Solving a two-agent single-machine learning scheduling problem

Scheduling with learning effects has been widely studied. However, there has been little work done on multi-agent scheduling with learning effects. This article investigates a two-agent single-machine scheduling problem with learning effects via an objective function which minimises the weighted com...

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Published inInternational journal of computer integrated manufacturing Vol. 27; no. 1; pp. 20 - 35
Main Author Wu, Wen-Hung
Format Journal Article
LanguageEnglish
Published Taylor & Francis 02.01.2014
Subjects
scheduling
simulated-annealing
single-machine
sum-of-processing-times-based learning
two-agent
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Abstract Scheduling with learning effects has been widely studied. However, there has been little work done on multi-agent scheduling with learning effects. This article investigates a two-agent single-machine scheduling problem with learning effects via an objective function which minimises the weighted completion time of all the jobs subject to a constraint that one agent makespan cannot exceed a prescribed upper bound. This article develops a branch-and-bound algorithm along with three simulated-annealing (SA) algorithms searching for an optimal and near-optimal solution. The computational results show that all the average error percentages of combined SA algorithms are less than 0.076%.
AbstractList Scheduling with learning effects has been widely studied. However, there has been little work done on multi-agent scheduling with learning effects. This article investigates a two-agent single-machine scheduling problem with learning effects via an objective function which minimises the weighted completion time of all the jobs subject to a constraint that one agent makespan cannot exceed a prescribed upper bound. This article develops a branch-and-bound algorithm along with three simulated-annealing (SA) algorithms searching for an optimal and near-optimal solution. The computational results show that all the average error percentages of combined SA algorithms are less than 0.076%.
Author Wu, Wen-Hung
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SubjectTerms scheduling
simulated-annealing
single-machine
sum-of-processing-times-based learning
two-agent
Title Solving a two-agent single-machine learning scheduling problem
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