Showing papers by "André Langevin published in 2021"

TL;DR: This paper proposes an inverse optimization formulation to derive a proper cost matrix by learning from the experts experience and shows the effectiveness of the approach based on randomly generated instances and real-world instances.

TL;DR: This paper presents a robust counterpart formulation with graph transformation to node routing in order to optimize the worst-case realization of the demands from the uncertainty set and developed a variant of the Slack Induction by String Removals metaheuristic for large-scale instances called the Robust SISRs.