<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents an efficient method for optimizing the problem of unequal facility layouts. In this method, the genetic algorithm is improved and developed into an adaptive genetic algorithm. In this algorithm, the mutation operator is applied only when the similarity of chromosomes in each population reaches a certain level. This intelligence prevents jumps in situations where they are not needed and reduces computational time. In order to measure the performance of the proposed algorithm, its performance is compared with the performance of conventional genetic algorithms and refrigeration simulators. Computational results show that the adaptive genetic algorithm is able to achieve higher-quality solutions.</p>
</abstract>

The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents an efficient method for optimizing the problem of unequal facility layouts. In this method, the genetic algorithm is improved and developed into an adaptive genetic algorithm. In this algorithm, the mutation operator is applied only when the similarity of chromosomes in each population reaches a certain level. This intelligence prevents jumps in situations where they are not needed and reduces computational time. In order to measure the performance of the proposed algorithm, its performance is compared with the performance of conventional genetic algorithms and refrigeration simulators. Computational results show that the adaptive genetic algorithm is able to achieve higher-quality solutions.

Development of an Adaptive Genetic Algorithm to Optimize the Problem Of Unequal Facility Location

Department of Oral Biology, Faculty of Dental Medicine

Plekhanov Russian University of Economics

Medical Laboratories Techniques Department

Kuban State Agrarian University named after I.T. Trubilin

Mathematics Education, Faculty of Science, Engineering and Applied

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the...

Development of an Adaptive Genetic Algorithm to Optimize the Problem Of Unequal Facility Location

Data publikacji: 09 lip 2022

Zakres stron: 111 - 125

Otrzymano: 16 maj 2022

Przyjęty: 30 mar 2022

DOI: https://doi.org/10.2478/fcds-2022-0006

Słowa kluczowe
Unequal Facility location, Interactions, Adaptive Genetic Algorithm, Mutation Operator Intelligence, healthy lifestyle

© 2022 Hendrik Setia Budi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Development of an Adaptive Genetic Algorithm to Optimize the Problem Of Unequal Facility Location

Data publikacji: 09 lip 2022

Zakres stron: 111 - 125

Otrzymano: 16 maj 2022

Przyjęty: 30 mar 2022

DOI: https://doi.org/10.2478/fcds-2022-0006

Słowa kluczoweUnequal Facility location, Interactions, Adaptive Genetic Algorithm, Mutation Operator Intelligence, healthy lifestyle

© 2022 Hendrik Setia Budi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Unequal Facility location, Interactions, Adaptive Genetic Algorithm, Mutation Operator Intelligence, healthy lifestyle