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27
Choosing Replica Placement Heuristics for WideArea Systems
 In ICDCS ’04: Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS’04
, 2004
"... Data replication is used extensively in widearea distributed systems to achieve low dataaccess latency. A large number of heuristics have been proposed to perform replica placement. Practical experience indicates that the choice of heuristic makes a big difference in terms of the cost of required ..."
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Cited by 40 (0 self)
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Data replication is used extensively in widearea distributed systems to achieve low dataaccess latency. A large number of heuristics have been proposed to perform replica placement. Practical experience indicates that the choice of heuristic makes a big difference in terms of the cost of required infrastructure (e.g., storage capacity and network bandwidth), depending on system topology, workload and performance goals.
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.
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 Modeling Framework for Facility Location of Medical Services for LargeScale Emergencies
, 2005
"... Research on facility location is abundant. However, this research does not typically address the particular conditions that arise when locating facilities to service largescale emergencies, such as earthquakes, terrorist attacks, etc. In this work we first survey general facility location problem ..."
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Cited by 8 (2 self)
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Research on facility location is abundant. However, this research does not typically address the particular conditions that arise when locating facilities to service largescale emergencies, such as earthquakes, terrorist attacks, etc. In this work we first survey general facility location problems and identify models used to address common emergency situations, such as house fires and regular health care needs. We then analyze the characteristics of largescale emergencies and propose a general facility location model that is suited for largescale emergencies. This general facility location model can be cast as a covering model, a Pmedian model or a Pcenter model, each suited for different needs in a largescale emergency. Illustrative examples are given to show how the proposed model can be used to optimize the locations of facilities for medical supplies to address largescale emergencies in the Los Angeles area. Furthermore, comparison of the solutions obtained by respectively using the proposed model and the traditional model is given to show the benefits of the proposed model in reducing lifeloss and economic loss.
MixedInteger Nonlinear Programming Models and Algorithms for LargeScale Supply
 Chain Design with Stochastic Inventory Management. Industrial & Engineering Chemistry Research 2008
"... An important challenge for most chemical companies is to simultaneously consider inventory optimization and supply chain network design under demand uncertainty. This leads to a problem that requires integrating a stochastic inventory model with the supply chain network design model. This problem ca ..."
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Cited by 6 (5 self)
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An important challenge for most chemical companies is to simultaneously consider inventory optimization and supply chain network design under demand uncertainty. This leads to a problem that requires integrating a stochastic inventory model with the supply chain network design model. This problem can be formulated as a large scale combinatorial optimization model that includes nonlinear terms. Since these models are very difficult to solve, they require exploiting their properties and developing special solution techniques to reduce the computational effort. In this work, we analyze the properties of the basic model and develop solution techniques for a joint supply chain network design and inventory management model for a given product. The model is formulated as a nonlinear integer programming problem. By reformulating it as a mixedinteger nonlinear programming (MINLP) problem and using an associated convex relaxation model for initialization, we first propose a heuristic method to quickly obtain good quality solutions. Further, a decomposition algorithm based on Lagrangean relaxation is developed for obtaining global or nearglobal optimal solutions. Extensive computational examples with up to 150 distribution centers and 150 retailers are presented to illustrate the performance of the algorithms and to compare them with the fullspace solution. To whom all correspondence should be addressed.
Discrete Optimization Methods and their Role in the Integration of Planning and Scheduling
 AICHE SYMPSIUM SERIES
, 2002
"... The need for improvement in process operations, logistics and supply chain management has created a great demand for the development of optimization models for planning and scheduling. In this paper we first review the major classes of planning and scheduling models that arise in process operations, ..."
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Cited by 5 (2 self)
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The need for improvement in process operations, logistics and supply chain management has created a great demand for the development of optimization models for planning and scheduling. In this paper we first review the major classes of planning and scheduling models that arise in process operations, and establish the underlying mathematical structure of these problems. As will be shown, the nature of these models is greatly affected by the time representation (discrete or continuous), and is often dominated by discrete decisions. We then briefly review the major recent developments in mixedinteger linear and nonlinear programming, disjunctive programming and constraint programming, as well as general decomposition techniques for solving these problems. We present a general formulation for integrating planning and scheduling to illustrate the models and methods discussed in this paper.
Planning for disruptions in supply chain networks
 TutORials in Operations Research. INFORMS
, 2006
"... Abstract Recent events have highlighted the need for planners to consider the risk of disruptions when designing supply chain networks. Supply chain disruptions have a number of causes and may take a number of forms. Once a disruption occurs, there is very little recourse regarding supply chain infr ..."
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Cited by 5 (1 self)
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Abstract Recent events have highlighted the need for planners to consider the risk of disruptions when designing supply chain networks. Supply chain disruptions have a number of causes and may take a number of forms. Once a disruption occurs, there is very little recourse regarding supply chain infrastructure because these strategic decisions cannot be changed quickly. Therefore, it is critical to account for disruptions during the design of supply chain networks so that they perform well even after a disruption. Indeed, these systems can often be made substantially more reliable with only small additional investments in infrastructure. Planners have a range of options available to them in designing resilient supply chain networks, and their choice of approaches will depend on the financial resources available, the decision maker’s risk preference, the type of network under consideration, and other factors. In this tutorial, we present a broad range of models for designing supply chains resilient to disruptions. We first categorize these models by the status of the existing network: A network may be designed from scratch, or an existing network may be modified to prevent disruptions at some facilities. We next divide each category based on the underlying optimization model (facility location or network design) and the risk measure (expected cost or worstcase cost).
Bounds on the Replication Cost for QoS
, 2003
"... Data replication is used extensively in widearea distributed systems to achieve low dataaccess latency. Minimizing the cost of the resources used for replication is a key problem in these systems. The paper proposes a method to calculate lower bounds for the replication cost required to achieve ce ..."
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Cited by 2 (1 self)
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Data replication is used extensively in widearea distributed systems to achieve low dataaccess latency. Minimizing the cost of the resources used for replication is a key problem in these systems. The paper proposes a method to calculate lower bounds for the replication cost required to achieve certain QoS goals. We obtain bounds for the general case as well as for certain classes of replica placement heuristics. We observe that the cost of heuristics depends heavily on the workload and QoS goal. Based on these results, we discuss the inherent properties of heuristics that affect their cost and applicability to different environments.
Abstract Distribution network design with postponement
"... An important concern in supply chain management is about network design, involving factories, central warehouses, regional warehouses as well as customers. The best strategy has to be ascertained for distributing products within this network. The objective is to select the optimal numbers and locati ..."
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Cited by 1 (1 self)
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An important concern in supply chain management is about network design, involving factories, central warehouses, regional warehouses as well as customers. The best strategy has to be ascertained for distributing products within this network. The objective is to select the optimal numbers and locations of central and regional warehouses such that all customer demands are satisfied at minimum total costs of the network. An extension of existing approaches is to take into account aspects of postponement, in particular regarding the problem of postponing activities like assembling halffinished goods or packaging them. A mixed integer programming model is provided and it is demonstrated that the resulting formulation can be used to solve realistic problem instances with commercially available mathematical programming software. 1