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THE PRIMALDUAL METHOD FOR APPROXIMATION ALGORITHMS AND ITS APPLICATION TO NETWORK DESIGN PROBLEMS
"... The primaldual method is a standard tool in the design of algorithms for combinatorial optimization problems. This chapter shows how the primaldual method can be modified to provide good approximation algorithms for a wide variety of NPhard problems. We concentrate on results from recent researc ..."
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Cited by 123 (7 self)
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The primaldual method is a standard tool in the design of algorithms for combinatorial optimization problems. This chapter shows how the primaldual method can be modified to provide good approximation algorithms for a wide variety of NPhard problems. We concentrate on results from recent research applying the primaldual method to problems in network design.
Existential arc consistency: Getting closer to full arc consistency in weighted csps
 In Proc. of the 19 th IJCAI
, 2005
"... The weighted CSP framework is a soft constraint framework with a wide range of applications. Most current stateoftheart complete solvers can be described as a basic depthfirst branch and bound search that maintain some form of arc consistency during the search. In this paper we introduce a new s ..."
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Cited by 68 (18 self)
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The weighted CSP framework is a soft constraint framework with a wide range of applications. Most current stateoftheart complete solvers can be described as a basic depthfirst branch and bound search that maintain some form of arc consistency during the search. In this paper we introduce a new stronger form of arc consistency, that we call existential directional arc consistency and we provide an algorithm to enforce it. The efficiency of the algorithm is empirically demonstrated in a variety of domains. 1
Facility Location under Uncertainty: A Review
 IIE Transactions
, 2004
"... Plants, distribution centers, and other facilities generally function for years or decades, during which time the environment in which they operate may change substantially. Costs, demands, travel times, and other inputs to classical facility location models may be highly uncertain. This has made th ..."
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Cited by 35 (7 self)
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Plants, distribution centers, and other facilities generally function for years or decades, during which time the environment in which they operate may change substantially. Costs, demands, travel times, and other inputs to classical facility location models may be highly uncertain. This has made the development of models for facility location under uncertainty a high priority for researchers in both the logistics and stochastic/robust optimization communities. Indeed, a large number of the approaches that have been proposed for optimization under uncertainty have been applied to facility location problems. This paper reviews the literature...
Facility location models for distribution system design
, 2004
"... The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamenta ..."
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Cited by 33 (0 self)
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The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamental assumptions, mathematical complexity and computational performance. This paper reviews some of the contributions to the current stateoftheart. In particular, continuous location models, network location models, mixedinteger programming models, and applications are summarized.
A 3Approximation Algorithm for the kLevel Uncapacitated Facility Location Problem
 Information Processing Letters
, 1999
"... In the klevel uncapacitated facility location problem, we have a set of demand points where clients are located. The demand of each client is known. Facilities have to be located at given sites in orde to service the clients, and each client is to be serviced by a sequence of k different facilit ..."
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Cited by 29 (1 self)
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In the klevel uncapacitated facility location problem, we have a set of demand points where clients are located. The demand of each client is known. Facilities have to be located at given sites in orde to service the clients, and each client is to be serviced by a sequence of k different facilities, each of which belongs to a distinct level. Thee are no capacity restrictions on the facilities. There is a positive fixed cost of setting up a facility, and a pe unit cost of shipping goods between each pair of locations. We assume that these distances are all nonnegative and satisfy the triangle inequality. The problem is to find an assignment of each client to a sequence of k facilities, one at each level, so that the demand of each client is satisfied, for which the sum of the setup costs and the service costs is minimized.
Solving The Simple Plant Location Problem By Genetic Algorithm
 RAIRO Operations Research
, 2001
"... The simple plant location problem (SPLP) is considered and a genetic algorithm is proposed to solve this problem. By using the developed algorithm it is possible to solve SPLP with more than 1000 facility sites and customers. Computational results are presented and compared to dual based algorit ..."
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Cited by 21 (1 self)
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The simple plant location problem (SPLP) is considered and a genetic algorithm is proposed to solve this problem. By using the developed algorithm it is possible to solve SPLP with more than 1000 facility sites and customers. Computational results are presented and compared to dual based algorithms.
On the TwoLevel Uncapacitated Facility Location Problem
 INFORMS J. COMPUT
, 1996
"... We study the twolevel uncapacitated facility location (TUFL) problem. Given two types of facilities, which we call yfacilities and zfacilities, the problem is to decide which facilities of both types to open, and to which pair of y and zfacilities each client should be assigned, in order to sat ..."
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Cited by 16 (3 self)
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We study the twolevel uncapacitated facility location (TUFL) problem. Given two types of facilities, which we call yfacilities and zfacilities, the problem is to decide which facilities of both types to open, and to which pair of y and zfacilities each client should be assigned, in order to satisfy the demand at maximum profit. We first present two multicommodity flow formulations of TUFL and investigate the relationship between these formulations and similar formulations of the onelevel uncapacitated facility location (UFL) problem. In particular, we show that all nontrivial facets for UFL define facets for the twolevel problem, and derive conditions when facets of TUFL are also facets for UFL. For both formulations of TUFL, we introduce new families of facets and valid inequalities and discuss the associated separation problems. We also characterize the extreme points of the LPrelaxation of the first formulation. While the LPrelaxation of a multicommodity formulation provi...
A Hybrid Multistart Heuristic for the Uncapacitated Facility Location Problem
, 2003
"... We present a multistart heuristic for the uncapacitated facility location problem, based on a very successful method we originally developed for the pmedian problem. ..."
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Cited by 16 (2 self)
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We present a multistart heuristic for the uncapacitated facility location problem, based on a very successful method we originally developed for the pmedian problem.
Heuristic Methods for Large Centroid Clustering Problems
, 1996
"... This article presents new heuristic methods for solving a class of hard centroid clustering problems including the fmedian, the sumofsquares clustering and the multisource Weber problems. Centroid clustering is to partition a set of entities into a given number of subsets and to find the locatio ..."
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Cited by 16 (5 self)
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This article presents new heuristic methods for solving a class of hard centroid clustering problems including the fmedian, the sumofsquares clustering and the multisource 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. Numer ical 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.