Abstract
Decompositions are methods derived from the “divide et impera” principle, dictating to break up a difficult problem into smaller ones, and to solve each of the smaller ones separately, ultimately recomposing the individual solutions to get the overall one. Decompositions have longly been applied to solve optimization problems, and they come in many different flavors, ranging from constraint programming to logical decomposition, from dynamic programming to linear decompositions, among many others. In this text, we are interested in heuristic approaches. We present three related mathematical decomposition techniques that have been used as seeds for classes of heuristic algorithms, plainly to be included among matheuristics, namely Lagrangian, Dantzig–Wolfe, and Benders decompositions. A detailed presentation and a run trace will be presented for a Lagrangian heuristic.
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Maniezzo, V., Boschetti, M.A., Stützle, T. (2021). Decomposition-Based Heuristics. In: Matheuristics. EURO Advanced Tutorials on Operational Research. Springer, Cham. https://doi.org/10.1007/978-3-030-70277-9_7
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DOI: https://doi.org/10.1007/978-3-030-70277-9_7
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