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

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.

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19. Questions?