General form of a cooperative gradual maximal covering location problem

Cooperative and gradual covering are two new methods for developing covering location models. In this paper, a cooperative maximal covering location-allocation model is developed (CMCLAP). In addition, both cooperative and gradual covering concepts are applied to the maximal covering location simult...

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Bibliographic Details
Published in:Journal of industrial engineering international 2018-06, Vol.14 (2), p.241-253
Main Authors: Bagherinejad, Jafar, Bashiri, Mahdi, Nikzad, Hamideh
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Cooperative covering
Engineering
Engineering Economics
Facility Management
Gradual covering
Industrial and Production Engineering
Location–allocation
Logistics
Marketing
Mathematical and Computational Engineering
Mathematical models
Maximal covering
Metaheuristics
Organization
Original Research
Parameter sensitivity
Production scheduling
Quality Control
Reliability
Run time (computers)
Safety and Risk
Sensitivity analysis
Simulated annealing
Site selection
Tabu search
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Summary:Cooperative and gradual covering are two new methods for developing covering location models. In this paper, a cooperative maximal covering location-allocation model is developed (CMCLAP). In addition, both cooperative and gradual covering concepts are applied to the maximal covering location simultaneously (CGMCLP). Then, we develop an integrated form of a cooperative gradual maximal covering location problem, which is called a general CGMCLP. By setting the model parameters, the proposed general model can easily be transformed into other existing models, facilitating general comparisons. The proposed models are developed without allocation for physical signals and with allocation for non-physical signals in discrete location space. Comparison of the previously introduced gradual maximal covering location problem (GMCLP) and cooperative maximal covering location problem (CMCLP) models with our proposed CGMCLP model in similar data sets shows that the proposed model can cover more demands and acts more efficiently. Sensitivity analyses are performed to show the effect of related parameters and the model's validity. Simulated annealing (SA) and a tabu search (TS) are proposed as solution algorithms for the developed models for large-sized instances. The results show that the proposed algorithms are efficient solution approaches, considering solution quality and running time.
ISSN:2251-712X
1735-5702
2251-712X
DOI:10.1007/s40092-017-0219-5