Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks

Asma Lahimer (1) , Pierre Lopez (1) , Mohamed Haouari (2)

1 LAAS-MOGISA
2 INSAT

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing systems. We propose a new approach based on limited discrepancy search to solve the problem. Our method is tested with reference to a proposed lower bound as well as the best-known solutions in literature. Computational results show that the developed approach is efficient in particular for large-size problems.

Domaines: Recherche opérationnelle [math.OC]; Intelligence artificielle [cs.AI]

Asma Lahimer, Pierre Lopez, Mohamed Haouari. Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks. International Conference CPAIOR 2011, May 2011, Berlin, Germany. p. 117-130. ⟨hal-00574226⟩