Reliability problems in multiple path-shaped facility location on networks

In this paper we study a location problem on networks that combines three important issues: (1) it considers that facilities are extensive, (2) it handles simultaneously the location of more than one facility, and (3) it incorporates reliability aspects related to the fact that facilities may fail. The problem consists of locating two path-shaped facilities minimizing the expected service cost in the long run, assuming that paths may become unavailable and their failure probabilities are known in advance. We discuss several aspects of the computational complexity of problems of locating two or more reliable paths on graphs, showing that multifacility path location–with and without reliability issues–is a difficult problem even for 2 facilities and on very special classes of graphs. In view of this, we focus on trees and provide a polynomial time algorithm that solves the 2 unreliable path location problem on tree networks in $O\left(n^2\right)$ time, where $n$ is the number of vertices.     

Local search algorithm for universal facility location problem with linear penalties

The universal facility location problem generalizes several classical facility location problems, such as the uncapacitated facility location problem and the capacitated location problem (both hard and soft capacities). In the universal facility location problem, we are given a set of demand points and a set of facilities. We wish to assign the demands to facilities such that the total service as well as facility cost is minimized. The service cost is proportional to the distance that each unit of the demand has to travel to its assigned facility. The open cost of facility i depends on the amount z of demand assigned to i and is given by a cost function \(f_i(z)\). In this work, we extend the universal facility location problem to include linear penalties, where we pay certain penalty cost whenever we refuse serving some demand points. As our main contribution, we present a (\(7.88+\epsilon \))-approximation local search algorithm for this problem.     

Heuristic Solution Methods for Two Location Problems with Unreliable Facilities

In this paper, the p-median and p-centre problems are generalized by considering the possibility that one or more of the facilities may become inactive. The unreliable p-median problem is defined by introducing the probability that a facility becomes inactive. The (p, q)-centre problem is defined when p facilities need to be located but up to q of them may become unavailable at the same time. An heuristic procedure is presented for each problem. A rigorous procedure is discussed for the (p, q)-centre problem. Computational results are presented.     

An Automated Procedure to Establish Workzone Boundaries for Air Force Facilities Maintenance Operations

The problem of balancing maintenance workloads among geographical workzones on an air force base is discussed. An algorithm is presented which assigns facilities to workzones such that facilities assigned to the same workzone satisfy two conditions:

1. 1)
   The facility must be in close geographical proximity to the other facilities in the workzone.
    
2. 2)
   The sum of the expected maintenance workloads of all facilities assigned to a workzone should have little variation between workzones.
    

A brief introduction to the air force base civil engineering function is followed by an explanation of the procedure for workzone determination presently prescribed for worldwide application. Existing research on the ‘loading problem’ is reviewed and determined to be of limited application to the problem at hand.Two mathematical programming formulations are presented to aid in defining the quantitative aspect of the problem and to illustrate the complexity of the problem.A heuristic algorithm is then presented which incorporates a Pythagorean calculation to determine the unassigned facility in closest geographical proximity to the last assigned facility. If the facility found has an expected maintenance workload smaller than the unused capacity in the workzone currently being filled, it is assigned to that workzone. Otherwise, it is the first facility assigned to a newly opened workzone. In turn its closest unassigned facility is considered for assignment to the workzone currently being filled.The system was used in a test application at Maxwell Air Force Base, Montgomery, Alabama. It is shown to produce very useful results with significantly less time and effort than presently required of the manual workzone determination system.     

Heuristic algorithms for general <Emphasis Type="Italic">k</Emphasis>-level facility location problems

In a general k-level uncapacitated facility location problem (k-GLUFLP), we are given a set of demand points, denoted by D, where clients are located. Facilities have to be located at a given set of potential sites, which is denoted by F in order to serve the clients. Each client needs to be served by a chain of k different facilities. The problem is to determine some sites of F to be set up and to find an assignment of each client to a chain of k facilities so that the sum of the setup costs and the shipping costs is minimized. In this paper, for a fixed k, an approximation algorithm within a factor of 3 of the optimum cost is presented for k-GLUFLP under the assumption that the shipping costs satisfy the properties of metric space. In addition, when no fixed cost is charged for setting up the facilities and k=2, we show that the problem is strong NP-complete and the constant approximation factor is further sharpen to be 3/2 by a simple algorithm. Furthermore, it is shown that this ratio analysis is tight.     

Medi-centre Location Problems

In this paper we consider two medi-centre location problems. One is the m-medi-centre problem in which we add to the m-median problem uniform distance constraints. The other problem is the uncapacitated medi-centre facility location problem where we include the fixed costs of establishing the facilities and thus the number of facilities is also a decision variable. For the two problems we present algorithms and discuss computational experience.     

Planar maximal covering location problem under block norm distance measure

This paper introduces a new model for the planar maximal covering location problem (PMCLP) under different block norms. The problem involves locating g facilities anywhere on the plane in order to cover the maximum number of n given demand points. The generalization, in this paper, is that the distance measures assigned to facilities are block norms of different types and different proximity measures. First, the PMCLP under different block norms is modelled as a maximum clique partition problem on an equivalent multi-interval graph. Then, the equivalent graph problem is modelled as an unconstrained binary quadratic problem (UQP). Both the maximum clique partition problem and the UQP are NP-hard problems; therefore, we solve the UQP format through a genetic algorithm heuristic. Computational examples are given.     

A distance constrainedp-facility location problem on the real line

LetV = {v ₁,, v _n } be a set ofn points on the real line (existing facilities). The problem considered is to locatep new point facilities,F ₁,, F _p , inV while satisfying distance constraints between pairs of existing and new facilities and between pairs of new facilities. Fori = 1, , p, j = 1, , n, the cost of locatingF _i at pointv _j isc _ij . The objective is to minimize the total cost of setting up the new facilities. We present anO(p ³ n ² logn) algorithm to solve the model.     

Competitive location in the plane

Two questions in competitive environment are presented in the literature. One question deals with finding the best location for new facilities in order to attract the most buying power away from existing facilities. The second question is how to find the best location for the defending facilities, so that a future competitor will be able to capture the least buying power. In this paper, we study the second problem for the case of a large number of customers spread independently and uniformly over a given regionA² and a large number of original facilities. We show that, under these conditions, a very simple solution, the honeycomb heuristic, is almost surely guaranteed to be within 2.5 percent from optimality. This is the case for any number of facilities contemplated by the competitor.     

Location of Two Facilities with Minimal Separation

In this technical note it is shown how a problem of location of two facilities may be formulated as a non-convex optimization problem, and an algorithm is provided to determine an ?-optimal solution.     

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.     

A linear time approximation scheme for computing geometric maximum k-star

Facility dispersion seeks to locate the facilities as far away from each other as possible, which has attracted a multitude of research attention recently due to the pressing need on dispersing facilities in various scenarios. In this paper, as a facility dispersion problem, the geometric maximum k-star problem is considered. Given a set P of n points in the Euclidean plane, a k-star is defined as selecting k points from P and linking k ? 1 points to the center point. The maximum k-star problem asks to compute a k-star on P with the maximum total length over its k ? 1 edges. A linear time approximation scheme is proposed for this problem. It approximates the maximum k-star within a factor of ${(1+\epsilon)}$ in ${O(n+1/\epsilon^4 \log 1/\epsilon)}$ time for any ${\epsilon >0 }$ . To the best of the authors’ knowledge, this work presents the first linear time approximation scheme on the facility dispersion problems.     

Discrete facility location with nonlinear diseconomies in fixed costs

A discrete facility location problem is formulated where the total fixed cost for establishing the facilities includes a component that is a nonlinear function of the number of facilities being established. Some theoretical properties of the solution are derived when this fixed cost is a convex nondecreasing function of the number of facilities. Based on these properties an efficient bisection heuristic is developed where at each iteration, the classical uncapacitated facility location and/or m-median subproblems are solved using available efficient heuristics.     

Approximation algorithms for the robust facility leasing problem

In this paper, we consider the robust facility leasing problem (RFLE), which is a variant of the well-known facility leasing problem. In this problem, we are given a facility location set, a client location set of cardinality n, time periods \(\{1, 2, \ldots , T\}\) and a nonnegative integer \(q < n\). At each time period t, a subset of clients \(D_{t}\) arrives. There are K lease types for all facilities. Leasing a facility i of a type k at any time period s incurs a leasing cost \(f_i^{k}\) such that facility i is opened at time period s with a lease length \(l_k\). Each client in \(D_t\) can only be assigned to a facility whose open interval contains t. Assigning a client j to a facility i incurs a serving cost \(c_{ij}\). We want to lease some facilities to serve at least \(n-q\) clients such that the total cost including leasing and serving cost is minimized. Using the standard primal–dual technique, we present a 6-approximation algorithm for the RFLE. We further offer a refined 3-approximation algorithm by modifying the phase of constructing an integer primal feasible solution with a careful recognition on the leasing facilities.     

An algorithm for generalized constrained multi-source Weber problem with demand substations

In this paper, we consider a multi-source Weber problem of m new facilities with respect to n demand regions in order to minimize the sum of the transportation costs between these facilities and the demand regions. We find a point on the border of each demand region from which the facilities serve the demand regions at these points. We present an algorithm including a location phase and an allocation phase in each iteration for solving this problem. An algorithm is also proposed for carrying out the location phase. Moreover, global convergence of the new algorithm is proved under mild assumptions, and some numerical results are presented.     

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.     

A k-product uncapacitated facility location problem

A k-product uncapacitated facility location problem can be described as follows. There is a set of demand points where clients are located and a set of potential sites where facilities of unlimited capacities can be set up. There are k different kinds of products. Each client needs to be supplied with k kinds of products by a set of k different facilities and each facility can be set up to supply only a distinct product with a non-negative fixed cost determined by the product it intends to supply. There is a non-negative cost of shipping goods between each pair of locations. These costs are assumed to be symmetric and satisfy the triangle inequality. The problem is to select a set of facilities to be set up and their designated products and to find an assignment for each client to a set of k   facilities so that the sum of the setup costs and the shipping costs is minimized. In this paper, an approximation algorithm within a factor of 2k+1$2k+1$ of the optimum cost is presented. Assuming that fixed setup costs are zero, we give a 2k-1$2k-1$ approximation algorithm for the problem. In addition we show that for the case k=2$k=2$, the problem is NP-complete when the cost structure is general and there is a 2-approximation algorithm when the costs are symmetric and satisfy the triangle inequality. The algorithm is shown to produce an optimal solution if the 2-product uncapacitated facility location problem with no fixed costs happens to fall on a tree graph.