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© C.Hicks, University of Newcastle IGLS04/1 Stochastic simulation of dispatching rules in the capital goods industry Dr Christian Hicks University of Newcastle.

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Presentation on theme: "© C.Hicks, University of Newcastle IGLS04/1 Stochastic simulation of dispatching rules in the capital goods industry Dr Christian Hicks University of Newcastle."— Presentation transcript:

1 © C.Hicks, University of Newcastle IGLS04/1 Stochastic simulation of dispatching rules in the capital goods industry Dr Christian Hicks University of Newcastle upon Tyne

2 © C.Hicks, University of Newcastle IGLS04/2 Dispatching rule literature 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.

3 © C.Hicks, University of Newcastle IGLS04/3 Capital goods companies 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.

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5 © C.Hicks, University of Newcastle IGLS04/5 Case Study 52 Machine tools Three product families competing for resource (main product, spares and subcontract) Complex product structures

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7 © C.Hicks, University of Newcastle IGLS04/7 FactorsLevels Minimum setup time0, 30 (mins) Minimum machining time0, 60 (mins) Minimum transfer time0, 2 days Data update period0, 8 hours Capacity constraintsInfinite, finite* Experimental design Process times normally distributed with standard deviation = 0.1 * mean

8 © C.Hicks, University of Newcastle IGLS04/8 Dispatching rules 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)

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10 © C.Hicks, University of Newcastle IGLS04/10 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 Performance Metrics

11 © C.Hicks, University of Newcastle IGLS04/11 Infinite capacity experiments

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16 © C.Hicks, University of Newcastle IGLS04/16 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.

17 © C.Hicks, University of Newcastle IGLS04/17 Finite Capacity Experiments

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23 © C.Hicks, University of Newcastle IGLS04/23 Finite Capacity Experiment Summary 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.

24 © C.Hicks, University of Newcastle IGLS04/24 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.

25 © C.Hicks, University of Newcastle IGLS04/25 Conclusions 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.

26 © C.Hicks, University of Newcastle IGLS04/26 Conclusions 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.


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