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Ph.D de

Ph.D
Group : Networking & Stochastic and Combinatorial Optimization

Programmation semi-définie positive : méthodes et algorithmes pour le management d'énergie

Starts on 14/12/2009
Advisor : LISSER, Abdel

Funding : Autre financement à préciser
Affiliation : Université Paris-Sud
Laboratory : EDF puis LRI

Defended on 26/09/2013, committee :
Alain Denise, Professeur, Université Paris Sud (examinateur)
Didier Henrion, Directeur de Recherche, LAAS CNRS - Toulouse (rapporteur)
Abdel Lisser, Professeur, Université Paris Sud (directeur de thèse)
Abdelatif Mansouri, Professeur, Université Cadi Ayyad - Marrakech (examinateur)
Michel Minoux, Professeur, Université Paris 6 (examinateur)
Franz Rendl, Professeur, University of Klagenfurt - Austria (rapporteur)
Riadh Zorgati, Docteur, EDF R&D (rapporteur)

Research activities :

Abstract :
The present thesis aims at exploring the potentialities of a powerful optimization technique, namely Semidefinite Programming, for addressing some difficult problems of energy management.
We pursue two main objectives. The first one consists of using SDP to provide tight relaxations of combinatorial and quadratic problems. A first relaxation, called “standard” can be derived in a generic way but it is generally desirable to reinforce them, by means of tailor-made tools or in a systematic fashion. These two approaches are implemented on different models of the Nuclear Outages Scheduling Problem, a famous combinatorial problem. We conclude this topic by experimenting the Lasserre's hierarchy on this problem, leading to a sequence of semidefinite relaxations whose optimal values tends to the optimal value of the initial problem.
The second objective deals with the use of SDP for the treatment of uncertainty. We investigate an original approach called “distributionnally robust optimization”, that can be seen as a compromise between stochastic and robust optimization and admits approximations under the form of a SDP. We compare the benefits of this method w.r.t classical approaches on a demand/supply equilibrium problem. Finally, we propose a scheme for deriving SDP relaxations of MISOCP and we report promising computational results indicating that the semidefinite relaxation improves significantly the continuous relaxation, while requiring a reasonable computational effort.
SDP therefore proves to be a promising optimization method that offers great opportunities for innovation in energy management.

Ph.D. dissertations & Faculty habilitations
DECODING THE PLATFORM SOCIETY: ORGANIZATIONS, MARKETS AND NETWORKS IN THE DIGITAL ECONOMY
The original manuscript conceptualizes the recent rise of digital platforms along three main dimensions: their nature of coordination devices fueled by data, the ensuing transformations of labor, and the accompanying promises of societal innovation. The overall ambition is to unpack the coordination role of the platform and where it stands in the horizon of the classical firm – market duality. It is also to precisely understand how it uses data to do so, where it drives labor, and how it accommodates socially innovative projects. I extend this analysis to show continuity between today’s society dominated by platforms and the “organizational society”, claiming that platforms are organized structures that distribute resources, produce asymmetries of wealth and power, and push social innovation to the periphery of the system. I discuss the policy implications of these tendencies and propose avenues for follow-up research.

DISTRIBUTED COMPUTING WITH LIMITED RESOURCES


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