Heuristics and matheuristics for a real-life machine reassignment problem

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Abstract

This paper addresses a real-life machine reassignment problem proposed in the Google ROADEF/EURO Challenge (2012). In this paper, we propose a linear integer programming (IP) formulation and iterated local search (ILS) heuristics for approximately solving this problem. Different versions of the ILS heuristics are presented. Two of these versions rely on IP-based perturbations, whereas the other two are based on randomized perturbations. We also propose efficient restricted versions of the classic perturbation and local search procedures based on the “shift” and “swap” neighborhoods. Computational experiments showed that the IP-based heuristics are competitive with the best heuristics in the literature.

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