Simulation Modeling and Analysis
Simulation Modeling and Analysis
Simulation with Arena
Improved genetic algorithm for the permutation flowshop scheduling problem
Computers and Operations Research
A computational study of the permutation flow shop problem based on a tight lower bound
Computers and Operations Research
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Deterministic scheduling algorithms are often applied to problems in stochastic settings perhaps because they are already hard to solve even without considering stochastic characteristics. We are interested in assessing the measure of risk in performance measures (e.g., makespan) when these algorithms are used in probabilistic environment. We design an easy-to-use Microsoft Excel® program that integrates a Visual Basic Application (VBA) subroutine which performs scheduling procedures, with an Arena® simulation model that imitates the stochastic production environment. Our program suggests a job schedule, its associated performance measures and the corresponding prediction intervals. At the moment, we only consider the m-machine permutation flowshop problem with the makespan (or completion time) objective.