Facility Location Algorithm Research Articles

An LP rounding algorithm for approximating uncapacitated facility location problem with penalties

In this paper, we consider an interesting variant of the facility location problem called uncapacitated facility location problem with penalties (UFLWP, for short) in which each client can be either assigned to some opened facility or rejected by paying a penalty. Existing approaches [M. Charikar, S. Khuller, D. Mount, G. Narasimhan, Algorithms for facility location problems with outliers, in: Proc. Symposium on Discrete Algorithms, 2001, p. 642] and [K. Jain, M. Mahdian, E. Markakis, A. Saberi, V. Vazirani, Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP, J. ACM 50 (2003) 795] for this variant of facility location problem are all based on primal-dual method. In this paper, we present an efficient linear programming (LP) rounding based approach to show that LP rounding techniques are equally capable of solving this variant of facility location problem. Our algorithm uses a two-phase filtering technique (generalized from Lin and Vitter's [ ɛ-approximation with minimum packing constraint violation, in: Proc. 24th Annual ACM Symp. on Theory of Computing, 1992, p. 771]) to identify those to-be-rejected clients and open facilities for the remaining ones. Our approach achieves an approximation ratio of 2 + 2 / e (≈2.736) which is worse than the best approximation ratio of 2 achieved by the much more sophisticated dual fitting technique [K. Jain, M. Mahdian, E. Markakis, A. Saberi, V. Vazirani, Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP, J. ACM 50 (2003) 795], but better than the approximation ratio of 3 achieved by the primal-dual method [M. Charikar, S. Khuller, D. Mount, G. Narasimhan, Algorithms for facility location problems with outliers, in: Proc. Symposium on Discrete Algorithms, 2001, p. 642]. Our algorithm is simple, natural, and can be easily integrated into existing LP rounding based algorithms for facility location problem to deal with outliers.

Improved Combinatorial Algorithms for Facility Location Problems

We present improved combinatorial approximation algorithms for the uncapacitated facility location problem. Two central ideas in most of our results are cost scaling and greedy improvement. We present a simple greedy local search algorithm which achieves an approximation ratio of $2.414+\epsilon$ in $\tilde{O}(n^2/\epsilon)$ time. This also yields a bicriteria approximation tradeoff of $(1+\gamma,1+2/\gamma)$ for facility cost versus service cost which is better than previously known tradeoffs and close to the best possible. Combining greedy improvement and cost scaling with a recent primal-dual algorithm for facility location due to Jain and Vazirani, we get an approximation ratio of $1.853$ in $\tilde{O}(n^3)$ time. This is very close to the approximation guarantee of the best known algorithm which is linear programming (LP)-based. Further, combined with the best known LP-based algorithm for facility location, we get a very slight improvement in the approximation factor for facility location, achieving $1.728$. We also consider a variant of the capacitated facility location problem and present improved approximation algorithms for this.

A simple and deterministic competitive algorithm for online facility location

This paper presents a deterministic and efficient algorithm for online facility location. The algorithm is based on a simple hierarchical partitioning and is extremely simple to implement. It also applies to a variety of models, i.e., models where the facilities can be placed anywhere in the region, or only at customer sites, or only at fixed locations. The paper shows that the algorithm is O (log n )-competitive under these various models, where n is the total number of customers. It also shows that the algorithm is O (1)-competitive with high probability and for any arrival order when customers are uniformly distributed or when they follow a distribution satisfying a smoothness property. Experimental results for a variety of scenarios indicate that the algorithm behaves extremely well in practice.

The Power of Priority Algorithms for Facility Location and Set Cover

We apply and extend the priority algorithm framework introduced by Borodin, Nielsen, and Rackoff to define “greedy-like” algorithms for the (uncapacitated) facility location problems and set cover problems. These problems have been the focus of extensive research from the point of view of approximation algorithms and for both problems greedy-like algorithms have been proposed and analyzed. The priority algorithm definitions are general enough to capture a broad class of algorithms that can be characterized as “greedy-like” while still possible to derive non-trivial lower bounds on the approximability of the problems by algorithms in such a class. Our results are orthogonal to complexity considerations, and hence apply to algorithms that are not necessarily polynomial time.

A geometric theorem for network design

Consider an infinite square grid G. How many discs of given radius r, centered at the vertices of G, are required, in the worst case, to completely cover an arbitrary disc of radius r placed on the plane? We show that this number is an integer in the set {3,4,5,6} whose value depends on the ratio of r to the grid spacing. One application of this result is to design facility location algorithms with constant approximation factors. Another application is to determine if a grid network design, where facilities are placed on a regular grid in a way that each potential customer is within a reasonably small radius around the facility, is cost effective in comparison to a nongrid design. This can be relevant to determine a cost effective design for base station placement in a wireless network

Approximate k -MSTs and k -Steiner trees via the primal-dual method and Lagrangean relaxation

Garg [10] gives two approximation algorithms for the minimum-cost tree spanning k vertices in an undirected graph. Recently Jain and Vazirani [15] discovered primal-dual approximation algorithms for the metric uncapacitated facility location and k-median problems. In this paper we show how Garg’s algorithms can be explained simply with ideas introduced by Jain and Vazirani, in particular via a Lagrangean relaxation technique together with the primal-dual method for approximation algorithms. We also derive a constant factor approximation algorithm for the k-Steiner tree problem using these ideas, and point out the common features of these problems that allow them to be solved with similar techniques.

An improved algorithm for solving a multi-period facility location problem

This note develops a pruning rule for a previously published multi-period facility location algorithm. This rule can be used to reduce the number of candidate single period location configurations to be examined. An important feature of the proposed rule is that very little additional computation is required in order to apply it. The rule is illustrated using one of the examples in the previously published paper. Finally an experiment is also conducted to examine the effectiveness of the proposed rule under different conditions.

Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP

In this article, we will formalize the method of dual fitting and the idea of factor-revealing LP. This combination is used to design and analyze two greedy algorithms for the metric uncapacitated facility location problem. Their approximation factors are 1.861 and 1.61, with running times of O ( m log m ) and O ( n 3 ), respectively, where n is the total number of vertices and m is the number of edges in the underlying complete bipartite graph between cities and facilities. The algorithms are used to improve recent results for several variants of the problem.

The Online Median Problem

We introduce a natural variant of the (metric uncapacitated) k-median problem that we call the online median problem. Whereas the k-median problem involves optimizing the simultaneous placement of k facilities, the online median problem imposes the following additional constraints: the facilities are placed one at a time, a facility cannot be moved once it is placed, and the total number of facilities to be placed, k, is not known in advance. The objective of an online median algorithm is to minimize the competitive ratio, that is, the worst-case ratio of the cost of an online placement to that of an optimal offline placement. Our main result is a constant-competitive algorithm for the online median problem running in time that is linear in the input size. In addition, we present a related, though substantially simpler, constant-factor approximation algorithm for the (metric uncapacitated) facility location problem that runs in time linear in the input size. The latter algorithm is similar in spirit to the recent primal-dual-based facility location algorithm of Jain and Vazirani, but our approach is more elementary and yields an improved running time. While our primary focus is on problems which ask us to minimize the weighted average service distance to facilities, we also show that our results can be generalized to hold, to within constant factors, for more general objective functions. For example, we show that all of our approximation results hold, to within constant factors, for the k-means objective function.

Approximation Algorithms for Access Network Design

Abstract. We consider the problem of designing a minimum cost access network to carry traffic from a set of endnodes to a core network. Trunks are available in Ktypes reflecting economies of scale . A trunk type with a high initial overhead cost has a low cost per unit bandwidth and a trunk type with a low overhead cost has a high cost per unit bandwidth. We formulate the problem as an integer program. We first use a primal—dual approach to obtain a solution whose cost is within O(K 2 )of optimal. Typically the value of Kis small. This is the first combinatorial algorithm with an approximation ratio that is polynomial in Kand is independent of the network size and the total traffic to be carried. We also explore linear program rounding techniques and prove a better approximation ratio of O(K) . Both bounds are obtained under weak assumptions on the trunk costs. Our primal—dual algorithm is motivated by the work of Jain and Vazirani on facility location [7]. Our rounding algorithm is motivated by the facility location algorithm of Shmoys et al. [12].

Approximation algorithms for metric facility location and k -Median problems using the primal-dual schema and Lagrangian relaxation

We present approximation algorithms for the metric uncapacitated facility location problem and the metric k -median problem achieving guarantees of 3 and 6 respectively. The distinguishing feature of our algorithms is their low running time: O(m log m ) and O(m log m(L + log ( n ))) respectively, where n and m are the total number of vertices and edges in the underlying complete bipartite graph on cities and facilities. The main algorithmic ideas are a new extension of the primal-dual schema and the use of Lagrangian relaxation to derive approximation algorithms.

Greedy Strikes Back: Improved Facility Location Algorithms

A fundamental facility location problem is to choose the location of facilities, such as industrial plants and warehouses, to minimize the cost of satisfying the demand for some commodity. There are associated costs for locating the facilities, as well as transportation costs for distributing the commodities. We assume that the transportation costs form a metric. This problem is commonly referred to as theuncapacitated facility locationproblem. Application to bank account location and clustering, as well as many related pieces of work, are discussed by Cornuejols, Nemhauser, and Wolsey. Recently, the first constant factor approximation algorithm for this problem was obtained by Shmoys, Tardos, and Aardal. We show that a simple greedy heuristic combined with the algorithm by Shmoys, Tardos, and Aardal, can be used to obtain an approximation guarantee of 2.408. We discuss a few variants of the problem, demonstrating better approximation factors for restricted versions of the problem. We also show that the problem is max SNP-hard. However, the inapproximability constants derived from the max SNP hardness are very close to one. By relating this problem to Set Cover, we prove a lower bound of 1.463 on the best possible approximation ratio, assumingNP∉DTIME[nO(loglogn)].

Facility location with dynamic distance functions

Facility location problems have always been studied with theassumption that the edge lengths in the network are static anddo not change over time. The underlying network could be used to model a city street networkfor emergency facility location/hospitals, or an electronic network for locating information centers. In any case, it is clear that due to trafficcongestion the traversal time on links changes with time. Very often, we have estimates as to how the edge lengths change over time, and our objective is to choose a set of locations (vertices) ascenters, such that at every time instant each vertex has a center close to it (clearly, the center close to a vertex may change over time). We also provide approximation algorithms as well as hardness results forthe K-center problem under this model. This is the first comprehensive study regarding approximation algorithmsfor facility location for good time-invariant solutions.

An efficient algorithm for facility location in the presence of forbidden regions : Steven E. Butt and Tom M. Cavalier European Journal of Operational Research Vol. 90, 1996, pp. 56–70

An efficient algorithm for facility location in the presence of forbidden regions : Steven E. Butt and Tom M. Cavalier European Journal of Operational Research Vol. 90, 1996, pp. 56–70

An efficient algorithm for facility location in the presence of forbidden regions

This paper investigates a constrained form of the classical Weber problem. Specifically, we consider the problem of locating a new facility in the presence of convex polygonal forbidden regions such that the sum of the weighted distances from the new facility to n existing facilities is minimized. It is assumed that a forbidden region is an area in the plane where travel and facility location are not permitted and that distance is measured using the Euclidean-distance metric. A solution procedure for this nonconvex programming problem is presented. It is shown that by iteratively solving a series of unconstrained problems, this procedure terminates at a local optimum to the original constrained problem. Numerical examples are presented.

Using a Facility Location Algorithm to Determine Optimum Cast Bloom Lengths

The dimensions of a bloom, which is a rectangular piece of steel, are critical for efficiently and effectively rolling the bloom into a finished structural shape (I-beam) for sale to the customer. To achieve maximum productivity and yield, the bloom size (thickness, width and length) to be rolled on a finishing mill into a structural shape must be determined by steel deformation experts. Suppose, for a particular finishing mill, these ‘rolled' blooms are all produced from a ‘cast' bloom of the same cross-section but with many different lengths. It is necessary to consolidate the many ‘cast' bloom length-metallurgical grade combinations to a number that can be managed by the casting operation and bloom stockyard without significantly impacting productivity and yield. An uncapacitated facility location problem formulation and algorithm were used to solve this problem. The way in which this approach was used to solve a real-world application is discussed.

Using a Facility Location Algorithm to Determine Optimum Cast Bloom Lengths

The dimensions of a bloom, which is a rectangular piece of steel, are critical for efficiently and effectively rolling the bloom into a finished structural shape (I-beam) for sale to the customer. To achieve maximum productivity and yield, the bloom size (thickness, width and length) to be rolled on a finishing mill into a structural shape must be determined by steel deformation experts. Suppose, for a particular finishing mill, these ‘rolled' blooms are all produced from a ‘cast' bloom of the same cross-section but with many different lengths. It is necessary to consolidate the many ‘cast' bloom length-metallurgical grade combinations to a number that can be managed by the casting operation and bloom stockyard without significantly impacting productivity and yield. An uncapacitated facility location problem formulation and algorithm were used to solve this problem. The way in which this approach was used to solve a real-world application is discussed.

Using a Facility Location Algorithm to Determine Optimum Cast Bloom Lengths

Using a Facility Location Algorithm to Determine Optimum Cast Bloom Lengths

A globally convergent algorithm for facility location on a sphere

In this paper, we study the problem of facility location on a sphere. This is a generalization of the planar Euclidean facility location problem. This problem was first studied by Katz and Cooper and by Drezner and Wesolowsky where Weiszfeld-like algorithms were proposed. However, convergence has never been proved. In this paper, we first prove a hull property of the problem, i.e., every global minimizer is in the spherical convex hull of the existing facilities. We then study the relationship between the spherical facility location problem and a planar Euclidean facility location problem corresponding to each approximate solution to the spherical facility location problem. Optimality conditions for the spherical facility location problem are established in terms of optimality conditions for the corresponding planar Euclidean facility location problem and a gradient algorithm is proposed to solve the spherical facility location problem. We prove that our algorithm always converges to a global minimizer of the spherical facility location problem. Computational results are also given.

A Practical Approach for Determining Rectangular Stock Sizes

In this paper we address the problem of determining what rectangular sizes should be stocked in order to satisfy a bill of materials composed of smaller rectangles. As is common in many manufacturing operations, the stock material will be cut using two-staged guillotine cutting patterns. We first generate a large number of stock sizes ideally suited to the bill of materials. Then we use a facility location algorithm to consolidate the stock sizes down to an acceptable number. Given the solution of what rectangular sizes to stock, a second program is used to map bills of materials into the stock rectangles. The effectiveness of our approach is demonstrated by generating stock sizes for a real-world bill of materials with 392 distinct order sizes and over 7700 order pieces.