1 / 19

M. G. Abou-Ali and M. A. Shouman Department of Production Engineering, Faculty of Engineering, Alexandria University, A

Effect of dynamic and static dispatching strategies on dynamically planned and unplanned FMS Journal of Materials Processing Technology Volume 148, Issue 1 , 1 May 2004, Pages 132-138. M. G. Abou-Ali and M. A. Shouman Department of Production Engineering, Faculty of Engineering,

arch
Download Presentation

M. G. Abou-Ali and M. A. Shouman Department of Production Engineering, Faculty of Engineering, Alexandria University, A

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Effect of dynamic and static dispatching strategies on dynamically planned and unplanned FMS Journal of Materials Processing TechnologyVolume 148, Issue 1 , 1 May 2004, Pages 132-138 M. G. Abou-Ali and M. A. Shouman Department of Production Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt Department of Information Systems, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt Presentation by Ryan Gillan 11/8/04

  2. Outline • Introduction • Model Assumptions and Description • Experiment Description • Results • Conclusions • References

  3. Introduction Purpose of the Paper Analyze dynamic and static dispatching strategies as applied to dynamically planned and unplanned Flexible Manufacturing systems.

  4. Definitions • Dynamic dispatching- also called the look-ahead simulation approach, a dispatching rule is determined for each short period just before the implementation occurs. • Static dispatching- also called the rule-based (heuristic) approach, scheduling of changing dispatching rules is first acquired and then this knowledge is into the manufacturing system to make intelligent decisions in real-time.

  5. More Definitions • Planned FMS- part types with their relative demands are dynamically changed at deterministic dates over the whole scheduled period. • Unplanned FMS- part types are dynamically changed at undeterministic dates.

  6. Model Assumptions • 12 different dispatching strategies were considered. -shift from standard rules -extended dispatching -combined scheduling -learning-based methodology -genetic algorithm model

  7. Model Assumptions • Each part type requires one or more operation(s). • There are one or more machine(s) which can process one operation at a time. • The part moving time has no effect on lead-time and parts size transports. • System congestion is to be prevented by limiting the total service time of each machine station to the capacity of that station. • Tool change-over times are included in the processing time and tool magazine capacities are not binding constraints due to the availability of an automatic tool handling system. • Data on all alternative routes and processing times can be provided. • Arrival rates, due dates, transporter speed, resources, setup and tear down times are deterministic. • Each operation can be processed by one machine only at a time.

  8. Model Description

  9. Model Description

  10. Experiment Description • 12 dispatching rules are applied, but only the four most effective ones were finally adopted. -Random -Farthest -By turn -Shortest idle -Low usage -Longest idle -High usage -Fewest parts -Closest -Most parts -Newest parts -Oldest parts

  11. Experiment Description

  12. Experiment Parameters • In order to measure the results of the experiment, the following performance parameters were selected: throughput rate product make span mean flow time mean tardiness number of tardy jobs

  13. Results

  14. Results

  15. Results

  16. Conclusions • Greater overall improvements were achieved utilizing a dynamic dispatching schedule. • Both machines and resources are not best utilized for the best schedule, but are close to the best conditions. • Overall performance shows a greater increase for planned, as opposed to unplanned, systems.

  17. Conclusions • Experimental results and conclusions would be best utilized in large manufacturing plants where FMS, in some form, is already being implemented. • This experiment provided further advancement to FMS studies through implementing and building upon established strategies and principles.

  18. 1.C.-H. Tsai, Y.-M. Feng and R.-K. Li, A hybrid dispatching rules in wafer fabrication factories. Int. J. Comput. Internet Manage.11 1 (2003), pp. 64–72. 2. O. Rose, Accelerating products under due-date oriented dispatching rules in semiconductor manufacturing, in: S. Chick, P.J. Sanchez, D. Ferrin, D.J. Morrice (Eds.), Proceedings of the 2003 Winter Simulation Conference, 2003, pp. 1346–1350. 3. J. Chandra and J. Talavage, Intelligent dispatching for flexible manufacturing. Int. J. Prod. Res.29 11 (1991), pp. 2259–2278. 4. N. Ishii and M. Muraki, An extended dispatching rule approach in an on-line scheduling framework for batch process management. Int. J. Prod. Res.34 2 (1996), pp. 329–348. 5. H. Pierreval and N. Mebarki, Dynamic scheduling selection of dispatching rules for manufacturing system. Int. J. Prod. Res.35 6 (1997), pp. 1575–1591. 6. M.S. Jayamohan and C. Rajendran, New dispatching rules for shop scheduling: a step forward. Int. J. Prod. Res.38 3 (2000), pp. 563–586. 7. R.W. Seifert and S. Morito, Cooperative dispatching-exploiting the flexibility of an FMS by means of incremental optimization. Eur. J. Oper. Res.129 1 (2001), pp. 116–133. 8. M.A. Shouman, G.M. Nawara, A.H. Reyad and A.M. El Sadek, The interactive process between some dispatching mechanisms and interrupted machine centers in FMSs. J. Mater. Process. Technol.107 (2000), pp. 465–477. 9. C.N. Potts and J.D. Whitehead, Workload balancing and loop layout in the design of a flexible manufacturing system. Eur. J. Oper. Res.129 2 (2001), pp. 326–336. 10. M.G. Abou-Ali, M.A. Shouman, Evaluation of dispatching mechanisms with mutli-cell and random flexible manufacturing system, in: K.-D. Bouzakis (Ed.), Proceedings of the International Conference on Manufacturing Engineering (ICMEN), 2002, pp. 305–314. 11. C. Chiu and Y. Yih, A learning-based methodology for dynamic scheduling in distributed manufacturing systems. Int. J. Prod. Res.33 11 (1995), pp. 3217–3232. 12. F. Mahmoodi and G.E. Martin, A new shop-based and predictive scheduling heuristic for cellular manufacturing. Int. J. Prod. Res.35 2 (1997), pp. 313–326. 13. A. Rossi and G. Dini, Dynamic scheduling of FMS using a real-time genetic algorithm. Int. J. Prod. Res.38 1 (2000), pp. 1–20. 14. O. Holthaus and C. Rajendran, A study on the performance of scheduling rules in buffer-constrained dynamic flow shops. Int. J. Prod. Res.40 13 (2002), pp. 3041–3052. 15. S.W. Haider, J. Banks, Simulation software products for analyzing manufacturing systems, Ind. Eng. 31 (1986) 98–103. 16. A.M. Law and S.W. Haider, Selecting simulation software for manufacturing application: practical guideline and software survey. Ind. Eng.31 (1989), pp. 33–46. References

  19. Questions?

More Related