A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems
Electrifying public bus transportation is a critical step in reaching net-zero goals. In this paper, the focus is on the problem of optimal scheduling of an electric bus (EB) fleet to cover a public transport timetable. The problem is modelled using a mixed integer program (MIP) in which the chargin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/20/6610 |
| _version_ | 1797514723173859328 |
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| author | Raka Jovanovic Islam Safak Bayram Sertac Bayhan Stefan Voß |
| author_facet | Raka Jovanovic Islam Safak Bayram Sertac Bayhan Stefan Voß |
| author_sort | Raka Jovanovic |
| collection | DOAJ |
| description | Electrifying public bus transportation is a critical step in reaching net-zero goals. In this paper, the focus is on the problem of optimal scheduling of an electric bus (EB) fleet to cover a public transport timetable. The problem is modelled using a mixed integer program (MIP) in which the charging time of an EB is pertinent to the battery’s state-of-charge level. To be able to solve large problem instances corresponding to real-world applications of the model, a metaheuristic approach is investigated. To be more precise, a greedy randomized adaptive search procedure (GRASP) algorithm is developed and its performance is evaluated against optimal solutions acquired using the MIP. The GRASP algorithm is used for case studies on several public transport systems having various properties and sizes. The analysis focuses on the relation between EB ranges (battery capacity) and required charging rates (in kW) on the size of the fleet needed to cover a public transport timetable. The results of the conducted computational experiments indicate that an increase in infrastructure investment through high speed chargers can significantly decrease the size of the necessary fleets. The results also show that high speed chargers have a more significant impact than an increase in battery sizes of the EBs. |
| first_indexed | 2024-03-10T06:35:43Z |
| format | Article |
| id | doaj.art-76eeca468bfb4472ab9263a1e84dd988 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T06:35:43Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-76eeca468bfb4472ab9263a1e84dd9882023-11-22T18:05:54ZengMDPI AGEnergies1996-10732021-10-011420661010.3390/en14206610A GRASP Approach for Solving Large-Scale Electric Bus Scheduling ProblemsRaka Jovanovic0Islam Safak Bayram1Sertac Bayhan2Stefan Voß3Qatar Environment and Energy Research Institute, Hamad bin Khalifa University, Doha P.O. Box 5825, QatarDepartment of Electronic and Electrical Engineering, University of Strathclyde, 204 George St, Glasgow G1 1XW, UKQatar Environment and Energy Research Institute, Hamad bin Khalifa University, Doha P.O. Box 5825, QatarInstitute of Information Systems, University of Hamburg, 20146 Hamburg, GermanyElectrifying public bus transportation is a critical step in reaching net-zero goals. In this paper, the focus is on the problem of optimal scheduling of an electric bus (EB) fleet to cover a public transport timetable. The problem is modelled using a mixed integer program (MIP) in which the charging time of an EB is pertinent to the battery’s state-of-charge level. To be able to solve large problem instances corresponding to real-world applications of the model, a metaheuristic approach is investigated. To be more precise, a greedy randomized adaptive search procedure (GRASP) algorithm is developed and its performance is evaluated against optimal solutions acquired using the MIP. The GRASP algorithm is used for case studies on several public transport systems having various properties and sizes. The analysis focuses on the relation between EB ranges (battery capacity) and required charging rates (in kW) on the size of the fleet needed to cover a public transport timetable. The results of the conducted computational experiments indicate that an increase in infrastructure investment through high speed chargers can significantly decrease the size of the necessary fleets. The results also show that high speed chargers have a more significant impact than an increase in battery sizes of the EBs.https://www.mdpi.com/1996-1073/14/20/6610GRASPelectric busesnet-zero transportationfleet scheduling |
| spellingShingle | Raka Jovanovic Islam Safak Bayram Sertac Bayhan Stefan Voß A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems Energies GRASP electric buses net-zero transportation fleet scheduling |
| title | A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems |
| title_full | A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems |
| title_fullStr | A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems |
| title_full_unstemmed | A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems |
| title_short | A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems |
| title_sort | grasp approach for solving large scale electric bus scheduling problems |
| topic | GRASP electric buses net-zero transportation fleet scheduling |
| url | https://www.mdpi.com/1996-1073/14/20/6610 |
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