@article{ElHachemi2015,
abstract = {Problem decomposition requires the ability to recombine partial solutions. This recombination task, which we call integration, is a fundamental feature of many methods, both those based on mathematical formulations such as Dantzig–Wolfe or Benders and those based on heuristics. Integration may be implicit in mathematical decompositions, but in heuristics this critical task is usually managed by ad-hoc operators, e.g., operators that combine decisions and heuristic adjustments to manage incompatibilities. In this paper, we propose a general framework for integration, which is viewed as a problem in itself with well-defined objectives and constraints. Four different mechanisms are proposed, based on well-known concepts from the literature such as constraining or giving incentives to particular characteristics of partial solutions. We perform computational experiments on the multi-depot periodic vehicle routing problem to compare the various integration approaches. The strategy that places incentives on selected solution characteristics rather than imposing constraints seems to yield the best results in the context of a cooperative search for this problem.},
author = {{El Hachemi}, N. and Crainic, T.G. and Lahrichi, N. and Rei, W. and Vidal, T.},
doi = {10.1007/s10732-015-9296-z},
file = {:C$\backslash$:/Users/Thibaut/Documents/Mendeley-Articles/El Hachemi et al/El Hachemi et al. - 2015 - Solution integration in combinatorial optimization with applications to cooperative search and rich vehicle r.pdf:pdf},
journal = {Journal of Heuristics},
number = {5},
pages = {663--685},
title = {{Solution integration in combinatorial optimization with applications to cooperative search and rich vehicle routing}},
url = {https://www.cirrelt.ca/DocumentsTravail/CIRRELT-2014-40.pdf},
volume = {21},
year = {2015}
}

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