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Title: A biased-randomized discrete-event algorithm for the hybrid flow shop problem with time dependencies and priority constraints
Author: Copado Méndez, Pedro Jesús
Panadero Martínez, Javier  
Laroque, Christoph
Leißau, Madlene
Schumacher, Christin
Juan Pérez, Ángel Alejandro
Others: University of Applied Sciences Zwickau
Universitat Oberta de Catalunya
TU Dortmund University
Universitat Politècnica de València
Keywords: machine scheduling
discrete-event heuristics
based-randomization
batching
priority
hybrid flow shop
Issue Date: 2-Feb-2022
Publisher: Algorithms
Citation: Laroque, C., Leißau, M., Copado Méndez, P., Schumacher, C., Panadero, J. & Juan Perez, A.A. (2022). A Biased-Randomized Discrete-Event Algorithm for the Hybrid Flow Shop Problem with Time Dependencies and Priority Constraints. Algorithms, 15(2), 1-14. doi: 10.3390/a15020054
Published in: 15(2);54
Also see: https://doi.org/10.3390/a15020054
Abstract: Based on a real-world application in the semiconductor industry, this article models and discusses a hybrid flow shop problem with time dependencies and priority constraints. The analyzed problem considers a production where a large number of heterogeneous jobs are processed by a number of machines. The route that each job has to follow depends upon its type, and, in addition, some machines require that a number of jobs are combined in batches before starting their processing. The hybrid flow model is also subject to a global priority rule and a ¿same setup¿ rule. The primary goal of this study was to find a solution set (permutation of jobs) that minimizes the production makespan. While simulation models are frequently employed to model these time-dependent flow shop systems, an optimization component is needed in order to generate high-quality solution sets. In this study, a novel algorithm is proposed to deal with the complexity of the underlying system. Our algorithm combines biased-randomization techniques with a discrete-event heuristic, which allows us to model dependencies caused by batching and different paths of jobs efficiently in a near-natural way. As shown in a series of numerical experiments, the proposed simulation-optimization algorithm can find solutions that significantly outperform those provided by employing state-of-the-art simulation software.
Language: English
URI: http://hdl.handle.net/10609/143406
ISSN: 1999-4893MIAR
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