The gradual covering decay location problem on a network

Berman, Oded; Krass, Dmitry; Drezner, Zvi

doi:10.1016/s0377-2217(02)00604-5

Cited by 171 publications

(90 citation statements)

References 10 publications

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“…While this is a step in the right direction, it is ad-hoc and rather limited. More recently, Karasakal and Karasakal (2004) and Berman, Krass, and Drezner (2003) independently introduced coverage models where coverage decays gradually with distance. Karasakal and Karasakal (2004) focus on algorithmic issues, and they design a Lagrangian heuristic to solve the problem.…”

Section: Generalizations Of Coverage Modelsmentioning

confidence: 99%

“…In their computational experiments, they assume that coverage changes from 1 (full coverage) to 0 (no coverage) in a narrower interval than would be appropriate for the context we focus on, where survival probability might decay gradually from around 30% to 5% when response time varies from 0 to 10 minutes. Berman, Krass, and Drezner (2003) present a structural result that allows one to limit candidate locations in a network to a finite set without loss of generality. Then, they show how the problem can be formulated as an uncapacitated facility location problem and they also provide an alternative and more efficient formulation.…”

Section: Generalizations Of Coverage Modelsmentioning

confidence: 99%

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Ambulance location for maximum survival

Erkut

Ingolfsson

Erdoğan

2007

Naval Research Logistics

195

139

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This paper proposes new location models for emergency medical service stations. The models are generated by incorporating a survival function into existing covering models. A survival function is a monotonically decreasing function of the response time of an EMS vehicle to a patient that returns the probability of survival for the patient. The survival function allows for the calculation of tangible outcome measures-the expected number of survivors in case of cardiac arrests. The survival-maximizing location models are better suited for EMS location than the covering models which do not adequately differentiate between consequences of different response times. We demonstrate empirically the superiority of the survival-maximizing models using data from the Edmonton EMS system.

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Section: Generalizations Of Coverage Modelsmentioning

confidence: 99%

Section: Generalizations Of Coverage Modelsmentioning

confidence: 99%

Ambulance location for maximum survival

Erkut

Ingolfsson

Erdoğan

2007

Naval Research Logistics

195

139

View full text Add to dashboard Cite

show abstract

“…One active area of research has involved relaxing key assumptions in the basic location models outlined earlier. For example, Berman et al [2] consider a gradual covering model in which a demand is fully covered if the nearest facility is within l, uncovered if the nearest facility is further than u and partially covered if the nearest facility is between l and u distance units away. They show that, for convex decay functions, the maximal gradual covering problem can be restructured as a special case of the UFLP.…”

Section: Where To From Here?mentioning

confidence: 99%

What you should know about location modeling

Daskin

2008

Naval Research Logistics

204

110

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Facility location models have been applied to problems in the public and private sectors for years. In this article, the author first presents a taxonomy of location problems based on the underlying space in which the problem is embedded. The article illustrates problems from each part of the taxonomy with an emphasis on discrete location problems. Selected recent research in the area is also discussed.

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“…Up to a distance r there is no decay due to distance (f (d) = 1), beyond a distance R > r no customer is attracted to the facility (f (d) = 0), and between r and R decline is linear. A general decay function in the context of gradual cover was analyzed in Berman and Krass [10] and Berman et al [14].…”

Section: The Distance Decay Functionmentioning

confidence: 99%

“…However, in some cases there were solutions with a relative error as high as 15%. (14)- (15)) where the only change is that f p ∆ p replaces f p in (14). Let the solution value of this problem be denoted by V 1 .…”

Section: The Greedy Heuristicmentioning

confidence: 99%

Modeling Competitive Facility Location Problems: New Approaches and Results

Berman¹,

Drezner²,

Drezner³

et al. 2009

Decision Technologies and Applications

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The gravity model as applied to competitive facility location is described in this tutorial. The gravity model is used mainly by marketers to estimate the market share attracted by competing retail facilities. A demand area consists of potential customers who spend their discretionary buying power at competing retail facilities. We discuss the gravity model and issues related to the determination of the parameters of the models. The basic model is to find the best locations for new facilities that would attract the maximum buying power from customers in the area. Extensions to this basic model include modeling uncertainty about future market conditions, minimizing the probability that the attracted market share falls short of a certain target, demand generated by intercepting a flow of customers, market expansion and lost demand, and allocation of a given budget among one's facilities to improve their attractiveness. We describe special tools that are utilized in the solution procedures. These are the generalized Weiszfeld procedure, the big triangle small triangle global optimization approach, and the tangent line approximation.

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The gradual covering decay location problem on a network

Cited by 171 publications

References 10 publications

Ambulance location for maximum survival

Ambulance location for maximum survival

What you should know about location modeling

Modeling Competitive Facility Location Problems: New Approaches and Results

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