Reinforcement learning-enabled genetic algorithm for school bus scheduling
In this paper, the authors focus on a bi-objective school bus scheduling optimization problem, which is a subset of vehicle fleet scheduling problems to transport students distributed across a designated area to the relevant schools. The problem being proven as NP-hard in the literature, they propose an algorithm that seamlessly integrates a reinforcement learning approach with a genetic algorithm. Their proposed algorithm utilizes the processed data supplied by their intelligent transportation system framework to decide the genetic algorithm parameters on-the-fly with the aid of reinforcement learning. With the active guidance of reinforcement learning, the efficiency of the genetic algorithm is improved, and the near-optimal schedule can be achieved in a shorter duration. To evaluate the model,theye conducted experiments on a geospatial dataset comprising road networks, trip trajectories of buses, and the address of students. Results indicate that the genetic algorithm improves the travel distance and time compared to the existing schedule. Reinforcement learning-enabled genetic algorithm improves the performance and the objective function significantly, furthermore with a fewer number of generations compared to various state-of-the-art evolutionary algorithms. The saving by reinforcement learning-enabled genetic algorithm compared to the schedule by initial state generation process is 8.63% and 16.92% for the travel distance for buses and students, respectively, and 14.95% and 26.58% for the travel time for buses and students, respectively.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/15472450
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Supplemental Notes:
- © 2020 Taylor & Francis Group, LLC 2020. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Köksal Ahmed, Eda
- Li, Zengxiang
- Veeravalli, Bharadwaj
- Ren, Shen
- Publication Date: 2022-5
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 269-283
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Serial:
- Journal of Intelligent Transportation Systems
- Volume: 26
- Issue Number: 3
- Publisher: Taylor & Francis
- ISSN: 1547-2450
- EISSN: 1547-2442
- Serial URL: http://www.tandfonline.com/loi/gits20
Subject/Index Terms
- TRT Terms: Bus transit; Combinatorial analysis; Genetic algorithms; Geospatial data; Intelligent transportation systems; Machine learning; Optimization; Scheduling; School buses
- Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation;
Filing Info
- Accession Number: 01850306
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 27 2022 5:19PM