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Publications associées (26)

A Mixed-Integer Linear Programming Formulation for Optimizing Multi-Scale Material and Energy Integration

François Maréchal, Ivan Daniel Kantor, Hür Ebuzer Bütün, Jean-Loup Sylvain Robineau

This research presents a mathematical formulation for optimizing integration of complex industrial systems from the level of unit operations to processes, entire plants, and finally to considering industrial symbiosis opportunities between plants. The fram ...

2020

A general framework for the integration of complex choice models into mixed integer optimization

Meritxell Pacheco Paneque

The objective of this thesis is to develop a general methodology to incorporate a disaggregate demand representation in supply-oriented optimization problems that allows to capture the interplay between the behavior of individuals and the decisions to be o ...

EPFL2020

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Kilian Schindler

Stochastic optimization is a popular modeling paradigm for decision-making under uncertainty and has a wide spectrum of applications in management science, economics and engineering. However, the stochastic optimization models one faces in practice are int ...

EPFL2020

Waste collection inventory routing with non-stationary stochastic demands

Michel Bierlaire, Mohammad Yousef Maknoon, Sacha Varone, Iliya Dimitrov Markov

We solve a rich routing problem inspired from practice, in which a heterogeneous fixed fleet is used for collecting recyclable waste from large containers over a finite planning horizon. Each container is equipped with a sensor that communicates its level ...

PERGAMON-ELSEVIER SCIENCE LTD2020

The electric autonomous dial-a-ride problem

Claudia Bongiovanni

This thesis develops mathematical programming frameworks to operate electric autonomous vehicles in the context of ride-sharing services. The introduced problem is a novel variant of the Dial-a-Ride Problem (DARP), denoted by the electric Autonomous Dial-a ...

EPFL2020

Learning with tensors: a framework based on convex optimization and spectral regularization

Quoc Tran Dinh

We present a framework based on convex optimization and spectral regularization to perform learning when feature observations are multidimensional arrays (tensors). We give a mathematical characterization of spectral penalties for tensors and analyze a uni ...

Springer2014