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International audienc

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From 2010, the health transportation in France has become one of the top ten priorities of the risk management plan due to the increasing cost of these transports. For social and Medico-Social Institutions (MSI), this cost represents the second-biggest expense after that of the wages. In this context, the NOMAd project aims to improve the daily transportation service for people with disabilities between their home to MSI. To this end, we performed a field survey to identify the needs of the different stakeholders. This survey allows us to propose the transportation pooling among several MSIs on one side, and a global transport management process on the other side. This process makes possible to group and optimize routes on a given geographical area. The challenge is then to improve economic performance while maintaining social and environmental goals. A decision aiding tool for the transport optimization is proposed to tackle this problem.

We consider a class of Markov Decision Processes frequently employed to model queueing and inventory control problems. For these problems, we explore how changes in different system input parameters (transition rates, costs, discount rates etc.) affect the optimal cost and the optimal policy when the state space of the problem is multi-dimensional. To address a large class of problems, we introduce two generic dynamic programming operators to model different types of controlled events. For these operators, we derive sufficient conditions to propagate monotonicity and supermodularity properties of the value function. These properties allow to predict how changes in system input parameters affect the optimal cost and policy. Finally, we explore the case when several parameters are changed at the same time.

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In the context of door-to-door transportation of people with disabilities, service quality considerations such as maximum ride time and service time-consistency are critical requirements. To identify a good trade-off between these considerations and economic objectives, we define a new variant of the multi-period dial-a-ride problem called the time-consistent dial-a-ride problem. A transportation planning is supposed to be time-consistent if for each passenger, the same service time is used all along the planning horizon. However, considering the numerous variations in transportation demands over a week, designing consistent plan for all users can be too expensive. It is therefore necessary to find a compromise solution between costs and time-consistency objectives. The time-consistent dial-a-ride problem is solved using an epsilon-constraint approach to illustrate the trade-off between these two objectives. It computes an approximation of the Pareto front, using a matheuristic framework that combines a large neighbourhood search with the solution of set partitioning problems. This approach is benchmarked on time-consistent vehicle routing problem literature instances. Experiments are also conducted in the context of door-to-door transportation for people with disabilities, using real data. These experiments support managerial insights regarding the inter-relatedness of costs and quality of service.