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Programming tasks in business processes like a realistic hybrid flexible flow shop using genetic algorithms

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  • Jaime Antero Arango-Marin
Abstract
An adaptation of the job scheduling to the programming of business process tasks is made in a hybrid flexible flow shop environment. The problem is modelled considering realistic situations: sequence-dependent task change times, malleability of batch sizes, variable transfer batch, objective function of minimising average tardiness, unrelated parallel resources and more than two stages. To solve the problem, the proposed standard and modified genetic algorithms were presented. The results of the experimentation allow us to appreciate that both genetic algorithms achieve average tardiness values between 20% and 60% better than the dispatch rules with best performance of the modified genetic algorithm. The conclusions are that it is possible to schedule business process tasks as an industrial plant, that it is necessary to take account of the real environment requirements and that the best solution is reached when a smart technique adapted to the features of the problem is used.

Suggested Citation

  • Jaime Antero Arango-Marin, 2022. "Programming tasks in business processes like a realistic hybrid flexible flow shop using genetic algorithms," International Journal of Process Management and Benchmarking, Inderscience Enterprises Ltd, vol. 12(2), pages 131-146.
    • Handle: RePEc:ids:ijpmbe:v:12:y:2022:i:2:p:131-146
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