Data-Driven Models and Methodologies to Optimize Production Schedules

Prabhakar Sastri; Andreas Stephanides

doi:doi:10.11648/j.acis.20160401.11

| Peer-Reviewed

Data-Driven Models and Methodologies to Optimize Production Schedules

Prabhakar Sastri, Andreas Stephanides

Published in Automation, Control and Intelligent Systems (Volume 4, Issue 1)

Received: 29 January 2016 Accepted: 8 February 2016 Published: 2 March 2016

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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.

Published in	Automation, Control and Intelligent Systems (Volume 4, Issue 1)
DOI	10.11648/j.acis.20160401.11
Page(s)	1-9
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Data-Driven Models, Optimized Production Scheduling, Job-Shop Scheduling, Time Based and/or Cost Based Production Optimization, Management Decision Tool

References

[1]	Graham, Ronald L. Bounds for certain multiprocessing anomalies. Bell System Technical Journal 1966; 45 (9): 1563-1581.
[2]	Dimopoulos, A. M. S., C.; Zalzala. Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons. IEEE Transactions on Evolutionary Computation 2000; 4.
[3]	Mathirajan, M, V Chandru, and AI Sivakumar. Heuristic algorithms for scheduling heat-treatment furnaces of steel casting industries. Sadhana 2007; 32 (5): 479-500.
[4]	Jamili, Amin, Mohammad Ali Shaa, and Reza Tavakkoli-Moghaddam. A hybrid algorithm based on particle swarm optimization and simulated annealing for a periodic job shop scheduling problem. The International Journal of Advanced Manufacturing Technology 2011; 54 (1-4): 309-322.
[5]	Gomez Urrutia, Edwin David, Riad Aggoune, and Stephane Dauzere-Peres. Solving the integrated lot-sizing and job-shop scheduling problem. International Journal of Production Research (ahead-of-print): 2014; 1-19.
[6]	Luarn, Ching-Jong Liao; Chao-Tang Tseng; Pin. A discrete version of particle swarm optimization for flowshop scheduling problems. Computers & Operations Research 2007; 34.
[7]	Wang, Yong Ming, Hong Li Yin, and Jiang Wang. “Genetic algorithm with new encoding scheme for job shop scheduling.” The International Journal of Advanced Manufacturing Technology 2009; 44 (9-10) 977-984.
[8]	Pfund, Lars Monch, Hari Balasubramanian; John W. Fowler; Michele E. Heuristic scheduling of jobs on parallel batch machines with incompatible job families and unequal ready times. Computers & Operations Research 2005; 32.
[9]	Bean, James C. Genetic algorithms and random keys for sequencing and optimization. ORSA journal on computing 1994; 6 (2): 154-160.
[10]	Uzsoy, Cheng-Shuo Wang; Reha. A genetic algorithm to minimize maximum lateness on a batch processing machine. Computers & Operations Research 2002; 29.
[11]	Taillard, E. Benchmarks for basic scheduling problems. European Journal of Operational Research 1993; 64.
[12]	Shaw, KJ, AL Nortcli e, M Thompson, J Love, PJ Fleming, and CM Fonseca. Assessing the performance of multiobjective genetic algorithms for optimization of a batch process scheduling problem. Proceedings of the 1999 Congress on Evolutionary Computation, 1999; CEC 99, Vol. 1.

Cite This Article

Plain Text BibTeX RIS

APA Style

Prabhakar Sastri, Andreas Stephanides. (2016). Data-Driven Models and Methodologies to Optimize Production Schedules. Automation, Control and Intelligent Systems, 4(1), 1-9. https://doi.org/10.11648/j.acis.20160401.11

Copy | Download

ACS Style

Prabhakar Sastri; Andreas Stephanides. Data-Driven Models and Methodologies to Optimize Production Schedules. Autom. Control Intell. Syst. 2016, 4(1), 1-9. doi: 10.11648/j.acis.20160401.11

Copy | Download

AMA Style

Prabhakar Sastri, Andreas Stephanides. Data-Driven Models and Methodologies to Optimize Production Schedules. Autom Control Intell Syst. 2016;4(1):1-9. doi: 10.11648/j.acis.20160401.11

Copy | Download

@article{10.11648/j.acis.20160401.11,
  author = {Prabhakar Sastri and Andreas Stephanides},
  title = {Data-Driven Models and Methodologies to Optimize Production Schedules},
  journal = {Automation, Control and Intelligent Systems},
  volume = {4},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.acis.20160401.11},
  url = {https://doi.org/10.11648/j.acis.20160401.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20160401.11},
  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.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Data-Driven Models and Methodologies to Optimize Production Schedules
AU  - Prabhakar Sastri
AU  - Andreas Stephanides
Y1  - 2016/03/02
PY  - 2016
N1  - https://doi.org/10.11648/j.acis.20160401.11
DO  - 10.11648/j.acis.20160401.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20160401.11
AB  - 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.
VL  - 4
IS  - 1
ER  -

Copy | Download

Author Information

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

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Prabhakar Sastri, Andreas Stephanides. (2016). Data-Driven Models and Methodologies to Optimize Production Schedules. Automation, Control and Intelligent Systems, 4(1), 1-9. https://doi.org/10.11648/j.acis.20160401.11

Copy | Download

ACS Style

Prabhakar Sastri; Andreas Stephanides. Data-Driven Models and Methodologies to Optimize Production Schedules. Autom. Control Intell. Syst. 2016, 4(1), 1-9. doi: 10.11648/j.acis.20160401.11

Copy | Download

AMA Style

Prabhakar Sastri, Andreas Stephanides. Data-Driven Models and Methodologies to Optimize Production Schedules. Autom Control Intell Syst. 2016;4(1):1-9. doi: 10.11648/j.acis.20160401.11

Copy | Download

@article{10.11648/j.acis.20160401.11,
  author = {Prabhakar Sastri and Andreas Stephanides},
  title = {Data-Driven Models and Methodologies to Optimize Production Schedules},
  journal = {Automation, Control and Intelligent Systems},
  volume = {4},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.acis.20160401.11},
  url = {https://doi.org/10.11648/j.acis.20160401.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20160401.11},
  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.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Data-Driven Models and Methodologies to Optimize Production Schedules
AU  - Prabhakar Sastri
AU  - Andreas Stephanides
Y1  - 2016/03/02
PY  - 2016
N1  - https://doi.org/10.11648/j.acis.20160401.11
DO  - 10.11648/j.acis.20160401.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20160401.11
AB  - 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.
VL  - 4
IS  - 1
ER  -

Copy | Download