Stabilized dynamic constraint aggregation for solving set partitioning problems

Dynamic constraint aggregation (DCA) and dual variable stabilization (DVS) are two methods that can reduce the negative impact of degeneracy when solving linear programs. The first uses a projection to reduce the primal space whereas the second acts in the dual space. In this paper, we develop a new method, called stabilized dynamic constraint aggregation (SDCA), that combines DCA and DVS for solving set partitioning problems. It allows to fight degeneracy from both primal and dual perspectives simultaneously. To assess the effectiveness of SDCA, we report computational results obtained for highly degenerate multi-depot vehicle scheduling problem instances solved by column generation. These results indicate that SDCA can reduce the average computational time of the master problem by a factor of up to 7 with respect to the best of the two combined methods. Furthermore, they show that its performance is robust with regard to increasing levels of degeneracy in test problems.

► Primal simplex and column generation methods are subject to degeneracy. ► We combine dynamic constraint aggregation and dual variable stabilization. ► This fights degeneracy from both primal and dual perspectives. ► The new method is applicable to set partitioning problems. ► Within column generation, it reduces the master problem cpu time by a factor of 7.