Combining dual price smoothing and piecewise linear penalty function stabilization in column generation: experimental results
SADYKOV, Ruslan
Reformulations based algorithms for Combinatorial Optimization [Realopt]
Institut de Mathématiques de Bordeaux [IMB]
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Reformulations based algorithms for Combinatorial Optimization [Realopt]
Institut de Mathématiques de Bordeaux [IMB]
SADYKOV, Ruslan
Reformulations based algorithms for Combinatorial Optimization [Realopt]
Institut de Mathématiques de Bordeaux [IMB]
Reformulations based algorithms for Combinatorial Optimization [Realopt]
Institut de Mathématiques de Bordeaux [IMB]
VANDERBECK, François
Reformulations based algorithms for Combinatorial Optimization [Realopt]
Institut de Mathématiques de Bordeaux [IMB]
< Réduire
Reformulations based algorithms for Combinatorial Optimization [Realopt]
Institut de Mathématiques de Bordeaux [IMB]
Langue
en
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Ce document a été publié dans
3rd International Symposium on Combinatorial Optimization ISCO'2014, 2014-03-05, Lisbon.
Résumé en anglais
Column generation is a well-known method to solve large-scale combinatorial optimisation problem. However, its application in practice if often limited by convergence issues. To overcome this drawback, several stabilization ...Lire la suite >
Column generation is a well-known method to solve large-scale combinatorial optimisation problem. However, its application in practice if often limited by convergence issues. To overcome this drawback, several stabilization techniques have been proposed. Probably, the most common techniques are 1) adding piecewise linear penalty functions to the dual objective and 2) dual price smoothing, which consists in pricing with a linear combination of the current dual solution and the best dual solution obtained earlier. Recently we proposed an automatic (parameter-less) variant of the dual price smoothing technique. In this work, we experimentally compare efficiency of the two mentioned stabilisation techniques on a wide range of problems, including machine scheduling, generalised assignment, lot sizing, capacitated vehicle routing, shift scheduling, and min-cost multi-commodity flow. Then, we also test numerically the combination of these techniques. Our experimental results show that for most problems this combination outperforms the two techniques applied separately.< Réduire
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