Results 1  10
of
77
Facility location and supply chain management – A review.
 European Journal of Operational Research,
, 2009
"... a b s t r a c t Facility location decisions play a critical role in the strategic design of supply chain networks. In this paper, a literature review of facility location models in the context of supply chain management is given. We identify basic features that such models must capture to support d ..."
Abstract

Cited by 60 (0 self)
 Add to MetaCart
(Show Context)
a b s t r a c t Facility location decisions play a critical role in the strategic design of supply chain networks. In this paper, a literature review of facility location models in the context of supply chain management is given. We identify basic features that such models must capture to support decisionmaking involved in strategic supply chain planning. In particular, the integration of location decisions with other decisions relevant to the design of a supply chain network is discussed. Furthermore, aspects related to the structure of the supply chain network, including those specific to reverse logistics, are also addressed. Significant contributions to the current stateoftheart are surveyed taking into account numerous factors. Supply chain performance measures and optimization techniques are also reviewed. Applications of facility location models to supply chain network design ranging across various industries are presented. Finally, a list of issues requiring further research are highlighted.
MyExperience: A System for
 In Situ Tracing and Capturing of User Feedback on Mobile Phones. Proceedings of MobiSys 2007
, 2007
"... Abstract—With the protiferation of highspeed networks and networked services, prov~loning dfierentiated serviees to a d]verse user base with heterogeneous QoS requirements has beeome an important]problem. The traditional approach of resouree reservation and admiksion control provides both guarantee ..."
Abstract

Cited by 37 (10 self)
 Add to MetaCart
(Show Context)
Abstract—With the protiferation of highspeed networks and networked services, prov~loning dfierentiated serviees to a d]verse user base with heterogeneous QoS requirements has beeome an important]problem. The traditional approach of resouree reservation and admiksion control provides both guarantees and graded serviee+%however, at the cost of potentially underutilized resources and tindted sealabltity. In thu paper, we describe a WAN QoS prov~]on areMtecture that adaptively organizes beateffort bandwidth into stratified services with graded QoS properties such that the QoS needs of a diverse user base ean be effectively met. Our mdriteetu~BS (Stratitied Besteffort Service)pmmotes a simple user/shnple network reatkation where neither the user nor the network is burdened with complex comprrtationat responsibitities. SBS is scalablq efficient and adaptive, and it complements the guaranteed service archL teeturq sharing a common network substrate comprised of GPS routers. It is also a functional complemen ~ pmvi&oning QoS efficiently commensurate with user needs, albt4t at the cost of weaker pmteetilon. SBS is suited to noncooperative network envimnrnerrts where users belhave seltishly and resouree contention reaohrtion k m~rated by the principle of competitive interaction. A principat feature of SBS is the transformation of usercentric QoS prevision mechanisms—a defining characteristic of competitive interaction entaiting intimate user control of internal networlk rmoureesinto network.eentrie mechanisms while preserving the former’s resouree atloeation
The Stochastic Location Model with Risk Pooling
 EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
, 2007
"... In this paper, we present a stochastic version of the Location Model with Risk Pooling (LMRP) that optimizes location, inventory, and allocation decisions under random parameters described by discrete scenarios. The goal of our model (called the stochastic LMRP, or SLMRP) is to find solutions that m ..."
Abstract

Cited by 28 (4 self)
 Add to MetaCart
(Show Context)
In this paper, we present a stochastic version of the Location Model with Risk Pooling (LMRP) that optimizes location, inventory, and allocation decisions under random parameters described by discrete scenarios. The goal of our model (called the stochastic LMRP, or SLMRP) is to find solutions that minimize the expected total cost (including location, transportation, and inventory costs) of the system across all scenarios. The location model explicitly handles the economies of scale and riskpooling effects that result from consolidating inventory sites. The SLMRP framework can also be used to solve multicommodity and multiperiod problems. We present a Lagrangianrelaxation–based exact algorithm for the SLMRP. The Lagrangian subproblem is a nonlinear integer program, but it can be solved by a loworder polynomial algorithm. We discuss simple variablefixing routines that can drastically reduce the size of the problem. We present quantitative and qualitative computational results on problems with up to 150 nodes and 9 scenarios, describing both algorithm performance and solution behavior as key parameters change.
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 ..."
Abstract

Cited by 22 (4 self)
 Add to MetaCart
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.
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 ..."
Abstract

Cited by 15 (4 self)
 Add to MetaCart
(Show Context)
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).
INTEGRATED SUPPLY CHAIN DESIGN MODELS: A SURVEY AND FUTURE RESEARCH DIRECTIONS
, 2007
"... Optimization models, especially nonlinear optimization models, have been widely used to solve integrated supply chain design problems. In integrated supply chain design, the decision maker needs to take into consideration inventory costs and distribution costs when the number and locations of the f ..."
Abstract

Cited by 15 (2 self)
 Add to MetaCart
(Show Context)
Optimization models, especially nonlinear optimization models, have been widely used to solve integrated supply chain design problems. In integrated supply chain design, the decision maker needs to take into consideration inventory costs and distribution costs when the number and locations of the facilities are determined. The objective is to minimize the total cost that includes location costs and inventory costs at the facilities, and distribution costs in the supply chain. We provide a survey of recent developments in this research area.
Enhancing WLAN capacity by strategic placement of tetherless relay points
 IEEE TRANS. ON MOBILE COMPUTING
, 2007
"... With the proliferation of wireless local area network (WLAN) technologies, wireless Internet access via public hotspots will become a necessity in the near future. In outdoor areas where the installation of a large number of wired access points is practically or economically infeasible, mobile user ..."
Abstract

Cited by 11 (1 self)
 Add to MetaCart
With the proliferation of wireless local area network (WLAN) technologies, wireless Internet access via public hotspots will become a necessity in the near future. In outdoor areas where the installation of a large number of wired access points is practically or economically infeasible, mobile users located at the edge of the network communicate with the access point at a very low rate and in turn waste network resources. In this work, we promote the use of tetherless relay points (TRPs) to improve the throughput of a WLAN in such environments. We first provide a high level description on how to integrate TRPs in a multirate WLAN architecture. We then propose an integerprogramming optimization formulation and an iterative approach to compute the best placement of a fixed number of TRPs. Finally, we show in numerical analysis, through a case study based on relayenabled rate adaptation and IEEE 802.11like multirate physical model with Rayleigh fading, that for a wide range of system parameters, significant performance gain can be achieved when TRPs are strategically installed in the network.
Solution Approaches for Facility Location of Medical Supplies for LargeScale Emergencies
 Computers and Industrial Engineering
, 2007
"... Abstract: In this paper, we propose models and solution approaches for determining the facility locations of medical supplies in response to largescale emergencies. We address the demand uncertainty and medical supply insufficiency by providing each demand point with services from a multiple quanti ..."
Abstract

Cited by 8 (0 self)
 Add to MetaCart
Abstract: In this paper, we propose models and solution approaches for determining the facility locations of medical supplies in response to largescale emergencies. We address the demand uncertainty and medical supply insufficiency by providing each demand point with services from a multiple quantity of facilities that are located at different quality levels (distances). The problem is formulated as a maximal covering problem with multiple facility quantityofcoverage and qualityofcoverage requirements. Three heuristics are developed to solve the location problem: a genetic algorithm heuristic, a locateallocate heuristic, and a Lagrangean relaxation heuristic. We evaluate the performance of the model and the heuristics by using illustrative emergency examples. Suggestions are given on how to select the most appropriate heuristic to solve different location problem instances.
Multimarket facility network design with offshoring applications. Manufacturing Service Oper. Management, ePub ahead of print April 17
, 2008
"... informs doi 10.1287/msom.1070.0198 ..."
A plant location guide for the unsure
, 2008
"... This paper studies an extension of the kmedian problem where we are given a metric space (V, d) and not just one but m client sets {Si ⊆ V} m i=1, and the goal is to open k facilities F to minimize: maxi∈[m] j∈Si d(j, F) �, i.e., the worstcase cost over all the client sets. This is a “minmax ” or ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
(Show Context)
This paper studies an extension of the kmedian problem where we are given a metric space (V, d) and not just one but m client sets {Si ⊆ V} m i=1, and the goal is to open k facilities F to minimize: maxi∈[m] j∈Si d(j, F) �, i.e., the worstcase cost over all the client sets. This is a “minmax ” or “robust ” version of the kmedian problem; however, note that in contrast to previous papers on robust/stochastic problems, we have only one stage of decisionmaking—where should we place the facilities? We present an O(log n+log m) approximation for robust kmedian: The algorithm is combinatorial and very simple, and is based on reweighting/Lagrangeanrelaxation ideas. In fact, we give a general framework for (minimization) facility location problems where there is a bound on the number of open facilities. For robust and stochastic versions of such location problems, we show that if the problem satisfies a certain “projection” property, essentially the same algorithm gives a logarithmic approximation ratio in both versions. We use our framework to give the first approximation algorithms for robust/stochastic versions of ktree, capacitated kmedian, and faulttolerant kmedian.