Data-Driven Models and Methodologies to Optimize Production Schedules
Automation, Control and Intelligent Systems
Volume 4, Issue 1, February 2016, Pages: 1-9
Received: Jan. 29, 2016; Accepted: Feb. 8, 2016; Published: Mar. 2, 2016
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Authors
Prabhakar Sastri, Automation and Data Analytics Department, Isa Technologies Pvt. Ltd., Manipal, India
Andreas Stephanides, Automation and Data Analytics Department, Isa Technologies Pvt. Ltd., Manipal, India
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Abstract
Data driven Models based on production parameters in combination with modern optimization algorithms are shown to be useful in industry to optimize production schedules and improve profitability. Based on real data obtained from an existing facility, we have developed models for time and costs of heat treatment. Using these data statistical models have been developed and used to find an optimal solution to the Job-Shop scheduling problem using three algorithms namely Particle Filter, Particle Swarm Optimization and Genetic Algorithm. The algorithm is useful when we would like to arrive at job schedules based on a mix of both time and cost optimization. The results are compared and future work discussed with respect to the data used.
Keywords
Data-Driven Models, Optimized Production Scheduling, Job-Shop Scheduling, Time Based and/or Cost Based Production Optimization, Management Decision Tool
To cite this article
Prabhakar Sastri, Andreas Stephanides, Data-Driven Models and Methodologies to Optimize Production Schedules, Automation, Control and Intelligent Systems. Vol. 4, No. 1, 2016, pp. 1-9. doi: 10.11648/j.acis.20160401.11
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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