapacitated multi-facility Weber problem is concerned with locating m facilities in the Euclidean plane and allocating the demands of n customers to these facilities with the minimum total transportation cost. This is a non-convex optimization problem and difficult to solve exactly. As a consequence, efficient and accurate heuristic solution procedures are needed. The problem has different types based on the distance function used to model the distance between the facilities and customers. We concentrate on the rectilinear and Euclidean problems and propose new vector quantization and self-organizing map algorithms. They incorporate the properties of the distance function to their update rules, which makes them different from the existing two neural network methods that use rather ad hoc squared Euclidean metric in their updates even though the problem is originally stated in terms of the rectilinear and Euclidean distances. Computational results on benchmark instances indicate that the new methods are better than the existing ones, both in terms of the solution quality and computation time.

Probabilistic formulations of the multifacility weber problem

AbstractThe problem considered is to locate one or more new facilities relative to a number of existing facilities when both the locations of the existing facilities, the weights between new facilities, and the weights between new and existing facilities are random variables. The new facilities are to be located such that expected distance traveled is minimized. Euclidean distance measure is considered; both unconstrained and chance‐constrained formulations are treated.

A Nonlinear Approximation Method for Solving a Generalized Rectangular Distance Weber Problem

This paper provides a method for approximating optimal location in a multi-facility Weber problem where rectangular distances apply. Optimality is achieved when the sum of weighted distances is minimized. Two upper bounds on the error incurred by using the approximation are developed. The formulation can be used in convex programming to solve some nonlinearly constrained problems.

Heuristic Methods for Location-Allocation Problems

Heuristic Methods for Location-Allocation Problems