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Stochastic simulation of dispatching rules in the capital goods industry

Dr Christian HicksUniversity of Newcastle upon Tyne

http://www.staff.ncl.ac.uk/chris.hicks/presindex.htm

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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.

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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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Manufacturing Planing &Control System

Manufacturing Facility

Manufacturing System Simulation Model

Planned Schedule

Resourceinformation

CAPM modules used

System parameters

Product information

Operational factors

System dynamics Logic

Measures ofperformance

Flow measurementCluster AnalysisLayout generation methods

Tools

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Case Study

• 52 Machine tools• Three product families competing for

resource (main product, spares and subcontract)

• Complex product structures

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Factors LevelsMinimum setup time 0, 30 (mins)Minimum machining time 0, 60 (mins)Minimum transfer time 0, 2 daysData update period 0, 8 hoursCapacity constraints Infinite, finite*

Experimental design

Process times normally distributed with standard deviation = 0.1 * mean

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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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  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

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Infinite capacity experiments

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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.

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Finite Capacity Experiments

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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.

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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.

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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.

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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.