Abstract
Primal heuristics have become an essential component in mixed integer programming (MIP). Generic heuristic paradigms of the literature remain to be extended to the context of a column generation solution approach. As the Dantzig-Wolfe reformulation is typically tighter than the original compact formulation, techniques based on rounding its linear programming solution have better chance to yield good primal solutions. However, the dynamic generation of variables requires specific adaptation of heuristic paradigms. We focus here on “feasibility pump” approaches. We show how such methods can be implemented in a context of dynamically defined variables, and we report on numerically testing “feasibility pump” for cutting stock and generalized assignment problems.
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Pesneau, P., Sadykov, R., Vanderbeck, F. (2012). Feasibility Pump Heuristics for Column Generation Approaches. In: Klasing, R. (eds) Experimental Algorithms. SEA 2012. Lecture Notes in Computer Science, vol 7276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30850-5_29
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DOI: https://doi.org/10.1007/978-3-642-30850-5_29
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