ISyE Seminar - Patrick Jaillet

Primary tabs

TITLE: Satisficing Awakens: Models to Mitigate Uncertainty


In this talk we consider satisficing models, which, as an approach to decision-making under uncertainty, aims at achieving solutions that satisfy a problem’s constraints as much as possible. Mathematical optimization problems that are related to this form of decision-making include the P-model of Charnes and Cooper (1963), where satisficing is the objective, as well as chance-constrained and robust optimization problems, where satisficing is articulated in the constraints. We first introduce a most general framework of a satisficing model, termed the S-model, which seeks to maximize a satisficing decision criterion in its objective, and the corresponding satisficing-constrained optimization problem that generalizes robust optimization and chance-constrained optimization problems. We then focus on a specific family of a tractable probabilistic S-model, termed the T-model, which, for several different settings of practical interests, leads to efficient and effective ways to mitigate uncertainty. We conclude by illustrating the power of this approach on a class of stochastic maximum coverage problems. 


Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and Computer Science and a member of the Laboratory for Information and Decision Systems at MIT. He is also one of the two Directors of the MIT Operations Research Center. Before MIT, he held faculty positions at the University of Texas at Austin and at the Ecole Nationale des Ponts et Chaussees, Paris. He received a Diplôme d'Ingénieur from France, and a PhD in Operations Research from MIT. His current research interests include on-line and data-driven optimization under uncertainty. He is a Fellow of INFORMS and a member of SIAM.


  • Workflow Status:Published
  • Created By:Anita Race
  • Created:01/25/2016
  • Modified By:Fletcher Moore
  • Modified:04/13/2017


  • No keywords were submitted.