A review of congestion models in the location of facilities with immobile servers

Boffey, Brian; Galvão, Roberto D.; Espejo, Luis Gonzalo Acosta

doi:10.1016/j.ejor.2006.04.044

Cited by 93 publications

(53 citation statements)

References 55 publications

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“…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]. Huff used the probabilistic choice model to calculate the choice probability of shopping centers in 1963 for the first time [11].…”

Section: Related Literaturementioning

confidence: 99%

“…The high congestion of these centers adversely affects clients' satisfaction, and in emergency cases, it may lead to irreparable damages. To guarantee service quality, we usually deal with one of these factors: (1) average queue length, (2) average waiting time in queues, or (3) the probability of receiving service in a standard time [4], [5]. These factors can be a part of constrains that are called "constraint-oriented" approaches [6], [7].…”

Section: Related Literaturementioning

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: Related Literaturementioning

confidence: 99%

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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“…In this case, a primary work should be made to categorize demands according to criteria such as: the seriousness of the repairing, the importance of losses generated by buses failure. Boffey et al [14] proposed a review of congestion models in location.…”

Section: Dynamic Assignment Of Breakdown Mechanicsmentioning

confidence: 99%

Breakdown Mechanic Location Problem

Djiba¹,

Baldé²,

Ndiaye³

et al. 2012

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This paper studies the problem of locating breakdown mechanic. We consider a public transport network in which it can provide buses failure. The objective is, taking into account the statistics of breakdowns registered on the network, to locate optimally breakdown mechanics so as to minimize the response time (to ensure the network coverage of breakdown mechanics). In this work, we present a binary linear programming model for this location problem which provides assignments-locations of areas served. Once the location made, we discuss dynamic assignment of breakdown mechanics depending on their position in the network at a given time t. Numerical simulation results are presented based on real data of urban transportation society of Dakar Dem Dikk.

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“…In the many real-life applications of FLPs such as gas stations and car parking, customer demand on arrival at facilities is heavy; these facilities are called congested [8]. Therefore, queues are formed in these systems; consequently, waiting time will be a key parameter in such FLPs.…”

Section: Introductionmentioning

confidence: 99%

Bi-objective vibration damping optimization for congested location–pricing problem

Hajipour

Farahani

2016

Computers & Operations Research

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This paper presents a bi-objective mathematical programming model for the restricted facility location problem, under a congestion and pricing policy. Motivated by various applications such as locating server on internet mirror sites and communication networks, this research investigates congested systems with immobile servers and stochastic demand as M/M/m/k queues. For this problem, we consider two simultaneous perspectives; (1) customers who desire to limit waiting time for service and (2) service providers who intend to increase profits. We formulate a bi-objective facility location problem with two objective functions: (i) maximizing total profit of the whole system and (ii) minimizing the sum of waiting time in queues; the model type is mixed-integer nonlinear. Then, a multi-objective optimization algorithm based on vibration theory (so-called multi-objective vibration damping optimization (MOVDO)), is developed to solve the model. Moreover, the Taguchi method is also implemented, using a response metric to tune the parameters.The results are analyzed and compared with a non-dominated sorting genetic algorithm (NSGA-II) as a well-developed multi-objective evolutionary optimization algorithm. Computational results demonstrate the efficiency of the proposed MOVDO to solve large-scale problems.

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A review of congestion models in the location of facilities with immobile servers

Cited by 93 publications

References 55 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

Breakdown Mechanic Location Problem

Bi-objective vibration damping optimization for congested location–pricing problem

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