Study of RLT-enhancements for minimax optimization problems

This paper addresses the development of enhanced representations for the rich class of minimax mixed-integer 0-1 optimization on problems that typically arise in the context of a broad spectrum of applications encompassing mechanical and design engineering, machine and sports scheduling, and facility location, to name a few. In this paper, we study the development of enhanced formulations for the general class of minimax mixed-integer 0-1 optimization problems using the unified optimization framework offered by the Reformulation-Linearization Technique (RLT). We also propose various Lagrangian dual formulations for the RLT-enhanced formulations.