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Stochastic simulation of dispatching rules in the capital goods industry

Stochastic simulation of dispatching rules in the capital goods industry. Dr Christian Hicks University of Newcastle upon Tyne. http://www.staff.ncl.ac.uk/chris.hicks/presindex.htm. Majority of work has focused upon small problems.

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Stochastic simulation of dispatching rules in the capital goods industry

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  1. Stochastic simulation of dispatching rules in the capital goods industry Dr Christian Hicks University of Newcastle upon Tyne http://www.staff.ncl.ac.uk/chris.hicks/presindex.htm

  2. Majority of work has focused upon small problems. Work has focused upon the production of components, mostly in job shops. Minimum set-up, machining and transfer times have been neglected. Deterministic process times have been assumed. Dispatching rule literature

  3. Design, manufacture and construction of large products such as turbine generators, cranes and boilers. Complex product structures with many levels of assembly. Highly customised and produced in low volume on an engineer-to-order basis. Capital goods companies

  4. Case Study • 52 Machine tools • Three product families competing for resource (main product, spares and subcontract) • Complex product structures

  5. Experimental design FactorsLevels Minimum setup time 0, 30 (mins) Minimum machining time 0, 60 (mins) Minimum transfer time 0, 2 days Data update period 0, 8 hours Capacity constraints Infinite, finite* Process times normally distributed with standard deviation = 0.1 * mean

  6. Earliest due first (EDF) First event first (FEF) Longest operation first (LOF) Least remaining operations first (LRF) Least remaining slack first (LSF) Most remaining operations first (MRF) Shortest operation first (SOF) Random (RAN) Dispatching rules

  7. Performance Metrics Throughput Efficiency () = Minimum flow time x 100 (%) Actual flow time Tardiness (T) = completion time – due time (for completion time > due time) Tardiness (T) = 0 (for completion time  due time) Due date performance = completion time – due time

  8. Infinite capacity experiments

  9. Infinite Capacity Experiment Results • Infinite capacity experiments indicated that more factors and interactions were statistically significant at component level than at product level. • Minimum transfer time had the greatest impact upon mean throughput efficiency and mean tardiness. • Throughput efficiency was much higher at component level than product level suggesting that the Company’s plans were not well synchronised.

  10. Finite Capacity Experiments

  11. At product level: Mean throughput efficiency maximised by SOF (main and subcontract) and MRF (spares). Mean tardiness minimised by SOF (subcontract), LSF (main product), MRF (spares). Dispatching rule most important factor for both measures. Finite Capacity Experiment Summary

  12. Finite Capacity Experiment Results At component level: • Best rules for mean throughput efficiency and tardiness were LOF (subcontract), EDF (main) and SOF (spares) i.e. different to products • Minimum transfer time most important factor for minimising throughput time. • Dispatching rule most important factor for minimising tardiness.

  13. Most dispatching rule research has focused upon job shops and has neglected other operational factors such as minimum setup, machining and transfer times and the data update period. Dispatching rule research has investigated deterministic situations. This research has included complex assemblies, stochastic processing times and a multi-product environment. Conclusions

  14. Performance at product level much worse than at component level – probably due to poorly synchronised plan. “Best” dispatching rule varies according to measure, level and product family. Results for “best” rule under stochastic conditions different with deterministic processing times. SOF generally best in agreement with Blackstone. Statistical significance of other factors varies by level, product and measure, but dispatching rules important in all cases. Conclusions

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