Covering Problem

Fallah, Hamed; Sadigh, Ali Naimi; Aslanzadeh, Marjan

doi:10.1007/978-3-7908-2151-2_7

Cited by 17 publications

(10 citation statements)

References 13 publications

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“…The model will take this into account by a covering constraint that requires opening at least one transplant center within a maximum driving time from each municipality in a country. We refer to, e.g., Toregas and ReVelle [29], Rajagopalona et al [25], and Fallah et al [12], for more information on covering constraints in general. Note that this covering constraint can be seen as a guaranteed service level offered to the recipient.…”

Section: Specificities Of the Organ Transplant Processmentioning

confidence: 99%

Optimizing the facility location design of organ transplant centers

Beliën¹,

Boeck²,

Colpaert³

et al. 2013

Decision Support Systems

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a b s t r a c t a r t i c l e i n f o Available online xxxx Keywords:Facility location Organ transplant Long-term planning Mixed integer linear programming This paper presents a mixed integer linear programming (MILP) long-term decision model to optimize the location of organ transplant centers. The objective is to minimize the sum of the weighted time components between the moment a donor organ becomes available and its transplantation into the recipient's body. The weight factor for the elapsed time before the organ's removal from the donor body allows to assign a lower weight to this time component in the objective function in order to reflect the criticality of the process after the organ's removal. The specificity of organ transplants makes the model more complex than a traditional facility location model. The model is applied to the Belgian organ transplant path. Extensive numerical experiments reveal the key factors that impact the long-term decision of centralizing versus decentralizing transplant centers.

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Section: Specificities Of the Organ Transplant Processmentioning

confidence: 99%

Optimizing the facility location design of organ transplant centers

Beliën¹,

Boeck²,

Colpaert³

et al. 2013

Decision Support Systems

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“…Using this approach, a customer can be served if a facility is located within a given threshold distance or travel time from it (Francis et al, 1992). This threshold is called the coverage distance or coverage radius (Fallah et al, 2009). Schilling et al (1993) classify models which use the concept of covering in two categories: 1) Set Covering Problem (SCP) where coverage is required and tries to minimize location cost while satisfying a specified level of coverage.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling Facility Location Problems in Emerging Markets: The Case of The Public Healthcare Sector in Morocco

Mokrini

Boulaksil

Berrado

2019

OSCM: An Int. Journal

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Locating facilities in an emerging market involves specific characteristics that are different from developed countries. The differences between these two settings suggest that facility location modelling can present substantially different challenges when applied to an emerging market as opposed to a developed country. In this study, we develop a variant of the set covering model that explicitly takes specific characteristics into consideration. The model has been constructed and validated based on a real-life case study concerning the Moroccan Ministry of Health which is currently considering the redesign of its supply chain of pharmaceutical products. Amongst the findings, this paper shows that road infrastructure and demand dispersion affect the strategic decision of facility locations.

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“…Constraint 2is a function of fixed investment between waterworks system and the actual amount of water; constraint (3) is the balance between supply and demand of water supply and water demand; constraint equation 4represents a water demand point which is provided by one water plant; the relationship between the two is "many-to-one"; constraint (6) means when there is no demand for water, water purification plant is not enabled, but as long as water demand is present, water purification plants must be enabled; constraint (7) means the water plant size control; constraint (8) the amount of water supply is nonnegative. Constraint (9) is the decision variables whether the water purification plant is enabled; constraint (10) is decision variables whether the demand for water is transported to the water purification plant; constraint (11) is decision variables whether a waterworks takes water from one source; constraint (12) indicates within the water demand points of plant , whether to arrange pipes between water demand points and .…”

Section: Nested Site Selection Model For Waterwork Based On the Mostmentioning

confidence: 99%

“…If the distance between the two is less than or equal to a predetermined distance, the facility can provide service. This is called covered distance or coverage radius [12][13][14][15]. Determining the coverage radius will need apriority or practical experience and many of the studies mentioned above usually give directly coverage radius to be used to solve the covering problem, but do not discuss in detail how to reasonably determine the coverage radius.…”

Section: Introductionmentioning

confidence: 99%

The Nested Site Selection Model for Water Treatment Plants Based on the Optimization of Water Supply Radius

Zhang

2014

Abstract and Applied Analysis

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This paper analyzes the site selection problems of water treatment plants by utilizing the set covering model. Fully consider the influence of the pipe network arrangement on the site selection when confirming the covering radius, analyze the best water supply radius of water treatment plants combining investment benefits and pipe network optimization theory, and take the best water supply radius as the covering radius. Make the pipe network optimization nest into the site selection problem meanwhile confirming the covering radius, which fully reflect the viewpoints of the integrated logistics arrangement system. Considering the multiple solutions of the set covering model, this paper introduces the model of which the cost’s present value is minimum to make the quadratic optimization and get the best site selection results of water treatment plant. At last, this paper verifies the model by combining with the cases of water supply project construction of a county; the results prove that the model is feasible and effective. This paper expects to provide some reference for the planning design of regionally centralized water supply projects concerning villages and small towns and the site selection and construction of water treatment plant.

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Covering Problem

Cited by 17 publications

References 13 publications

Optimizing the facility location design of organ transplant centers

Optimizing the facility location design of organ transplant centers

Modelling Facility Location Problems in Emerging Markets: The Case of The Public Healthcare Sector in Morocco

The Nested Site Selection Model for Water Treatment Plants Based on the Optimization of Water Supply Radius

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