Use whale algorithm and neighborhood search metaheuristics with fuzzy values to solve the location problem
محورهای موضوعی : Operation Research
Mehdi Fazli
1
,
Farzin Modarres Khiabani
2
,
Behrooz Daneshian
3
1 - Department of Mathematics, Islamic Azad University, Tabriz Branch, Tabriz, Iran
2 - Department of Mathematics, Islamic Azad University, Tabriz Branch, Tabriz, Iran
3 - Department of Mathematics, Islamic Azad University, Central Tehran Branch, Tehran, Iran
کلید واژه: Location problem, Meta Heuristic, fuzzy function, Whale algorithm, Neighborhood search algorithm,
چکیده مقاله :
In this paper, a facility location model with fuzzy value parameters based on the meta-heuristic method is investigated and solved. The proposed method and model uses fuzzy values to investigate and solve the problem of location allocation. The hypotheses of the problem in question are considered as fuzzy random variables and the capacity of each facility is assumed to be unlimited. This article covers a modern, nature-inspired method called the whale algorithm and the neighborhood search method. The proposed method and related algorithm are tested with practical optimization problems and modeling problems. To evaluate the efficiency and performance of the proposed method, we apply this method to our location models in which fuzzy coefficients are used. The results of numerical optimization show that the proposed method performs better than conventional methods.
In this paper, a facility location model with fuzzy value parameters based on the meta-heuristic method is investigated and solved. The proposed method and model uses fuzzy values to investigate and solve the problem of location allocation. The hypotheses of the problem in question are considered as fuzzy random variables and the capacity of each facility is assumed to be unlimited. This article covers a modern, nature-inspired method called the whale algorithm and the neighborhood search method. The proposed method and related algorithm are tested with practical optimization problems and modeling problems. To evaluate the efficiency and performance of the proposed method, we apply this method to our location models in which fuzzy coefficients are used. The results of numerical optimization show that the proposed method performs better than conventional methods.
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