Decision-Making System Based on a Fuzzy Hierarchical Analysis Process and an Artificial Neural Network for Flow Shop Machine Scheduling Model Under Uncertainty

The management of the uncertainty existing in any production system is fundamental to define machine scheduling models that allow programming production instances attached to the real world. In this research, a generalized decision-making system is developed for the management of uncertainty existin...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.104059-104069
Main Authors: Villanueva-Jimenez, Luis Fernando, Vazquez-Lopez, Jose Antonio, Yanez-Mendiola, Javier, Calzada-Ledesma, Valentin, De Anda-Suarez, Juan
Format: Article
Language:English
Subjects:
Analytical models
Artificial neural network
Artificial neural networks
Decision analysis
Decision making
decision-making system
flow shop
fuzzy hierarchical analysis process
Industrial applications
Job shop scheduling
Job shops
machine scheduling
Mathematical model
Neural networks
Production scheduling
Programming
Scheduling
Uncertainty
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Summary:The management of the uncertainty existing in any production system is fundamental to define machine scheduling models that allow programming production instances attached to the real world. In this research, a generalized decision-making system is developed for the management of uncertainty existing in flow shop machine scheduling models. The system assessment the uncertainty existing in internal and external factors that influence the decision-making process of production programming experts, and that is decisive in a final machine scheduling. The system is based on the combination of the Fuzzy Hierarchical Analysis Process, a membership analysis, and an Artificial Neural Network (ANN). The system allows to concentrate the experience of experts in machine scheduling and generalize their knowledge. The efficiency of the system is verified with a Fuzzy Hierarchical Analysis Process Model, the "ANN toolbox" preloaded in MATLAB and variety of structures of an Artificial Neural Network. The results are validated in an industrial application and the system is contrasted against an expert. The results show the efficiency of the system as it defines and predicts the final machine scheduling of production instances; the joint assessment of variables that add uncertainty to the production system allowed to reduce delays in product deliveries.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3099342