Data-Driven Models and Methodologies to Optimize Production Schedules
Prabhakar Sastri,
Andreas Stephanides
Issue:
Volume 4, Issue 1, February 2016
Pages:
1-9
Received:
29 January 2016
Accepted:
8 February 2016
Published:
2 March 2016
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.
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 ...
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