Integrating operational decisions into the planning of one-way vehicle-sharing systems under uncertainty
This study proposes a systematic approach for planning and operating a one-way vehicle-sharing system (VSS) under demand uncertainty. It investigates the distribution of parking spaces and vehicles considering stochastic demand and interactions with the major operational decisions, namely vehicle redistribution activities. An optimization model is formulated that aims to determine the best deployment strategy for minimizing overall cost while achieving a certain level of service (LoS). Then, a simulation-based solution approach based on a discrete-event simulator (DES), Particle Swarm Optimization (PSO), and Optimal Computing Budget Allocation (OCBA) is devised to solve the mathematical model. The methodology is then applied to a car-sharing system in Singapore. Results demonstrate that considering rebalancing activities is imperative in making deployment strategies. The case study also provides managerial insights regarding designing and operating one-way VSS.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
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Supplemental Notes:
- Abstract reprinted with permission of Elsevier.
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Authors:
- Deng, Yinghan
- Cardin, Michel-Alexandre
- Publication Date: 2018-1
Language
- English
Media Info
- Media Type: Web
- Features: Figures; Maps; References; Tables;
- Pagination: pp 407-424
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Serial:
- Transportation Research Part C: Emerging Technologies
- Volume: 86
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0968-090X
- Serial URL: http://www.sciencedirect.com/science/journal/0968090X
Subject/Index Terms
- TRT Terms: Mathematical models; Optimization; Resource allocation; Simulation; Uncertainty; Vehicle sharing
- Uncontrolled Terms: Particle swarm optimization
- Geographic Terms: Singapore
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01661083
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 23 2018 4:39PM