Location and allocation of service units on a congested network

Aboolian, Robert; Berman, Oded; Drezner, Zvi

doi:10.1080/07408170701411385

Cited by 52 publications

(36 citation statements)

References 16 publications

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“…The input parameters are produced randomly in the following intervals in the ten test problems: the travel time between demand areas and health centers in the interval [0.25 -1.25] (hour), the travel time between health centers in the interval [0.2-1] (hour), the demand rates for four services per hour in the intervals [15][16][17][18][19][20][21][22][23][24][25], [10 -20], [5][6][7][8][9][10] and [3][4][5][6][7][8][9][10] respectively. The average service rate for four services is 6, 5, 5 and 4 patients per hour, standard waiting time in the system is 25, 30, 35 and 35 minutes and the minimum arrival rate required to provide services is 4, 3, 3 and 1 patient(s) per hour respectively.…”

Section: Computational Resultsmentioning

confidence: 99%

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A multi-service Healthcare Network Design with Patients' Choice to Exchange between Services

Radman¹,

Eshghi²

2016

Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

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Abstract-Efficient location of medical services is an issue of paramount importance in healthcare strategic planning. In this research, a mathematical model is developed for the location of multi-service health centers assuming probabilistic demand and service time. Since patients may be shifted to another service after receiving a service by doctors' order, health system is considered a Jackson queue network. The primary factor contributing to patients' choice of one center over another is their proximity to the center. The proposed model seeks to minimize the demand weighted total distance traveled by patients between their residential areas and health centers and also between health centers on the one hand, and the weighted sum of undesired deviations from standard arrival rates at service stations on the other hand. The location of health centers as well as the type of services they offer and the number of servers at each service station are the main determinants of the proposed model. A GAbased heuristic is developed to solve medium and large instances of the proposed model. For evaluation of the suggested heuristic, computational experiments are performed on a number of test problems.

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Section: Computational Resultsmentioning

confidence: 99%

“…In optimal choice models, patients choose the center with the maximum utility. For instance, patients choose to go to the nearest health center [9]. In probabilistic choice models, patients choose every center according to the probability calculated for it [5], [10].…”

Section: Related Literaturementioning

confidence: 99%

A multi-service Healthcare Network Design with Patients' Choice to Exchange between Services

Radman¹,

Eshghi²

2016

Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

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“…Aboolian et al (2008a) examined the same problem but with an objective that minimizes the maximum sum of travel and waiting time costs. Aboolian et al (2008b) generalized the results of Berman and Drezner (2007) by including in the objective function in addition to the expected travel and waiting cost, also the fixed cost of opening facilities and the variable cost of the servers. Castillo et al (2009) studied a problem similar to that of Aboolian et al (2008b).…”

Section: Congested Facilities With Immobile Serversmentioning

confidence: 99%

“…Aboolian et al (2008b) generalized the results of Berman and Drezner (2007) by including in the objective function in addition to the expected travel and waiting cost, also the fixed cost of opening facilities and the variable cost of the servers. Castillo et al (2009) studied a problem similar to that of Aboolian et al (2008b). In their model, there is a centralized authority that determines the assignment of customers to the facilities (whereas Aboolian et al (2008b) assign customers to the closest facility).…”

Section: Congested Facilities With Immobile Serversmentioning

confidence: 99%

Hub Location Problems: The Location of Interacting Facilities

Kara

Taner

2011

International Series in Operations Research &Amp; Management Science

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), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. PrefaceThis book is the final result of a number of incidents that occurred to us while discussing location issues with colleagues at conferences. Frequently we attended presentations, in which the authors quoted the well-known references that helped to make the discipline what it is today. Upon further inquiry, though, it turned out that some of these colleagues had never actually read the original papers. We then discussed among ourselves which contributions could be credited with shaping the field. And, lo and behold, we found that we, too, had neglected to read some of the papers that form the foundation of our science. Whether it was laziness or other things that got in the way, it had become clear that something had to be done. Our first thought was to collect the original contributions (once we could agree on what they were) and reprint them. When discussing this possibility with a publisher, we immediately ran into a roadblock in the form of copyright. While this appeared to have stopped our enthusiastic effort dead in its tracks, we kept on collecting and reading what we considered original contributions.This went on until we met Camille Price, who suggested that, rather than reprinting the original contributions, we should invite some of the leaders in our field and ask them to describe the original contribution, explain and interpret it, and comment on the impact that it had to the field. This was, of course, an excellent idea, and the response by our colleagues to our pertinent requests was equally enthusiastic. What you hold in your hands is the result of this effort.In other words, the purpose of this book is to provide easy access to the main contributions to location theory. The book is organized as follows. The introductory chapter provides an overview of some of the many facets of location analysis. This is followed by contributions in the main three fields of inquiry: minisum, minimax, and covering problems. The next chapters are part of an ever-growing list of nonstandard location models: models including competitive components, those that locate undesirable facilities, those with probabilistic features, and those that allow interactions between facilities. The following chapters discuss solution techniques: after a discussion of exact and heuristic techniques, we devote an entire chapter to Weiszfeld's method, and another to Lagrangean techniques. The last chapters of this book deal with the spheres of influence that the facilities generate and that attract viii customers to them, an...

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“…However, as Marianov and ReVelle (1994) point out, such descriptive queueing models usually fix the locations of ambulances a priori. There are recent developments in queueing-based location models (see, e.g., Berman and Krass (2002), Aboolian et al (2008), Zhang et al (2010) and references therein), but as Berman and Krass (2002) point out, "one invariably has to make simplifying assumptions and approximations to render the model tractable." 4 requires an integer programming code capable of solving large zero-one problems without special structure.…”

Section: Related Literaturementioning

confidence: 99%

Simultaneous Location of Trauma Centers and Helicopters for Emergency Medical Service Planning

Cho

Jang

Lee

et al. 2014

Operations Research

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This paper studies the problem of simultaneously locating trauma centers and helicopters. The standard approach to locating helicopters involves the use of helicopter busy fractions to model the random availability of helicopters. However, busy fractions cannot be estimated a priori in our problem because the demand for each helicopter cannot be determined until the trauma center locations are selected. To overcome this challenge, we endogenize the computation of busy fractions within an optimization problem. The resulting formulation has non-convex bilinear terms in the objective, for which we develop an integrated method that iteratively solves a sequence of problem relaxations and restrictions. Specifically, we devise a specialized algorithm, called the Shifting Quadratic Envelopes algorithm, that 1) generates tighter outer-approximations than linear McCormick envelopes, and 2) outperforms a Benders-like cut generation scheme. We apply our integrated method to the design of a nationwide trauma care system in Korea. By running a trace-based simulation on a full year of patient data, we find that the solutions generated by our model outperform several benchmark heuristics by up to 20%, as measured by an industry-standard metric: the proportion of patients successfully transported to a care facility within one hour. Our results have helped the Korean government to plan its nationwide trauma care system. More generally, our method can be applied to a class of optimization problems that aim to find the locations of both fixed and mobile servers when service needs to be carried out within a certain time threshold.

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Location and allocation of service units on a congested network

Cited by 52 publications

References 16 publications

A multi-service Healthcare Network Design with Patients' Choice to Exchange between Services

A multi-service Healthcare Network Design with Patients' Choice to Exchange between Services

Hub Location Problems: The Location of Interacting Facilities

Simultaneous Location of Trauma Centers and Helicopters for Emergency Medical Service Planning

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