Extreme Learning Machine-based Genetic Algorithm for the facility location problem with distributed demands on network edges
This study scrutinizes a facility location problem with uniformly distributed demands along the network edges. The objective is to determine the best locations for establishing facilities such that the aggregate traveling time is minimized. Each network edge is divided into two segments, each assign...
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| Vydáno v: | 2023 IEEE Congress on Evolutionary Computation (CEC) s. 1 - 8 |
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IEEE
01.07.2023
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| Abstract | This study scrutinizes a facility location problem with uniformly distributed demands along the network edges. The objective is to determine the best locations for establishing facilities such that the aggregate traveling time is minimized. Each network edge is divided into two segments, each assigned to its closest open facility. Finding the best combination for establishing facilities and using them as a basis for decomposing network edges form the main decision variables. Due to the NP-hardness of this problem, a Genetic Algorithm is used as the optimization method. This algorithm is known as one of the best metaheuristics for solving this problem. To accelerate the optimization process considering the computationally expensive fitness evaluation of the edge-based location problems, an extreme learning machine is hybridized with the implemented genetic algorithm to serve as a surrogate model for approximating the fitness of the majority of individuals. The results obtained from solving generated instances indicate that while keeping the same quality of solutions, the developed surrogate model-based genetic algorithm significantly reduces the required computational time. |
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| AbstractList | This study scrutinizes a facility location problem with uniformly distributed demands along the network edges. The objective is to determine the best locations for establishing facilities such that the aggregate traveling time is minimized. Each network edge is divided into two segments, each assigned to its closest open facility. Finding the best combination for establishing facilities and using them as a basis for decomposing network edges form the main decision variables. Due to the NP-hardness of this problem, a Genetic Algorithm is used as the optimization method. This algorithm is known as one of the best metaheuristics for solving this problem. To accelerate the optimization process considering the computationally expensive fitness evaluation of the edge-based location problems, an extreme learning machine is hybridized with the implemented genetic algorithm to serve as a surrogate model for approximating the fitness of the majority of individuals. The results obtained from solving generated instances indicate that while keeping the same quality of solutions, the developed surrogate model-based genetic algorithm significantly reduces the required computational time. |
| Author | Idoumghar, Lhassane Sulaman, Muhammad Essaid, Mokhtar Golabi, Mahmoud |
| Author_xml | – sequence: 1 givenname: Mahmoud surname: Golabi fullname: Golabi, Mahmoud email: mahmoud.golabi@uha.fr organization: University of Haute-Alsace,Mulhouse,France,F-68100 – sequence: 2 givenname: Mokhtar surname: Essaid fullname: Essaid, Mokhtar email: mokhtar.essaid@uha.fr organization: University of Haute-Alsace,Mulhouse,France,F-68100 – sequence: 3 givenname: Muhammad surname: Sulaman fullname: Sulaman, Muhammad email: muhammad.sulaman@uha.fr organization: University of Haute-Alsace,Mulhouse,France,F-68100 – sequence: 4 givenname: Lhassane surname: Idoumghar fullname: Idoumghar, Lhassane email: lhassane.idoumghar@uha.fr organization: University of Haute-Alsace,Mulhouse,France,F-68100 |
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| Snippet | This study scrutinizes a facility location problem with uniformly distributed demands along the network edges. The objective is to determine the best locations... |
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| SubjectTerms | Aggregates Approximation algorithms Computational modeling continuous demands Evolutionary computation extreme learning machine Extreme learning machines Facility location problem genetic algorithm Metaheuristics Optimization methods surrogate model |
| Title | Extreme Learning Machine-based Genetic Algorithm for the facility location problem with distributed demands on network edges |
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