Optimizing energy and CO2 efficiency in last-mile delivery using hybrid fleet models
Effective urban delivery systems demand innovative approaches to reduce energy use and lower CO2. This study compares the environmental performance of hybrid and diesel trucks with quadcopter and fixed-wing remotely piloted aircraft systems (RPAS), employing a multi-objective optimization approach n...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Sustainable Futures |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666188825006537 |
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| author | Armin Mahmoodi Leila Hashemi Jeremy Laliberte Seyed Mojtaba Sajadi |
| author_facet | Armin Mahmoodi Leila Hashemi Jeremy Laliberte Seyed Mojtaba Sajadi |
| author_sort | Armin Mahmoodi |
| collection | DOAJ |
| description | Effective urban delivery systems demand innovative approaches to reduce energy use and lower CO2. This study compares the environmental performance of hybrid and diesel trucks with quadcopter and fixed-wing remotely piloted aircraft systems (RPAS), employing a multi-objective optimization approach non-dominated sorting genetic algorithm II (NSGA-II) to identify optimal delivery routes balancing operational efficiency and sustainability. Given that existing solutions like e-bikes or electric vans may not be feasible everywhere, this research evaluates different vehicle types under various urban delivery scenarios. Using a synthetic dataset that simulates realistic conditions, the findings reveal that fixed-wing RPAS excel in long-range efficiency, while quadcopters perform better in short-range deliveries. Hybrid trucks are advantageous for larger loads, reducing emissions compared to diesel trucks. The results highlight key trade-offs in energy use and emissions, advocating for a mixed-fleet strategy tailored to specific logistics needs. This study provides actionable insights for sustainable urban freight planning and policymaking. |
| format | Article |
| id | doaj-art-c4062b0655524c0897da8a5c7cf6aadb |
| institution | Kabale University |
| issn | 2666-1888 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sustainable Futures |
| spelling | doaj-art-c4062b0655524c0897da8a5c7cf6aadb2025-08-20T03:43:55ZengElsevierSustainable Futures2666-18882025-12-011010108910.1016/j.sftr.2025.101089Optimizing energy and CO2 efficiency in last-mile delivery using hybrid fleet modelsArmin Mahmoodi0Leila Hashemi1Jeremy Laliberte2Seyed Mojtaba Sajadi3PhD Candidate and Research associate in aerospace engineering, Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, Canada; Corresponding author.Ph.D. Student in Aerospace Engineering, Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, CanadaProfessor, Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, CanadaAssistant Professor in Operations and Supply Chain Simulation, Operations and Information Management Department, Aston Business School, Aston University, Birmingham B4 7ET UKEffective urban delivery systems demand innovative approaches to reduce energy use and lower CO2. This study compares the environmental performance of hybrid and diesel trucks with quadcopter and fixed-wing remotely piloted aircraft systems (RPAS), employing a multi-objective optimization approach non-dominated sorting genetic algorithm II (NSGA-II) to identify optimal delivery routes balancing operational efficiency and sustainability. Given that existing solutions like e-bikes or electric vans may not be feasible everywhere, this research evaluates different vehicle types under various urban delivery scenarios. Using a synthetic dataset that simulates realistic conditions, the findings reveal that fixed-wing RPAS excel in long-range efficiency, while quadcopters perform better in short-range deliveries. Hybrid trucks are advantageous for larger loads, reducing emissions compared to diesel trucks. The results highlight key trade-offs in energy use and emissions, advocating for a mixed-fleet strategy tailored to specific logistics needs. This study provides actionable insights for sustainable urban freight planning and policymaking.http://www.sciencedirect.com/science/article/pii/S2666188825006537Last-mile logisticsVehicle routing problemMulti-objective optimizationCO2 emissionNSGA-II algorithmEnergy efficiency |
| spellingShingle | Armin Mahmoodi Leila Hashemi Jeremy Laliberte Seyed Mojtaba Sajadi Optimizing energy and CO2 efficiency in last-mile delivery using hybrid fleet models Sustainable Futures Last-mile logistics Vehicle routing problem Multi-objective optimization CO2 emission NSGA-II algorithm Energy efficiency |
| title | Optimizing energy and CO2 efficiency in last-mile delivery using hybrid fleet models |
| title_full | Optimizing energy and CO2 efficiency in last-mile delivery using hybrid fleet models |
| title_fullStr | Optimizing energy and CO2 efficiency in last-mile delivery using hybrid fleet models |
| title_full_unstemmed | Optimizing energy and CO2 efficiency in last-mile delivery using hybrid fleet models |
| title_short | Optimizing energy and CO2 efficiency in last-mile delivery using hybrid fleet models |
| title_sort | optimizing energy and co2 efficiency in last mile delivery using hybrid fleet models |
| topic | Last-mile logistics Vehicle routing problem Multi-objective optimization CO2 emission NSGA-II algorithm Energy efficiency |
| url | http://www.sciencedirect.com/science/article/pii/S2666188825006537 |
| work_keys_str_mv | AT arminmahmoodi optimizingenergyandco2efficiencyinlastmiledeliveryusinghybridfleetmodels AT leilahashemi optimizingenergyandco2efficiencyinlastmiledeliveryusinghybridfleetmodels AT jeremylaliberte optimizingenergyandco2efficiencyinlastmiledeliveryusinghybridfleetmodels AT seyedmojtabasajadi optimizingenergyandco2efficiencyinlastmiledeliveryusinghybridfleetmodels |