Multi-Objective Optimization for Green BTS Site Selection in Telecommunication Networks Using NSGA-II and MOPSO
Today, facility location planning primarily pertains to the long-term strategic and operational decision-making of large public and private organizations, and the significant costs associated with facility location, construction, and operation have turned location research into long-term decision-ma...
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MDPI AG
2025-01-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/1/9 |
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| author | Salar Babaei Mehran Khalaj Mehdi Keramatpour Ramin Enayati |
| author_facet | Salar Babaei Mehran Khalaj Mehdi Keramatpour Ramin Enayati |
| author_sort | Salar Babaei |
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| description | Today, facility location planning primarily pertains to the long-term strategic and operational decision-making of large public and private organizations, and the significant costs associated with facility location, construction, and operation have turned location research into long-term decision-making. Presenting a hub location model for the green supply chain can address the current status of facilities and significantly improve demand coverage at an acceptable cost. Therefore, in this study, a network of facilities for hub location in the service site domain, considering existing and potential facilities under probable scenarios, has been proposed. After presenting the mathematical model, validation was performed on a small scale, followed by sensitivity analysis of the main parameters of the model. Furthermore, a metaheuristic algorithm was employed to analyze the NP-Hardness of the model. Additionally, two metaheuristic algorithms, NSGAII and MOPSO, were developed to demonstrate the efficiency of the model. Based on the conducted analysis, it can be observed that the computational time increases exponentially with the size of sample problems, indicating the NP-Hardness of the problem. However, the NSGAII algorithm performs better in terms of computational time for medium-sized problems compared to the MOPSO algorithm. These algorithms were chosen due to their proven efficiency in handling NP-hard optimization problems and their ability to balance exploration and exploitation in search spaces. |
| format | Article |
| id | doaj-art-f463973d5e464540929d1ef2e6f93d4b |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-f463973d5e464540929d1ef2e6f93d4b2025-01-24T13:17:27ZengMDPI AGAlgorithms1999-48932025-01-01181910.3390/a18010009Multi-Objective Optimization for Green BTS Site Selection in Telecommunication Networks Using NSGA-II and MOPSOSalar Babaei0Mehran Khalaj1Mehdi Keramatpour2Ramin Enayati3Department of Industrial Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran, P.O. Box 189Department of Industrial Engineering, Parand and Robat Karim Branch, Islamic Azad University, Parand, Iran, P.O. Box 37613-96361Department of Industrial Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran, P.O. Box 189Department of Mathematics, Roudehen Branch, Islamic Azad University, Roudehen, Iran, P.O. Box 189Today, facility location planning primarily pertains to the long-term strategic and operational decision-making of large public and private organizations, and the significant costs associated with facility location, construction, and operation have turned location research into long-term decision-making. Presenting a hub location model for the green supply chain can address the current status of facilities and significantly improve demand coverage at an acceptable cost. Therefore, in this study, a network of facilities for hub location in the service site domain, considering existing and potential facilities under probable scenarios, has been proposed. After presenting the mathematical model, validation was performed on a small scale, followed by sensitivity analysis of the main parameters of the model. Furthermore, a metaheuristic algorithm was employed to analyze the NP-Hardness of the model. Additionally, two metaheuristic algorithms, NSGAII and MOPSO, were developed to demonstrate the efficiency of the model. Based on the conducted analysis, it can be observed that the computational time increases exponentially with the size of sample problems, indicating the NP-Hardness of the problem. However, the NSGAII algorithm performs better in terms of computational time for medium-sized problems compared to the MOPSO algorithm. These algorithms were chosen due to their proven efficiency in handling NP-hard optimization problems and their ability to balance exploration and exploitation in search spaces.https://www.mdpi.com/1999-4893/18/1/9facility locationmaximum coveragetelecommunicationhub locationNSGAIIMOPSO |
| spellingShingle | Salar Babaei Mehran Khalaj Mehdi Keramatpour Ramin Enayati Multi-Objective Optimization for Green BTS Site Selection in Telecommunication Networks Using NSGA-II and MOPSO Algorithms facility location maximum coverage telecommunication hub location NSGAII MOPSO |
| title | Multi-Objective Optimization for Green BTS Site Selection in Telecommunication Networks Using NSGA-II and MOPSO |
| title_full | Multi-Objective Optimization for Green BTS Site Selection in Telecommunication Networks Using NSGA-II and MOPSO |
| title_fullStr | Multi-Objective Optimization for Green BTS Site Selection in Telecommunication Networks Using NSGA-II and MOPSO |
| title_full_unstemmed | Multi-Objective Optimization for Green BTS Site Selection in Telecommunication Networks Using NSGA-II and MOPSO |
| title_short | Multi-Objective Optimization for Green BTS Site Selection in Telecommunication Networks Using NSGA-II and MOPSO |
| title_sort | multi objective optimization for green bts site selection in telecommunication networks using nsga ii and mopso |
| topic | facility location maximum coverage telecommunication hub location NSGAII MOPSO |
| url | https://www.mdpi.com/1999-4893/18/1/9 |
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