Exact and heuristic algorithms for parallel-machine scheduling with DeJong’s learning effect

We consider a parallel-machine scheduling problem with a learning effect and the makespan objective. The impact of the learning effect on job processing times is modelled by the general DeJong’s learning curve. For this NP -hard problem we propose two exact algorithms: a sequential branch-and-bound...

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Vydané v:	Computers & industrial engineering Ročník 59; číslo 2; s. 272 - 279
Hlavní autori:	Okołowski, Dariusz, Gawiejnowicz, Stanisław
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.09.2010
Predmet:	Algorithms Branch-and-bound algorithms Clusters Computer simulation Estimates Heuristic scheduling Heuristic scheduling algorithms Industrial engineering Learning Learning curves Learning effect Parallel-machine scheduling Scheduling Learning effect Heuristic scheduling algorithms Parallel-machine scheduling Branch-and-bound algorithms
ISSN:	0360-8352
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Shrnutí:	We consider a parallel-machine scheduling problem with a learning effect and the makespan objective. The impact of the learning effect on job processing times is modelled by the general DeJong’s learning curve. For this NP -hard problem we propose two exact algorithms: a sequential branch-and-bound algorithm and a parallel branch-and-bound algorithm. We also present the results of experimental evaluation of these algorithms on a computational cluster. Finally, we use the exact algorithms to estimate the performance of two greedy heuristic scheduling algorithms for the problem.
Bibliografia:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0360-8352
DOI:	10.1016/j.cie.2010.04.008