Title:
	Demand-based benefit maximization in the context of transportation networks
		Responsable(s) :
			Meritxell Pacheco, Michel Bierlaire
		Description :
			The integration of discrete choice models, the state-of-the-art demand modeling at disaggregate level, in Mixed Integer Linear Programming (MILP) models provides a better understanding of the preferences of the customers to the operators while planning for their systems. We have defined a linear formulation of a general discrete choice model that can be embedded in any MILP formulation by relying on simulation. We have also characterized a demand-based benefit maximization problem to illustrate the use of this approach, and a decomposition scheme based on Lagrangian relaxation to be able to deal with large-scale problems. As an extension to this research, we propose to adapt the developed formulations to the context of profit maximization by setting tolls over a subset of arcs of a transportation network. A small (synthetic) network will be used to test the suggested methodology.
		Collaboration with:
		Type :
			masters project, semester project
		Pré-requis :
			knowledge of discrete choice models and operations research, and general knowledge of coding
		Submitted on :
			June 04, 2018