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36
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.
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.
Lagrangean/Surrogate Heuristics for pMedian Problems
, 2000
"... : The pmedian problem is the problem of locating p facilities (medians) on a network so as to minimize the sum of all the distances from each demand point to its nearest facility. A successful approach to approximately solve this problem is the use of Lagrangean heuristics, based upon Lagrangean re ..."
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Cited by 14 (9 self)
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: The pmedian problem is the problem of locating p facilities (medians) on a network so as to minimize the sum of all the distances from each demand point to its nearest facility. A successful approach to approximately solve this problem is the use of Lagrangean heuristics, based upon Lagrangean relaxation and subgradient optimization. The Lagrangean/surrogate is an alternative relaxation proposed recently to correct the erratic behavior of subgradient like methods employed to solve the Lagrangean dual. We propose in this paper Lagrangean/surrogate heuristics to pmedian problems. Lagrangean and surrogate relaxations are combined relaxing in the surrogate way the assignment constraints in the pmedian formulation. Then, the Lagrangean relaxation of the surrogate constraint is obtained and approximately optimized (onedimensional dual). Lagrangean/surrogate relaxations are very stable (low oscillating) and reach the same good results of Lagrangean (alone) heuristics in less computation...
The Graph Voronoi Diagram with Applications
 Networks
, 2000
"... The Voronoi diagram is a famous structure of computational geometry. We show that there is a straightforward equivalent in graph theory which can be efficiently computed. In particular, we give two algorithms for the computation of graph Voronoi diagrams, prove a lower bound on the problem, and we i ..."
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Cited by 14 (0 self)
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The Voronoi diagram is a famous structure of computational geometry. We show that there is a straightforward equivalent in graph theory which can be efficiently computed. In particular, we give two algorithms for the computation of graph Voronoi diagrams, prove a lower bound on the problem, and we identify cases where the algorithms presented are optimal. The space requirement of a graph Voronoi diagram is modest, since it needs no more space than the graph itself. The investigation of graph Voronoi diagrams is motivated by many applications and problems on networks that can be easily solved with their help. This includes the computation of nearest facilities, all nearest neighbors and closest pairs, some kind of collision free moving, and anticenters and closest points. 1 Introduction The Voronoi diagram is a data structure extensively investigated in the domain of computational geometry [14]. Originally, it characterizes regions of proximity for a set of k sites in the pl...
M.: Strategyproof approximation of the minimax on networks
 Math. Oper. Res
"... We consider the problem of locating a facility on a network, represented by a graph. A set of strategic agents have different ideal locations for the facility; the cost of an agent is the distance between its ideal location and the facility. A mechanism maps the locations reported by the agents to t ..."
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Cited by 11 (1 self)
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We consider the problem of locating a facility on a network, represented by a graph. A set of strategic agents have different ideal locations for the facility; the cost of an agent is the distance between its ideal location and the facility. A mechanism maps the locations reported by the agents to the location of the facility. We wish to design mechanisms that are strategyproof, in the sense that agents can never benefit by lying, and at the same time provide a small approximation ratio with respect to the minimax measure. We design a novel “hybrid ” strategyproof randomized mechanism that provides a tight approximation ratio of 3/2 when the network is a circle (known as a ring in the case of computer networks). Furthermore, we show that no randomized SP mechanism can provide an approximation ratio better than 2 − o(1) even when the network is a tree, thereby matching a trivial upper bound of two. 1
Distributed Network Storage Service with QualityofService Guarantees
, 1999
"... This paper envisions a distributed network storage service with QualityofService (QoS) guarantees, and describes its architecture and key mechanisms. When fully realized, this service architecture would be able to support, in one integrated framework, network storage services ranging from besteffo ..."
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Cited by 10 (1 self)
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This paper envisions a distributed network storage service with QualityofService (QoS) guarantees, and describes its architecture and key mechanisms. When fully realized, this service architecture would be able to support, in one integrated framework, network storage services ranging from besteffort caching to replication with performance guarantees. Content owners could, through the use of standardized protocols, reserve network storage resources to satisfy their applicationspecific performance requirements. They would be able to specify either the number and/or placement of the replicas, or higherlevel performance goals based on access latency, bandwidth usage or data availability. The network storage provider would then optimally allocate storage resources to meet the service commitments, using leftover capacity for besteffort caching. Content consumers would then retrieve the nearest copy of the data object, be it from a replica, cache, or the original source, in a completely ...
Resource Allocation for storserv : Network Storage Services with QoS Guarantees
, 1999
"... There is increasing demand from content providers for distributed network storage services that go beyond traditional caching and replication. Through the storserv architecture [1], content providers can obtain storage services with QualityofService (QoS) guarantees to satisfy their applications ..."
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Cited by 8 (1 self)
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There is increasing demand from content providers for distributed network storage services that go beyond traditional caching and replication. Through the storserv architecture [1], content providers can obtain storage services with QualityofService (QoS) guarantees to satisfy their applicationspecific performance requirements. This paper presents a formal resource allocation model for the storserv architecture. The model can also be extended to solve network storage capacity planning problems. By applying the model to the ARPANET network topology, we are able to make the following observations: (i) services with deterministic guarantees require more network resources than those with statistical or stochastic guarantees; (ii) knowledge of nonuniformity in data access patterns may be exploited to achieve more efficient usage of network resources; (iii) partial replication of collections can improve mapping efficiency; (iv) the number and placement of storage servers in the network...
Stochastic pRobust Location Problems
, 2004
"... Many objectives have been proposed for optimization under uncertainty. The typical stochastic programming objective of minimizing expected cost may yield solutions that are inexpensive in the long run but perform poorly under certain realizations of the random data. On the other hand, the typical ro ..."
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Cited by 8 (3 self)
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Many objectives have been proposed for optimization under uncertainty. The typical stochastic programming objective of minimizing expected cost may yield solutions that are inexpensive in the long run but perform poorly under certain realizations of the random data. On the other hand, the typical robust optimization objective of minimizing maximum cost or regret tends to be overly conservative, planning against a disastrous but unlikely scenario. In this paper, we present facility location models that combine the two objectives by minimizing the expected cost while bounding the relative regret in each scenario. In particular, the models seek the minimumexpectedcost solution that is probust; i.e., whose relative regret is no more than 100p% in each scenario.
A Lagrangean/Surrogate Heuristic For The Maximal Covering Location Problem Using Hillsman's Edition
 International Journal of Industrial Engineering
, 2001
"... This paper assess the quality of the combined approach using the Lagrangean/surrogate heuristic of Senne and Lorena (2000) for solving pMP's in problem instances with both random generated and real world data. ..."
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Cited by 6 (3 self)
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This paper assess the quality of the combined approach using the Lagrangean/surrogate heuristic of Senne and Lorena (2000) for solving pMP's in problem instances with both random generated and real world data.