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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Bibliographic Details
Published in:Computers & industrial engineering Vol. 59; no. 2; pp. 272 - 279
Main Authors: Okołowski, Dariusz, Gawiejnowicz, Stanisław
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.09.2010
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0360-8352
DOI:10.1016/j.cie.2010.04.008