What you should know about location modeling

Daskin, Mark S.

doi:10.1002/nav.20284

Cited by 203 publications

(127 citation statements)

References 60 publications

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“…Drezner and Hamacher [11] gave a state-of-the-art review of available location models. A visionary paper by Daskin [8] offered an answer on the question what one should know about location modelling, and presented a taxonomy of location problems.…”

Section: General Modelsmentioning

confidence: 99%

Fuzzy logic based algorithms for maximum covering location problems

Batanovic

Petrov́ić

Petrović

2009

Information Sciences

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Section: General Modelsmentioning

confidence: 99%

Fuzzy logic based algorithms for maximum covering location problems

Batanovic

Petrov́ić

Petrović

2009

Information Sciences

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“…In covering models, a critical distance is established and cannot be exceeded for demand to be considered satisfied. Given the critical distance, the objective may be the maximization of the number of demand points that are within the covering distance (maximum covering model) (Marianov and Serra 2004) or the minimization of the total number of facilities (set covering model) (Daskin 1995). Otherwise, the objective of covering models can be the minimization of the covering distance (centre model).…”

Section: Location Modelsmentioning

confidence: 99%

“…The review by Marianov and Serra (2004) focuses on public sector facility location problems. Daskin (2008) identified two large groups of discrete location models: covering and median-based models. In covering models, a critical distance is established and cannot be exceeded for demand to be considered satisfied.…”

Section: Location Modelsmentioning

confidence: 99%

Organizing hospitals into networks: a hierarchical and multiservice model to define location, supply and referrals in planned hospital systems

2011

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Health care planners in countries with a system based on a National Health Service (NHS) have to make decisions on where to locate and how to organize hospital services, so as to improve the geographic equity of access in the delivery of care while accounting for efficiency and cost issues. This study proposes a hierarchical multiservice mathematical programming model to inform decisions on the location and supply of hospital services, when the decision maker wants to maximize patients' geographical access to a hospital network. The model considers the multiservice structure of hospital production (with hospitals producing inpatient care, emergency care and external consultations) and the costs associated with reorganizing the hospital network. Moreover, it considers the articulation between different hospital services and between hospital units, and the ascendant and descendent flows related to twoway referrals of patients in the hospital hierarchy. The proposed approach differs from previous literature by accounting simultaneously for these issues and provides crucial information for health care planners on referral networks, on hospital catchment areas, on the location and structure of hospital supply as well as on the costs required to improve access. The results from applying the model are illustrated in an application to the South region of the Portuguese NHS. Three scenarios are portrayed to describe how the model can be used in distinct institutional settings and policy contexts and when there is uncertainty concerning the key parameters of the model.

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“…Even in this simple case, the resulting problem is a typical NP-hard facility location optimization problem [13], [14] thus turning our attention to heuristic-based solutions. To further explore the problem space and guide the design of our heuristics, we first decompose T e2e into its constituents and investigate the most important factors impacting them (Section III).…”

Section: B Problem Statementmentioning

confidence: 99%

Cost-Efficient Low Latency Communication Infrastructure for Synchrophasor Applications in Smart Grids

Yang

Κατσαρός

Chai

et al. 2018

IEEE Systems Journal

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Abstract-With the introduction of distributed renewable energy resources and new loads, such as electric vehicles, the power grid is evolving to become a highly dynamic system, that necessitates continuous and fine-grained observability of its operating conditions. In the context of the medium voltage (MV) grid, this has motivated the deployment of Phasor Measurement Units (PMUs), that offer high precision synchronized grid monitoring, enabling mission-critical applications such as fault detection/location. However, PMU-based applications present stringent delay requirements, raising a significant challenge to the communication infrastructure. In contrast to the high voltage domain, there is no clear vision for the communication and network topologies for the MV grid; a full fledged optical fiber-based communication infrastructure is a costly approach due to the density of PMUs required. In this work, we focus on the support of low-latency PMU-based applications in the MV domain, identifying and addressing the trade-off between communication infrastructure deployment costs and the corresponding performance. We study a large set of real MV grid topologies to get an in-depth understanding of the various key latency factors. Building on the gained insights, we propose three algorithms for the careful placement of high capacity links, targeting a balance between deployment costs and achieved latencies. Extensive simulations demonstrate that the proposed algorithms result in low-latency network topologies while reducing deployment costs by up to 80% in comparison to a ubiquitous deployment of costly high capacity links.

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What you should know about location modeling

Cited by 203 publications

References 60 publications

Fuzzy logic based algorithms for maximum covering location problems

Fuzzy logic based algorithms for maximum covering location problems

Organizing hospitals into networks: a hierarchical and multiservice model to define location, supply and referrals in planned hospital systems

Cost-Efficient Low Latency Communication Infrastructure for Synchrophasor Applications in Smart Grids

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