\'Etude de Probl\`emes d'Optimisation Combinatoire \`a Multiples Composantes Interd\'ependantes

TL;DR

This paper reviews NP-hard combinatorial optimization problems with multiple interdependent components, highlighting their complexity and discussing heuristic and evolutionary approaches for solving them in real-world applications.

Contribution

It provides an overview of the challenges posed by interdependent components in NP-hard problems and introduces solution approaches from metaheuristics and evolutionary computation.

Findings

Interdependence increases problem complexity

Metaheuristics are promising for these problems

Real-world applications are impacted by these complexities

Abstract

This extended abstract presents an overview on NP-hard optimization problems with multiple interdependent components. These problems occur in many real-world applications: industrial applications, engineering, and logistics. The fact that these problems are composed of many sub-problems that are NP-hard makes them even more challenging to solve using exact algorithms. This is mainly due to the high complexity of this class of algorithms and the hardness of the problems themselves. The main source of difficulty of these problems is the presence of internal dependencies between sub-problems. This aspect of interdependence of components is presented, and some outlines on solving approaches are briefly introduced from a (meta)heuristics and evolutionary computation perspective.