Soutenance de thèse de Thiago jobson Barbalho, mardi 19 mars 2024 à 14h - UFR ST de l'Université Le Havre Normandie

Date :


Thiago jobson Barbalho soutiendra sa thèse intitulée "Problems, models and methods for risk reduction after industrial disasters involving dangerous substances", mardi 19 mars 2024 à 14h en amphithéâtre NORMAND à l'UFR ST de l'Université Le Havre Normandie.

Le jury sera composé de :

  • M. Nabil ABSI, Professeur des Universités à l'École Nationale des Mines de Saint-Étienne, Rapporteur
  • Mme Safia KEDAD-SIDHOUM, Professeur des Universités au CNAM de Paris, Rapporteur
  • Mme Nathalie BOSTEL, Professeur des Universités à Nantes Université, Examinatrice
  • M. Guilherme DE CASTRO PENA, Professeur Associé à l'Universidade Federal de São João Del-Rei, Examinateur
  • M. Christophe DUHAMEL, Maître de Conférences à l'ULHN, Co-encadrant de thèse
  • M. Juan Luis JIMENEZ-LAREDO, Professeur Associé à l'Universidad de Granada, Co-encadrant de thèse
  • Mme Andréa Cynthia SANTOS, Professeur des Universités à l'ULHN, Directrice de thèse
Résumé en anglais:

In Europe, more than 250 major accidents involving industrial sites under the Seveso Directive have been reported since 2010. Despite regulations in place to prevent such accidents and minimize their impact, managing risk after these disasters remains a complex challenge. Once an industrial disaster occurs, preliminary on-the-ground information is collected to determine the extent of the accident, and operational decisions need to be made based on the hazardous nature of the products involved and the extent of the affected area. In this thesis, we delve into the realm of complex scheduling problems closely linked to risk factors arising from the treatment (cleaning or neutralizing) of hazardous substances accidentally released by industrial sources. The primary objective is to develop effective optimization models and solutions addressing the challenges faced by industries and logistical operations. We propose new optimization problems to establish a framework for scheduling on-site operations to either clean or neutralize potential hazards. We present mathematical formulations and an Iterated Local Search metaheuristic. The methods were applied to solve various problem scenarios, and we investigate their numerical results and their applicability to realistic situations.