Learning to repeatedly solve routing problems

• URL: http://arxiv.org/abs/2212.08101v1
• Date: Thu, 15 Dec 2022 19:33:54 GMT
• Title: Learning to repeatedly solve routing problems
• Authors: Mouad Morabit, Guy Desaulniers, Andrea Lodi
• Abstract summary: We present a learned for the reoptimization of a problem after a minor change in its data. Given the edges of an original solution, the goal is to predict and fix the ones that have a high chance of remaining in an optimal solution. This partial prediction of the solution reduces the complexity of the problem and speeds up its resolution.
• Score: 5.08128537391027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the last years, there has been a great interest in machine-learning-based heuristics for solving NP-hard combinatorial optimization problems. The developed methods have shown potential on many optimization problems. In this paper, we present a learned heuristic for the reoptimization of a problem after a minor change in its data. We focus on the case of the capacited vehicle routing problem with static clients (i.e., same client locations) and changed demands. Given the edges of an original solution, the goal is to predict and fix the ones that have a high chance of remaining in an optimal solution after a change of client demands. This partial prediction of the solution reduces the complexity of the problem and speeds up its resolution, while yielding a good quality solution. The proposed approach resulted in solutions with an optimality gap ranging from 0\% to 1.7\% on different benchmark instances within a reasonable computing time.

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