A multi-objective model for facility location–allocation problem with immobile servers within queuing framework

Rahmati, Seyed Habib A.; Ahmadi, Abbas; Sharifi, Mani; Chambari, Amirhossain

doi:10.1016/j.cie.2014.04.018

Cited by 23 publications

(5 citation statements)

References 11 publications

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“…In addition to that, some specific applications are approached in [22] for the location of public services in high risk tsunami areas or in [42] for the selection of the best raster points in a Geographical Information System. Finally, [61] proposed a generic problem in which the first objective function minimizes total setup cost of facilities while the second one minimizes the total expected traveling and waiting time for the customers.…”

Section: Review On Recent Approaches To Location Problemsmentioning

confidence: 99%

Multiobjective Combinatorial Optimization with Interactive Evolutionary Algorithms: the case of facility location problems

Barbati¹,

Corrente²,

Greco³

2022

Preprint

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We consider multiobjective combinatorial optimization problems handled by means of preference driven efficient heuristics. They look for the most preferred part of the Pareto front on the basis of some preferences expressed by the Decision Maker during the process. In general, what is searched for in this case is the Pareto set of efficient solutions. This is a problem much more difficult than optimizing a single objective function. Moreover, obtaining the Pareto set does not mean that the decision problem is solved since one or some of the solutions have to be chosen. Indeed, to make a decision, it is necessary to determine the most preferred solution in the Pareto set, so that it is also necessary to elicit the preferences of the user. In this perspective, what we are proposing can be seen as the first structured methodology in facility location problems to search optimal solutions taking into account preferences of the user. With this aim, we approach facility location problems using a recently proposed interactive evolutionary multiobjective optimization procedure called NEMO-II-Ch. NEMO-II-Ch is applied to a real world multiobjective location problem with many users and many facilities to be located. Several simulations considering different fictitious users have been performed. The results obtained by NEMO-II-Ch are compared with those got by three algorithms which know the user's true value function that is, instead, unknown to NEMO-II-Ch. They show that in many cases NEMO-II-Ch finds the best subset of locations more quickly than the methods knowing, exactly, the whole user's true preferences.

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Section: Review On Recent Approaches To Location Problemsmentioning

confidence: 99%

Multiobjective Combinatorial Optimization with Interactive Evolutionary Algorithms: the case of facility location problems

Barbati¹,

Corrente²,

Greco³

2022

Preprint

View full text Add to dashboard Cite

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“…They used a capacity constraint for limiting the waiting time at each hub node to a predefined value and considered two objectives: (i) the facilities number that is treated by limiting the capacity, and (ii) the percentage of demand that may be lost because of the model's limitation. Rahmati et al (2014) developed a bi-objective model for the facility location-allocation problem, which is immobile service and stochastic demands with the M/M/1/K queue system. The objectives of the model were to minimize the total cost of server providers and minimize the total time of serving customers.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving a hub location-routing problem with a queue system under social responsibility by a fuzzy meta-heuristic algorithm

et al. 2021

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This paper presents a new multi-objective mathematical model for the hub location and routing problem under uncertainty in flows, costs, times and number of job opportunities. This model aims at minimizing the total transportation cost consisting of routing and fixed cost, and maximizing the employment and regional development as social responsibility. An M/M/c/K queue system is applied to estimate the waiting time at hub nodes and maximize the responsiveness. In addition, a fuzzy queuing method is applied to model the uncertainties in this network. A powerful evolutionary meta-heuristic algorithm based on the fuzzy invasive weed optimization (FIWO), variable neighborhood search (VNS) and game theory is developed to solve the introduced model and obtain near-optimal Pareto solutions. Many experiments as well as a real transportation case-study show the superiority of the proposed approaches compared to the state-of-the-art algorithm.

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“…The objective was to choose the location of at most p service centers and to allocate demand points to those centers with respect to maximum coverage. Rahmati et al (2014) developed a practical bi-objective model for the facility location–allocation problem with immobile servers and stochastic demand within the M/M/1/K queuing system. The objectives of the developed model were minimization of the total cost of the server provider and minimization of the total time of customers.…”

Section: Literature Reviewmentioning

confidence: 99%

A maximal covering facility location model for emergency services within an M _(t)/M/m/m queuing system

Bahrami

Ahari

Asadpour

2020

JM2

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Purpose In emergency services, maximizing population coverage with the lowest cost at the peak of the demand is important. In addition, due to the nature of services in emergency centers, including hospitals, the number of servers and beds is actually considered as the capacity of the system. Hence, the purpose of this paper is to propose a multi-objective maximal covering facility location model for emergency service centers within an M (t)/M/m/m queuing system considering different levels of service and periodic demand rate. Design/methodology/approach The process of serving patients is modeled according to queuing theory and mathematical programming. To cope with multi-objectiveness of the proposed model, an augmented ε-constraint method has been used within GAMS software. Since the computational time ascends exponentially as the problem size increases, the GAMS software is not able to solve large-scale problems. Thus, a NSGA-II algorithm has been proposed to solve this category of problems and results have been compared with GAMS through random generated sample problems. In addition, the applicability of the proposed model in real situations has been examined within a case study in Iran. Findings Results obtained from the random generated sample problems illustrated while both the GAMS software and NSGA-II almost share the same quality of solution, the CPU execution time of the proposed NSGA-II algorithm is lower than GAMS significantly. Furthermore, the results of solving the model for case study approve that the model is able to determine the location of the required facilities and allocate demand areas to them appropriately. Originality/value In the most of previous works on emergency services, maximal coverage with the minimum cost were the main objectives. Hereby, it seems that minimizing the number of waiting patients for receiving services have been neglected. To the best of the authors’ knowledge, it is the first time that a maximal covering problem is formulated within an M (t)/M/m/m queuing system. This novel formulation will lead to more satisfaction for injured people by minimizing the average number of injured people who are waiting in the queue for receiving services.

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A multi-objective model for facility location–allocation problem with immobile servers within queuing framework

Cited by 23 publications

References 11 publications

Multiobjective Combinatorial Optimization with Interactive Evolutionary Algorithms: the case of facility location problems

Multiobjective Combinatorial Optimization with Interactive Evolutionary Algorithms: the case of facility location problems

Solving a hub location-routing problem with a queue system under social responsibility by a fuzzy meta-heuristic algorithm

A maximal covering facility location model for emergency services within an M _(t)/M/m/m queuing system

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A multi-objective model for facility location–allocation problem with immobile servers within queuing framework

Cited by 23 publications

References 11 publications

Multiobjective Combinatorial Optimization with Interactive Evolutionary Algorithms: the case of facility location problems

Multiobjective Combinatorial Optimization with Interactive Evolutionary Algorithms: the case of facility location problems

Solving a hub location-routing problem with a queue system under social responsibility by a fuzzy meta-heuristic algorithm

A maximal covering facility location model for emergency services within an M (t)/M/m/m queuing system

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A maximal covering facility location model for emergency services within an M _(t)/M/m/m queuing system