Research Article
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Year 2014, Volume: 27 Issue: 4, 1143 - 1155, 24.11.2014

Abstract

References

  • 1. Mönch L. and Zimmermann J., “Simulationbased Assessment of Machine Criticality Measures for A Shifting Bottleneck Scheduling Approach in Complex Manufacturing Systems” Computers in Industry, 58:644-655 (2007).
  • 2. Pfeiffer A., Kádár B. and Monostori L., “Stability-oriented evaluation of rescheduling strategies, by using simulation” Computers in Industry, 58:630–643 (2007).
  • 3. Geyik F. and Cedimoglu I. H., “A review of the production scheduling approaches based-on artificial intelligence and the integration of process planning and scheduling” Proceedings on Swiss Conference of CAD/CAM’99, Neuchatel University, Switzerland, 167-174 (1999).
  • 4. Pinedo M., “Scheduling Theory, Algorithms and Systems” Prentice-Hall, pp. 378, New Jersey, (1995).
  • 5. Baker K. R., “Introduction to Sequencing & Scheduling”. New York: John Wiley (1974)
  • 6. Al-Turki. U., Andijani A. and Arifulsalam S., “A new dispatching rule for the stochastic single-machine scheduling problem” Simulation, 80-3:165-170 (2004).
  • 7. Baker K. R. and Trietsch D., “Principles of sequencing & scheduling” New York: John Wiley (2009).
  • 8. Olafsson S. and Li X., “Learning effective new single machine dispatching rules from optimal scheduling data” International Journal of Production Economics 128:118–126 (2010).
  • 9. Yang S., Wang D., Chai T. and Kendall G., “An improved constraint satisfaction adaptive neural network for job-shop scheduling” Journal of Scheduling 13:17–38 (2010).
  • 10. Vinod V. and Sridharan R., “Simulation Modeling and Analysis of Due-Date Assignment Methods and Scheduling Decision Rules in a Dynamic Job Shop Production System” International Journal of Production Economics 129:127–146 (2011).
  • 11. Akkaya G. and Gokcen T., “Job shop scheduling design with artificial neural networks” Sigma Engineering and Natural Sciences 4:121-130 (2006).
  • 12. Koruca H. I., Ozdemir G., Aydemir E. and Cayirli M., “The Simulation-Based Performance Measurement in an Evaluation Module for Faborg-Sim Simulation Software” Expert System with Applications 37- 12:8211-8220 (2010).
  • 13. Li H., Li Z., Li L. X. and Hu B., “A production rescheduling expert simulation systems” European Journal of Operational Research 124:283-293 (2000).
  • 14. Allaoui H. and Artiba A., “Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints” Computers and Industrial Engineering 47:431–450 (2004).
  • 15. Gharbi A. and Kenne J. P., “Maintenance scheduling and production control of multiple machine manufacturing systems” Computers and Industrial Engineering 48:693-707 (2005).
  • 16. Yildirim M. B., Cakar T., Doguc U. and Meza J. C., “Machine number, priority rule, and due date determination in flexible manufacturing systems using artificial neural networks” Computers and Industrial Engineering 50:185–194 (2006).
  • 17. Geiger C. D., Uzsoy R. and Aytug H., “Rapid Modelling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach” Journal of Scheduling 9:7–34 (2006)
  • 18. Holthaus O. and Ziegler H., “Improving job shop performance by coordinating dispatching rules” International Journal of Production Research 35-2:539-549 (1997).
  • 19. Holthaus O. and Rajendran C., “New dispatching rules for scheduling in a job shopan experimental study” International Journal of Advanced Manufacturing Technology 13:148-153 (1997).
  • 20. Ozturkoglu, Y., “A Bi-Criteria Single Machine Scheduling with Rate-Modifying-Activity” Gazi University Journal of Science, 26(1), 97-106 (2013).
  • 21. Turker, K. and Sel,Ç., “Scheduling Two Parallel Machines with Sequence Dependent Setups and A Single Server” Gazi University Journal of Science, 24(1),113-123 (2011).
  • 22. Moghaddam R. T. and Mehr M. D., “A Computer Simulation Model for Job Shop Scheduling Problems Minimizing Makespan” Computers and Industrial Engineering 48:811–823 (2005).
  • 23. Xing L. N., Chen Y. W. and Yang K. W., “Multi-objective flexible job shop schedule: Design and evaluation by simulation modelling” Applied Soft Computing 9-1:362- 376 (2009).
  • 24. Weng M. X. and Ren H., “An efficient priority rule for scheduling job shops to minimize mean tardiness” IIE Transactions 38-9:789-795 (2006).
  • 25. Chen (Gary) S. J. and Lin L., “Reducing total tardiness cost in manufacturing cell scheduling by a multi-factor priority rule” International Journal of Production Research 37-13:2939- 2956 (1999).
  • 26. Penn M. and Raviv T., “An Algorithm for The Maximum Revenue Jobshop Problem” European Journal of Operational Research 193-2:437-450 (2009).
  • 27. Thiagarajan S. and Rajendran C., “Scheduling in dynamic assembly job-shops to minimize the sum of weighted earliness, weighted tardiness and weighted flowtime of jobs” Computers and Industrial Engineering 49:463–503 (2005).
  • 28. Natarajan K., Mohanasundaram K. M., Babu B. S., Suresh S., Raj K. A. A. D., Rajendran C., “Performance evaluation of priority dispatching rules in multi-level assembly job shops with jobs having weights for flowtime and tardiness” International Journal of Advanced Manufacturing Technology 31:751-761 (2007).
  • 29. Weigert G. and Henlich T., “Simulation-based scheduling of assembly operations” International Journal of Computer Integrated Manufacturing, 22-4:325-333 (2009).
  • 30. Reeja M. K. and Rajendran C., “Dispatching rules for scheduling in assembly jobshops - Part 1” International Journal of Production Research 38-9:2051-2066 (2000).
  • 31. Dominic P. D. D., Kaliyamoorthy S. and Kumar M. S., “Efficient dispatching rules for dynamic job shop scheduling” International Journal of Advanced Manufacturing Technology 24:70-75 (2004).
  • 32. Yang W. H. and Liao C. J., “Survey of scheduling research involving setup times” International Journal of Systems Science 30- 2:143-155 (1999).
  • 33. Vinod V. and Sridharan R., “Simulation-based meta models for scheduling a dynamic job shop with sequence-dependent setup times” International Journal of Production Research 47-6:1425-1447 (2009).
  • 34. Balas E., Simonetti N. and Vazacopoulos A., “Job Shop Scheduling with Setup Times, Deadlines and Precedence Constraints” Journal of Scheduling, 11:253–262 (2008).
  • 35. Cakar T., Yıldırım M. B. and Barut M., “A Neuro-Genetic Approach to Design and Planning of a Manufacturing Cell” Journal of Intelligent Manufacturing 16:453–462 (2005).
  • 36. Sabuncuoglu I., “A study of scheduling rules of flexible manufacturing systems: A simulation approach” International Journal of Production Research, 36-2:527-546 (1998).
  • 37. VDI, “Richtlinie -3633” Düsseldorf, VDI Verlag (1983).
  • 38. Witte T., “Lexikon der Wirtschaftsinformatik” Hrsg. Mertens P., Berlin, Springer Verlag, 2.Auflage (1990).Law A. M. and Kelton W. D., “Simulation Modeling and Analysis” McGrawHill, 2nd Edition, New York (1991).
  • 39. Schmittbetz M., “Simulation wird zum Triebwerk für Innovation” VDI-Nachrichten, 52:18-24 (1998).
  • 40. Zülch G., Bogus T. and Fischer J., “Integrated Simulation and Workforce Assignment for the Evaluation of Flexible Working Time Models” in: Chen, Z. et al., System Simulation and Scientific Computing, International Academic Publishers, Beijing, 1:353-357 (2002).
  • 41. Schuh G., “Produktionsmanagements I” WZL/FIR, Acchen Universitat (2008).
  • 42. Frantzen M., Ng A. H. C. and Moore P., “A simulation-based Scheduling System for Realtime Optimization and Decision Making Support” Robotics and Computer-Integrated Manufacturing 27-4:696-705 (2011).
  • 43. Kapanoglu M. and Alikalfa M., “Learning IF– THEN Priority Rules for Dynamic Job Shops Using Genetic Algorithms” Robotics and Computer-Integrated Manufacturing 27:47– 55 (2011).
  • 44. Aydemir E., “Optimization of Job Shop Scheduling Problems With Priority Rule Based Genetic Algorithms By Simulation Method” MSc. Dissertation in Industrial Engineering at Suleyman Demirel University, Isparta, Turkey (2009).
  • 45. Panwalkar S. S. and Iskander W., “A survey of scheduling rules” Operations Research 25- 1:45-61 (1977).
  • 46. Brinkmeier B., “Prozeßorientieries Prototyping von Organisationsstrukturen im Produktionsbereich” PhD. Dissertation der Ingenieurwissenschaftes in Karlsruhe Universitat, Karlsruhe, Germany (1998).

A PRIORITY RULE BASED PRODUCTION SCHEDULING MODULE ON FABORG-SIM SIMULATION TOOL

Year 2014, Volume: 27 Issue: 4, 1143 - 1155, 24.11.2014

Abstract

This paper presents the development of a priority, rule-based, a production scheduling module for the Faborg-Sim simulation tool with ten priority rules. Faborg-Sim consists of three modules, i.e., modelling, simulation, and performance evaluation. In this study, a detailed conceptual framework was defined and a case study was modelled and evaluated for a machine parts manufacturing system by using Faborg-Sim. The simulations were run using only six selected priority rules for the information on customers’ orders in order to integrate the scheduling module in Faborg-Sim. Simulation models were run separately for each priority rule of scheduling to obtain the best performance of the production schedule. After repeating the simulations, performance measurement parameters were obtained and evaluated on a relative basis.   

References

  • 1. Mönch L. and Zimmermann J., “Simulationbased Assessment of Machine Criticality Measures for A Shifting Bottleneck Scheduling Approach in Complex Manufacturing Systems” Computers in Industry, 58:644-655 (2007).
  • 2. Pfeiffer A., Kádár B. and Monostori L., “Stability-oriented evaluation of rescheduling strategies, by using simulation” Computers in Industry, 58:630–643 (2007).
  • 3. Geyik F. and Cedimoglu I. H., “A review of the production scheduling approaches based-on artificial intelligence and the integration of process planning and scheduling” Proceedings on Swiss Conference of CAD/CAM’99, Neuchatel University, Switzerland, 167-174 (1999).
  • 4. Pinedo M., “Scheduling Theory, Algorithms and Systems” Prentice-Hall, pp. 378, New Jersey, (1995).
  • 5. Baker K. R., “Introduction to Sequencing & Scheduling”. New York: John Wiley (1974)
  • 6. Al-Turki. U., Andijani A. and Arifulsalam S., “A new dispatching rule for the stochastic single-machine scheduling problem” Simulation, 80-3:165-170 (2004).
  • 7. Baker K. R. and Trietsch D., “Principles of sequencing & scheduling” New York: John Wiley (2009).
  • 8. Olafsson S. and Li X., “Learning effective new single machine dispatching rules from optimal scheduling data” International Journal of Production Economics 128:118–126 (2010).
  • 9. Yang S., Wang D., Chai T. and Kendall G., “An improved constraint satisfaction adaptive neural network for job-shop scheduling” Journal of Scheduling 13:17–38 (2010).
  • 10. Vinod V. and Sridharan R., “Simulation Modeling and Analysis of Due-Date Assignment Methods and Scheduling Decision Rules in a Dynamic Job Shop Production System” International Journal of Production Economics 129:127–146 (2011).
  • 11. Akkaya G. and Gokcen T., “Job shop scheduling design with artificial neural networks” Sigma Engineering and Natural Sciences 4:121-130 (2006).
  • 12. Koruca H. I., Ozdemir G., Aydemir E. and Cayirli M., “The Simulation-Based Performance Measurement in an Evaluation Module for Faborg-Sim Simulation Software” Expert System with Applications 37- 12:8211-8220 (2010).
  • 13. Li H., Li Z., Li L. X. and Hu B., “A production rescheduling expert simulation systems” European Journal of Operational Research 124:283-293 (2000).
  • 14. Allaoui H. and Artiba A., “Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints” Computers and Industrial Engineering 47:431–450 (2004).
  • 15. Gharbi A. and Kenne J. P., “Maintenance scheduling and production control of multiple machine manufacturing systems” Computers and Industrial Engineering 48:693-707 (2005).
  • 16. Yildirim M. B., Cakar T., Doguc U. and Meza J. C., “Machine number, priority rule, and due date determination in flexible manufacturing systems using artificial neural networks” Computers and Industrial Engineering 50:185–194 (2006).
  • 17. Geiger C. D., Uzsoy R. and Aytug H., “Rapid Modelling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach” Journal of Scheduling 9:7–34 (2006)
  • 18. Holthaus O. and Ziegler H., “Improving job shop performance by coordinating dispatching rules” International Journal of Production Research 35-2:539-549 (1997).
  • 19. Holthaus O. and Rajendran C., “New dispatching rules for scheduling in a job shopan experimental study” International Journal of Advanced Manufacturing Technology 13:148-153 (1997).
  • 20. Ozturkoglu, Y., “A Bi-Criteria Single Machine Scheduling with Rate-Modifying-Activity” Gazi University Journal of Science, 26(1), 97-106 (2013).
  • 21. Turker, K. and Sel,Ç., “Scheduling Two Parallel Machines with Sequence Dependent Setups and A Single Server” Gazi University Journal of Science, 24(1),113-123 (2011).
  • 22. Moghaddam R. T. and Mehr M. D., “A Computer Simulation Model for Job Shop Scheduling Problems Minimizing Makespan” Computers and Industrial Engineering 48:811–823 (2005).
  • 23. Xing L. N., Chen Y. W. and Yang K. W., “Multi-objective flexible job shop schedule: Design and evaluation by simulation modelling” Applied Soft Computing 9-1:362- 376 (2009).
  • 24. Weng M. X. and Ren H., “An efficient priority rule for scheduling job shops to minimize mean tardiness” IIE Transactions 38-9:789-795 (2006).
  • 25. Chen (Gary) S. J. and Lin L., “Reducing total tardiness cost in manufacturing cell scheduling by a multi-factor priority rule” International Journal of Production Research 37-13:2939- 2956 (1999).
  • 26. Penn M. and Raviv T., “An Algorithm for The Maximum Revenue Jobshop Problem” European Journal of Operational Research 193-2:437-450 (2009).
  • 27. Thiagarajan S. and Rajendran C., “Scheduling in dynamic assembly job-shops to minimize the sum of weighted earliness, weighted tardiness and weighted flowtime of jobs” Computers and Industrial Engineering 49:463–503 (2005).
  • 28. Natarajan K., Mohanasundaram K. M., Babu B. S., Suresh S., Raj K. A. A. D., Rajendran C., “Performance evaluation of priority dispatching rules in multi-level assembly job shops with jobs having weights for flowtime and tardiness” International Journal of Advanced Manufacturing Technology 31:751-761 (2007).
  • 29. Weigert G. and Henlich T., “Simulation-based scheduling of assembly operations” International Journal of Computer Integrated Manufacturing, 22-4:325-333 (2009).
  • 30. Reeja M. K. and Rajendran C., “Dispatching rules for scheduling in assembly jobshops - Part 1” International Journal of Production Research 38-9:2051-2066 (2000).
  • 31. Dominic P. D. D., Kaliyamoorthy S. and Kumar M. S., “Efficient dispatching rules for dynamic job shop scheduling” International Journal of Advanced Manufacturing Technology 24:70-75 (2004).
  • 32. Yang W. H. and Liao C. J., “Survey of scheduling research involving setup times” International Journal of Systems Science 30- 2:143-155 (1999).
  • 33. Vinod V. and Sridharan R., “Simulation-based meta models for scheduling a dynamic job shop with sequence-dependent setup times” International Journal of Production Research 47-6:1425-1447 (2009).
  • 34. Balas E., Simonetti N. and Vazacopoulos A., “Job Shop Scheduling with Setup Times, Deadlines and Precedence Constraints” Journal of Scheduling, 11:253–262 (2008).
  • 35. Cakar T., Yıldırım M. B. and Barut M., “A Neuro-Genetic Approach to Design and Planning of a Manufacturing Cell” Journal of Intelligent Manufacturing 16:453–462 (2005).
  • 36. Sabuncuoglu I., “A study of scheduling rules of flexible manufacturing systems: A simulation approach” International Journal of Production Research, 36-2:527-546 (1998).
  • 37. VDI, “Richtlinie -3633” Düsseldorf, VDI Verlag (1983).
  • 38. Witte T., “Lexikon der Wirtschaftsinformatik” Hrsg. Mertens P., Berlin, Springer Verlag, 2.Auflage (1990).Law A. M. and Kelton W. D., “Simulation Modeling and Analysis” McGrawHill, 2nd Edition, New York (1991).
  • 39. Schmittbetz M., “Simulation wird zum Triebwerk für Innovation” VDI-Nachrichten, 52:18-24 (1998).
  • 40. Zülch G., Bogus T. and Fischer J., “Integrated Simulation and Workforce Assignment for the Evaluation of Flexible Working Time Models” in: Chen, Z. et al., System Simulation and Scientific Computing, International Academic Publishers, Beijing, 1:353-357 (2002).
  • 41. Schuh G., “Produktionsmanagements I” WZL/FIR, Acchen Universitat (2008).
  • 42. Frantzen M., Ng A. H. C. and Moore P., “A simulation-based Scheduling System for Realtime Optimization and Decision Making Support” Robotics and Computer-Integrated Manufacturing 27-4:696-705 (2011).
  • 43. Kapanoglu M. and Alikalfa M., “Learning IF– THEN Priority Rules for Dynamic Job Shops Using Genetic Algorithms” Robotics and Computer-Integrated Manufacturing 27:47– 55 (2011).
  • 44. Aydemir E., “Optimization of Job Shop Scheduling Problems With Priority Rule Based Genetic Algorithms By Simulation Method” MSc. Dissertation in Industrial Engineering at Suleyman Demirel University, Isparta, Turkey (2009).
  • 45. Panwalkar S. S. and Iskander W., “A survey of scheduling rules” Operations Research 25- 1:45-61 (1977).
  • 46. Brinkmeier B., “Prozeßorientieries Prototyping von Organisationsstrukturen im Produktionsbereich” PhD. Dissertation der Ingenieurwissenschaftes in Karlsruhe Universitat, Karlsruhe, Germany (1998).
There are 46 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Industrial Engineering
Authors

Halil Koruca

Erdal Aydemir

Publication Date November 24, 2014
Published in Issue Year 2014 Volume: 27 Issue: 4

Cite

APA Koruca, H., & Aydemir, E. (2014). A PRIORITY RULE BASED PRODUCTION SCHEDULING MODULE ON FABORG-SIM SIMULATION TOOL. Gazi University Journal of Science, 27(4), 1143-1155.
AMA Koruca H, Aydemir E. A PRIORITY RULE BASED PRODUCTION SCHEDULING MODULE ON FABORG-SIM SIMULATION TOOL. Gazi University Journal of Science. November 2014;27(4):1143-1155.
Chicago Koruca, Halil, and Erdal Aydemir. “A PRIORITY RULE BASED PRODUCTION SCHEDULING MODULE ON FABORG-SIM SIMULATION TOOL”. Gazi University Journal of Science 27, no. 4 (November 2014): 1143-55.
EndNote Koruca H, Aydemir E (November 1, 2014) A PRIORITY RULE BASED PRODUCTION SCHEDULING MODULE ON FABORG-SIM SIMULATION TOOL. Gazi University Journal of Science 27 4 1143–1155.
IEEE H. Koruca and E. Aydemir, “A PRIORITY RULE BASED PRODUCTION SCHEDULING MODULE ON FABORG-SIM SIMULATION TOOL”, Gazi University Journal of Science, vol. 27, no. 4, pp. 1143–1155, 2014.
ISNAD Koruca, Halil - Aydemir, Erdal. “A PRIORITY RULE BASED PRODUCTION SCHEDULING MODULE ON FABORG-SIM SIMULATION TOOL”. Gazi University Journal of Science 27/4 (November 2014), 1143-1155.
JAMA Koruca H, Aydemir E. A PRIORITY RULE BASED PRODUCTION SCHEDULING MODULE ON FABORG-SIM SIMULATION TOOL. Gazi University Journal of Science. 2014;27:1143–1155.
MLA Koruca, Halil and Erdal Aydemir. “A PRIORITY RULE BASED PRODUCTION SCHEDULING MODULE ON FABORG-SIM SIMULATION TOOL”. Gazi University Journal of Science, vol. 27, no. 4, 2014, pp. 1143-55.
Vancouver Koruca H, Aydemir E. A PRIORITY RULE BASED PRODUCTION SCHEDULING MODULE ON FABORG-SIM SIMULATION TOOL. Gazi University Journal of Science. 2014;27(4):1143-55.