Enhancing Campus Mobility: Simulated Multi-Objective Optimization of Electric Vehicle Sharing Systems Within an Intelligent Transportation System Frameworks
This research optimizes an electric vehicle (EV) sharing system for a university campus, focusing on different demand patterns and peak times within an Intelligent Transportation System (ITS) framework. The main objectives are to reduce the number of unserved demands and operational costs. A simulat...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10811944/ |
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| author | Omar S. Aba Hussen Shaiful J. Hashim Nasri Sulaiman Member S.A.R. Alhaddad Bassam Y. Ribbfors Masanobu Umeda Keiichi Katamine |
| author_facet | Omar S. Aba Hussen Shaiful J. Hashim Nasri Sulaiman Member S.A.R. Alhaddad Bassam Y. Ribbfors Masanobu Umeda Keiichi Katamine |
| author_sort | Omar S. Aba Hussen |
| collection | DOAJ |
| description | This research optimizes an electric vehicle (EV) sharing system for a university campus, focusing on different demand patterns and peak times within an Intelligent Transportation System (ITS) framework. The main objectives are to reduce the number of unserved demands and operational costs. A simulation model was developed in MATLAB, utilizing the Non-dominated Sorting Genetic Algorithm (NSGA-II), a powerful multi-objective optimization technique that balances conflicting objectives to achieve the best trade-offs for operational efficiency. In addition to conventional decision variables, dynamic dual relocation thresholds and charge levels are introduced as decision variables to enhance optimization. The study compares two scenarios: Equally Distributed Demand (EDD) and Non-Equally Distributed Demand (NEDD), customized for the University Putra Malaysia (UPM) campus. Findings indicate that the NEDD scenario, which concentrates on specific demand areas, effectively decreases unserved demands and operational costs. Additionally, a station-specific approach expanded the solution space, improving adaptability and resulting in notable reductions in operational costs and smaller but meaningful improvements in unserved demands, especially during peak periods. By setting station-specific relocation thresholds and charge levels, resources were deployed efficiently, minimizing unnecessary relocations. The use of dynamic values for dual relocation thresholds and charge-to-work levels further optimized the process, reducing operational costs significantly, with a lesser impact on unserved demands across both scenarios. This research offers valuable insights into the implementation of EV sharing systems in educational institutions, emphasizing the advantages of focused resource allocation and the integration of dynamic decision variables. |
| format | Article |
| id | doaj-art-b6d201fbf40a459e93dbaf890ca43284 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-b6d201fbf40a459e93dbaf890ca432842025-01-21T00:02:41ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302025-01-01631533110.1109/OJVT.2024.352109110811944Enhancing Campus Mobility: Simulated Multi-Objective Optimization of Electric Vehicle Sharing Systems Within an Intelligent Transportation System FrameworksOmar S. Aba Hussen0https://orcid.org/0000-0002-8235-5002Shaiful J. Hashim1https://orcid.org/0000-0002-4436-897XNasri Sulaiman Member2S.A.R. Alhaddad3https://orcid.org/0000-0001-5522-5096Bassam Y. Ribbfors4Masanobu Umeda5Keiichi Katamine6Faculty of Engineering, Universiti Putra Malaysia, Serdang, MalaysiaFaculty of Engineering, Universiti Putra Malaysia, Serdang, MalaysiaFaculty of Engineering, Universiti Putra Malaysia, Serdang, MalaysiaFaculty of Engineering, Universiti Putra Malaysia, Serdang, MalaysiaWirelesscar AB, Gothenburg, SwedenKyushu Institute of Technology, Iizuka, JapanKyushu Institute of Technology, Iizuka, JapanThis research optimizes an electric vehicle (EV) sharing system for a university campus, focusing on different demand patterns and peak times within an Intelligent Transportation System (ITS) framework. The main objectives are to reduce the number of unserved demands and operational costs. A simulation model was developed in MATLAB, utilizing the Non-dominated Sorting Genetic Algorithm (NSGA-II), a powerful multi-objective optimization technique that balances conflicting objectives to achieve the best trade-offs for operational efficiency. In addition to conventional decision variables, dynamic dual relocation thresholds and charge levels are introduced as decision variables to enhance optimization. The study compares two scenarios: Equally Distributed Demand (EDD) and Non-Equally Distributed Demand (NEDD), customized for the University Putra Malaysia (UPM) campus. Findings indicate that the NEDD scenario, which concentrates on specific demand areas, effectively decreases unserved demands and operational costs. Additionally, a station-specific approach expanded the solution space, improving adaptability and resulting in notable reductions in operational costs and smaller but meaningful improvements in unserved demands, especially during peak periods. By setting station-specific relocation thresholds and charge levels, resources were deployed efficiently, minimizing unnecessary relocations. The use of dynamic values for dual relocation thresholds and charge-to-work levels further optimized the process, reducing operational costs significantly, with a lesser impact on unserved demands across both scenarios. This research offers valuable insights into the implementation of EV sharing systems in educational institutions, emphasizing the advantages of focused resource allocation and the integration of dynamic decision variables.https://ieeexplore.ieee.org/document/10811944/NSGA-IImulti-objective optimizationEV sharing systemsmart campuscar sharing systemvehicle relocation |
| spellingShingle | Omar S. Aba Hussen Shaiful J. Hashim Nasri Sulaiman Member S.A.R. Alhaddad Bassam Y. Ribbfors Masanobu Umeda Keiichi Katamine Enhancing Campus Mobility: Simulated Multi-Objective Optimization of Electric Vehicle Sharing Systems Within an Intelligent Transportation System Frameworks IEEE Open Journal of Vehicular Technology NSGA-II multi-objective optimization EV sharing system smart campus car sharing system vehicle relocation |
| title | Enhancing Campus Mobility: Simulated Multi-Objective Optimization of Electric Vehicle Sharing Systems Within an Intelligent Transportation System Frameworks |
| title_full | Enhancing Campus Mobility: Simulated Multi-Objective Optimization of Electric Vehicle Sharing Systems Within an Intelligent Transportation System Frameworks |
| title_fullStr | Enhancing Campus Mobility: Simulated Multi-Objective Optimization of Electric Vehicle Sharing Systems Within an Intelligent Transportation System Frameworks |
| title_full_unstemmed | Enhancing Campus Mobility: Simulated Multi-Objective Optimization of Electric Vehicle Sharing Systems Within an Intelligent Transportation System Frameworks |
| title_short | Enhancing Campus Mobility: Simulated Multi-Objective Optimization of Electric Vehicle Sharing Systems Within an Intelligent Transportation System Frameworks |
| title_sort | enhancing campus mobility simulated multi objective optimization of electric vehicle sharing systems within an intelligent transportation system frameworks |
| topic | NSGA-II multi-objective optimization EV sharing system smart campus car sharing system vehicle relocation |
| url | https://ieeexplore.ieee.org/document/10811944/ |
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