Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems
To manage remotely piloted aircraft system (RPAS) networks effectively, this research presents a multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, and also introduces a...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-09-01
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| Series: | Cleaner Logistics and Supply Chain |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772390924000283 |
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| author | Armin Mahmoodi Leila Hashemi Jeremy Laliberte Richard C. Millar Robert Walter Meyer |
| author_facet | Armin Mahmoodi Leila Hashemi Jeremy Laliberte Richard C. Millar Robert Walter Meyer |
| author_sort | Armin Mahmoodi |
| collection | DOAJ |
| description | To manage remotely piloted aircraft system (RPAS) networks effectively, this research presents a multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, and also introduces a standardized framework to clarify the RPAS CO2 emission model. These integrations significantly decrease battery consumption in Remotely Piloted Aircraft Systems (RPAS) and lower transportation costs, while also optimizing delivery times, reducing operational risks, and minimizing CO2 emissions. The model’s enhancement for optimizing delivery schedules takes into account uncertain traffic conditions, thus improving accuracy in dynamic environments and further contributing to environmental sustainability. Risk assessment employs the Specific Operations Risk Assessment (SORA) standard, adding a third objective function. This combination of the model, further enhance the efficiency and sustainability of RPAS operations, by optimizing delivery schedules, reducing CO2 emissions and battery consumption, and improving accuracy under dynamic conditions. Also, it make RPAS logistics more practical and effective in real-world applications. As result, the NSGA-II algorithm achieves significant reductions across all objectives: 33.3 % in cost, 6.48 % in time, 33.3 % in risk, and 35.7 % in battery usage within 250 generations. The use of the NSGA-II meta-heuristic method for validation enhances the credibility and practicality of the model. The optimization model’s performance over 250 generations shows rapid initial improvements in cost, time, risk, and battery usage, followed by stabilization, indicating efficient convergence and effective evolutionary computation. Also the findings show that with a CO2 emission rate of 3.773 × 104 kg of CO2 per Wh, highlighting the model’s efficiency and effectiveness. |
| format | Article |
| id | doaj-art-7917d709164c49c0bb287f0d78fc43f1 |
| institution | OA Journals |
| issn | 2772-3909 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cleaner Logistics and Supply Chain |
| spelling | doaj-art-7917d709164c49c0bb287f0d78fc43f12025-08-20T02:22:19ZengElsevierCleaner Logistics and Supply Chain2772-39092024-09-011210016610.1016/j.clscn.2024.100166Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systemsArmin Mahmoodi0Leila Hashemi1Jeremy Laliberte2Richard C. Millar3Robert Walter Meyer4Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, Canada; Corresponding author.Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, CanadaDepartment of Mechanical and Aerospace Engineering, Carleton University, Ottawa, CanadaDepartment of Engineering Management and Systems Engineering, The George Washington University, Washington, USAState University of New York, Department of Chemical Engineering, Syracuse, NY, USATo manage remotely piloted aircraft system (RPAS) networks effectively, this research presents a multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, and also introduces a standardized framework to clarify the RPAS CO2 emission model. These integrations significantly decrease battery consumption in Remotely Piloted Aircraft Systems (RPAS) and lower transportation costs, while also optimizing delivery times, reducing operational risks, and minimizing CO2 emissions. The model’s enhancement for optimizing delivery schedules takes into account uncertain traffic conditions, thus improving accuracy in dynamic environments and further contributing to environmental sustainability. Risk assessment employs the Specific Operations Risk Assessment (SORA) standard, adding a third objective function. This combination of the model, further enhance the efficiency and sustainability of RPAS operations, by optimizing delivery schedules, reducing CO2 emissions and battery consumption, and improving accuracy under dynamic conditions. Also, it make RPAS logistics more practical and effective in real-world applications. As result, the NSGA-II algorithm achieves significant reductions across all objectives: 33.3 % in cost, 6.48 % in time, 33.3 % in risk, and 35.7 % in battery usage within 250 generations. The use of the NSGA-II meta-heuristic method for validation enhances the credibility and practicality of the model. The optimization model’s performance over 250 generations shows rapid initial improvements in cost, time, risk, and battery usage, followed by stabilization, indicating efficient convergence and effective evolutionary computation. Also the findings show that with a CO2 emission rate of 3.773 × 104 kg of CO2 per Wh, highlighting the model’s efficiency and effectiveness.http://www.sciencedirect.com/science/article/pii/S2772390924000283RPAS routing optimizationNSGA-II algorithmMulti-objective location-routingRPAS operational efficiencyEmissionsSORA standards |
| spellingShingle | Armin Mahmoodi Leila Hashemi Jeremy Laliberte Richard C. Millar Robert Walter Meyer Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems Cleaner Logistics and Supply Chain RPAS routing optimization NSGA-II algorithm Multi-objective location-routing RPAS operational efficiency Emissions SORA standards |
| title | Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems |
| title_full | Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems |
| title_fullStr | Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems |
| title_full_unstemmed | Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems |
| title_short | Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems |
| title_sort | revolutionizing rpas logistics and reducing co2 emissions with advanced rpas technology for delivery systems |
| topic | RPAS routing optimization NSGA-II algorithm Multi-objective location-routing RPAS operational efficiency Emissions SORA standards |
| url | http://www.sciencedirect.com/science/article/pii/S2772390924000283 |
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