GPU parallel B&B for the Blocking job shop scheduling problem.

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dc.contributor.authorDabah, Adel
dc.contributor.authorBendjoudi, Ahcène
dc.contributor.authorAit Zai, Abdelhakim
dc.contributor.authorEl Baz, Didier
dc.date.accessioned2016-02-28T09:46:20Z
dc.date.available2016-02-28T09:46:20Z
dc.date.issued2016-02-22
dc.description.abstractBranch and bound algorithms (B&B) are well known techniques for solving optimally combinatorial optimization problems. Nevertheless, these algorithms remain inefficient when dealing with large instances. This paper deals with the blocking job shop scheduling (BJSS), which is a version of classical job shop scheduling with no intermediate buffer between machines. This problem is an NP-hard problem and its exact resolution using the sequential approach is impractical. We propose in this paper a GPU-based parallelization in which a two level scheme is used. The first level is a node-based parallelization in which the bounding process is faster because it is calculated in parallel using several threads organized in one GPU block. To fully occupy the GPU, we propose a second level of parallelization which is a generalization of the first level. Therefore, for each iteration several search tree nodes are evaluated on the GPU using several thread-blocks. The obtained results, using the well-known Taillard instances, confirm the efficiency of the proposed approach. Also, the results show that our approach is 65 times faster than an optimized sequential B&B version.fr_FR
dc.identifier.isrnCERIST-DTISI/RR-16-00000006--DZfr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/797
dc.publisherCERIST
dc.relation.ispartofRapports de recherche internes
dc.relation.placeAlger
dc.structureIntégration des systèmes d'informationfr_FR
dc.subjectJob shop; blocking with swap; GPGPU; parallel computing; Branch-and- Bound.fr_FR
dc.titleGPU parallel B&B for the Blocking job shop scheduling problem.fr_FR
dc.typeTechnical Report
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