JONCOUR, Cédric; MICHEL, Sophie; SADYKOV, Ruslan; SVERDLOV, Dmitry; VANDERBECK, François

doi:10.1016/j.endm.2010.05.088

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JONCOUR, Cédric
Reformulations based algorithms for Combinatorial Optimization [Realopt]
Institut de Mathématiques de Bordeaux [IMB]

MICHEL, Sophie
Reformulations based algorithms for Combinatorial Optimization [Realopt]
Laboratoire de Mathématiques Appliquées du Havre [LMAH]

SADYKOV, Ruslan
Reformulations based algorithms for Combinatorial Optimization [Realopt]
Institut de Mathématiques de Bordeaux [IMB]

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International Symposium on Combinatorial Optimization (ISCO), 2010-03-24, HaMMamet. 2010-03-24, vol. 36, p. 695-702

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In the past decade, significant progress has been achieved in developing generic primal heuristics that made their way into commercial mixed integer programming (MIP) solver. Extensions to the context of a column generation solution approach are not straightforward. The Dantzig-Wolfe decomposition principle can indeed be exploited in greedy, local search, rounding or truncated exact methods. The price coordination mechanism can bring a global view that may be lacking in some ''myopic'' approaches based on a compact formulation. However, the dynamic generation of variables requires specific adaptation of heuristic paradigms. The column generation literature reports many application specific studies where primal heuristics are a key to success. There remains to extract generic methods that could be seen as black-box primal heuristics for use across applications. In this paper we review generic classes of column generation based primal heuristics. We then focus on a so-called ''diving'' method in which we introduce diversification based on Limited Discrepancy Search. While being a general purpose approach, the implementation of our heuristic illustrates the technicalities specific to column generation. The method is numerically tested on variants of the cutting stock and vehicle routing problems.< Réduire

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Column Generation based Primal Heuristics

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