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RETRACTED ARTICLE: Multi-criteria-based approach for job scheduling in industry 4.0 in smart cities using fuzzy logic

A flexible manufacturing system (FMS) is the model used for the system produced in the manufacturing industry, and it consists of the number of interconnected workstation. Inflexible manufacturing system scheduling of jobs has become a serious problem, even for a short breakdown of the machine and f...

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Published in:Soft computing (Berlin, Germany) Germany), 2021-09, Vol.25 (18), p.12059-12074
Main Authors: Kumar, Priyan Malarvizhi, Babu, Gokulnath Chandra, Selvaraj, Anandamurugan, Raza, Mohsin, Luhach, Ashish Kr, Díaz, Vicente García
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description A flexible manufacturing system (FMS) is the model used for the system produced in the manufacturing industry, and it consists of the number of interconnected workstation. Inflexible manufacturing system scheduling of jobs has become a serious problem, even for a short breakdown of the machine and for the unexpected arrival of the product. To overcome this problem, a flexible manufacturing system using fuzzy rules is proposed. In this proposed model, four input variables are considered: (1) machine allocated processing time; (2) priority of the machine; (3) priority of the due date; and (4) priority of the setup time. The priority based on the job is the fuzzy variable, which shows the status of the job, based on which the next job will be selected for the processing in the machine. In this model, the machine will be selected first, and then, the scheduling is done based on the multi-criteria scheduling system. The obtained results are compared with the existing system and from the results. The improved scheduling strategy provides better results for the scheduling problem.
doi_str_mv 10.1007/s00500-021-05765-7
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subjects Artificial Intelligence
Computational Intelligence
Control
Engineering
Focus
Mathematical Logic and Foundations
Mechatronics
Robotics
title RETRACTED ARTICLE: Multi-criteria-based approach for job scheduling in industry 4.0 in smart cities using fuzzy logic
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