https://hal-utt.archives-ouvertes.fr/hal-02291666Assunção, LucasLucasAssunçãoUFMG - Universidade Federal de Minas Gerais [Belo Horizonte]Santos, Andréa CynthiaAndréa CynthiaSantosLOSI - Laboratoire d'Optimisation des Systèmes Industriels - ICD - Institut Charles Delaunay - UTT - Université de Technologie de Troyes - CNRS - Centre National de la Recherche Scientifiquede Noronha, Thiago FerreiraThiago Ferreirade NoronhaUFMG - Universidade Federal de Minas Gerais [Belo Horizonte]Andrade, RafaelRafaelAndradeUFC - Universidade Federal do Ceará = Federal University of CearáOn the Finite Optimal Convergence of Logic-Based Benders’ Decomposition in Solving 0–1 Min-Max Regret Optimization Problems with Interval CostsHAL CCSD2016[INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO]VU VAN, Jean-Baptiste2019-09-19 10:07:042023-03-24 14:53:122019-09-19 10:07:04enBook sections1This paper addresses a class of problems under interval data uncertainty composed of min-max regret versions of classical 0–1 optimization problems with interval costs. We refer to them as interval 0–1 min-max regret problems. The state-of-the-art exact algorithms for this class of problems work by solving a corresponding mixed integer linear programming formulation in a Benders’ decomposition fashion. Each of the possibly exponentially many Benders’ cuts is separated on the fly through the resolution of an instance of the classical 0–1 optimization problem counterpart. Since these separation subproblems may be NP-hard, not all of them can be modeled by means of linear programming, unless P = NP. In these cases, the convergence of the aforementioned algorithms are not guaranteed in a straightforward manner. In fact, to the best of our knowledge, their finite convergence has not been explicitly proved for any interval 0–1 min-max regret problem. In this work, we formally describe these algorithms through the definition of a logic-based Benders’ decomposition framework and prove their convergence to an optimal solution in a finite number of iterations. As this framework is applicable to any interval 0–1 min-max regret problem, its finite optimal convergence also holds in the cases where the separation subproblems are NP-hard.