Title: Vehicle routing in flexible and dynamic transportation systems
Location: Ecole des Mines Saint-Etienne (Saint-Etienne, France)
Funding: The French National Research Agency – ANR project FITS – Flexible Intelligent Transportation Systems (https://anr.fr/Project-ANR-18-CE22-0014)
Basic salary : 2 400€/months
Duration: 1 year (could be extended by 1 year)
Programming (C/C++, JAVA and/or Python are preferred)
Doctor in operations research, computer science and/or in industrial engineering.
Thierry Garaix (email@example.com, +33 4 77 42 66 41), Mines Saint-Etienne – LIMOS UMR CNRS 6158, Saint-Etienne, France
Collaborators in the project:
Philippe Lacomme, Université de Clermont Auvergne – LIMOS, Clermont-Ferrand France
Nabil Absi, Mines Saint-Etienne – LIMOS, Gardanne, France
Dominique Feillet, Mines Saint-Etienne – LIMOS, Gardanne, France
Marina Vinot, INSA Lyon – DISP, Lyon, France
Project description: https://anr.fr/Project-ANR-18-CE22-0014
Home services are a growing sector. On the one hand, the range of services offered widens dramatically (delivery of meals, cleaning, care, help with daily or administrative tasks, etc.). On the other hand the mode of reservation of these services evolves and diversifies with the explosion of offers through platforms like Uber. Thus, the reactivity of these systems to unforeseen events is essential to guarantee the quality of services rendered and their economic viability.
Several types of hazards are considered: the availability of the drivers, the availability of the customers, the delays in the progress of the initial planning. The unexpected absence of a driver will be the first case study. In this case, it will be necessary to reassign its activities either to another driver or to distribute them among several drivers.
Some additional difficulties come from different sources of hazards, and have to be taken into account: (1) the variation on the transportation times, (2) modification of patient care schedules and (3) the arrival of new requests.
Exact and / or approximate methods may be addressed in this postdoc. The exact methods considered are based on stochastic programming and robust programming. The approximate methods will generalize existing heuristics by redefining the operators of the literature.
Numerical experiments will be conducted on academic data sets on dynamic dial-a-ride problems and real data from ambulance and home health care companies.
How to apply:
Send by email to firstname.lastname@example.org, your academic CV, publications, PhD thesis manuscript, motivation letter and at least two reference letters.