Towards Viable Modelling for Robust Flow Shop Scheduling in Production Environments Under Uncertainty

Luca Fumagalli (), Elisa Negri (), Laura Cattaneo (), Lorenzo Ragazzini () and Marco Macchi ()
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Luca Fumagalli: Politecnico Di Milano
Elisa Negri: Politecnico Di Milano
Laura Cattaneo: Università Carlo Cattaneo-LIUC
Lorenzo Ragazzini: Politecnico Di Milano
Marco Macchi: Politecnico Di Milano

A chapter in Digital Transformation in Industry, 2023, pp 267-279 from Springer

Abstract: Abstract The current work contributes to stochastic hybrid flow shop scheduling. After a thorough literature analysis, it is firstly evident that works on stochastic flow shop scheduling are still limited in number; moreover, they often rely on simplifying assumptions; eventually, they may lack in a full viability for industrial application of the proposed models or algorithms. Considering these limitations, the present work proposes a scheduling framework based on Discrete Event Simulation and on Genetic Algorithms. The work stems from a previously published work, therefore, contributes by identifying some inconsistencies in the original algorithm in the so called “limit cases”. Overall, the paper proposes an alternative fitness function to avoid the generation of such inconsistencies; besides, it considers a realistic probability distribution to describe the stochastic processing times for robust scheduling of a hybrid flow shop. The end purpose is to move towards a viable application of optimization algorithms in industrial environments.

Keywords: Scheduling; Uncertainty; Production management; Genetic algorithms (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-30351-7_21

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