| Issue |
RAIRO-Oper. Res.
Volume 53, Number 3, July-September 2019
ROADEF 2017
|
|
|---|---|---|
| Page(s) | 1007 - 1031 | |
| DOI | https://doi.org/10.1051/ro/2018042 | |
| Published online | 24 July 2019 | |
Research Article
Fleet management for autonomous vehicles: Online PDP under special constraints⋆
1
Université Grenoble Alpes, CNRS, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), G-SCOP, F-38000, Grenoble, France
2
Université Clermont Auvergne (LIMOS UMR CNRS 6158), Clermont-Ferrand, France
* Corresponding author: bsaybes@isima.fr
Received:
30
April
2017
Accepted:
30
May
2018
The VIPAFLEET project consists in developing models and algorithms for managing a fleet of Individual Public Autonomous Vehicles (VIPA). Hereby, we consider a fleet of cars distributed at specified stations in an industrial area to supply internal transportation, where the cars can be used in different modes of circulation (tram mode, elevator mode, taxi mode). One goal is to develop and implement suitable algorithms for each mode in order to satisfy all the requests under an economic point of view by minimizing the total tour length. The innovative idea and challenge of the project is to develop and install a dynamic fleet management system that allows the operator to switch between the different modes within the different periods of the day according to the dynamic transportation demands of the users. We model the underlying online transportation system and propose a corresponding fleet management framework, to handle modes, demands and commands. We consider two modes of circulation, tram and elevator mode, propose for each mode appropriate online algorithms and evaluate their performance, both in terms of competitive analysis and practical behavior.
Mathematics Subject Classification: 90C27 / 05C21
Key words: Fleet management / Online transportation problem / autonomous vehicles / competitive analysis
© The authors. Published by EDP Sciences, SMAI 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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