The generalized maximal covering location problem

Berman, Oded; Krass, Dmitry

doi:10.1016/s0305-0548(01)00079-x

Cited by 237 publications

(133 citation statements)

References 12 publications

Supporting

Mentioning

128

Contrasting

Unclassified

Order By: Relevance

“…Berman and Krass (2002) introduce the generalized maximal coverage location problem where different levels of coverage exist, defined by a decreasing step function of the distance to the closest facility. Marianov and Taborga (2001) consider the problem of locating public health centres providing subsidized services to low-income patients.…”

Section: Literature Reviewmentioning

confidence: 99%

Matching patient and physician preferences in designing a primary care facility network

Güneş

Yaman

Çekyay

et al. 2014

Journal of the Operational Research Society

View full text Add to dashboard Cite

This paper introduces an integer programming model for planning primary care facility networks, which accounts for the interests of different stakeholders while maximizing access to health care. Physician allocation to health-care facilities is explicitly modelled, which allows consideration of physician incentives in the planning phase. An illustrative case study in the Turkish primary care system is presented to show the implications of focusing on patient or physician preferences in the planning phase. A discussion of trade-offs between the different stakeholder preferences and some recommendations for modelling choices to match these preferences are provided. In the context of this case, we found that using an access measure that decays with distance, and incorporating nearest allocation constraints improves performance for all stakeholders. We also show that increasing the number of physicians may have adverse affects on access measures when physician preferences are addressed.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Matching patient and physician preferences in designing a primary care facility network

Güneş

Yaman

Çekyay

et al. 2014

Journal of the Operational Research Society

View full text Add to dashboard Cite

show abstract

“…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%

“…Problem (31)- (32) has the same structure as the generalized max-cover location problem (GMCLP) introduced in Berman and Krass [10]. It is shown in that paper that GMCLP is equivalent to the uncapacitated location problem whose LP relaxation very often yields all-integer optimal solutions.…”

Section: The Models With Stepwise Expenditure Functionsmentioning

confidence: 99%

Modeling Competitive Facility Location Problems: New Approaches and Results

Berman¹,

Drezner²,

Drezner³

et al. 2009

Decision Technologies and Applications

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…They used a combination of column generation and covering graph approaches in order to solve this problem. Berman and Krass [7] considered partial coverage of customers for a general class of MCLP. Batanovic, Petrovic, and Petrovic [8] suggested maximal covering location problems in networks with uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Solving Health Care Facility Location Problems with New Heuristic Algorithm Method

Valipour¹,

Nedjati²,

Kazemi³

2013

IJMO

View full text Add to dashboard Cite

Abstract-In recent years, there has been much conservation about the issue healthcare location problem. In this paper we focus on emergency aspect of healthcare that related to allocate facilities. The best locations for some health care facilities are allocated according to different covering distances. We present IPSO (Improved Particle swarm optimization), as a powerful heuristic algorithm to maximizes the population assigned to a facility within the coverage distance. The proposed method was compared with the results of the LINGO software, GA. The results of this comparison show that IPSO can achieve better results for the solution in a faster time.

show abstract

The generalized maximal covering location problem

Cited by 237 publications

References 12 publications

Matching patient and physician preferences in designing a primary care facility network

Matching patient and physician preferences in designing a primary care facility network

Modeling Competitive Facility Location Problems: New Approaches and Results

Solving Health Care Facility Location Problems with New Heuristic Algorithm Method

Contact Info

Product

Resources

About