© C.Hicks, University of Newcastle IGLS04/1 Stochastic simulation of dispatching rules in the...
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Transcript of © C.Hicks, University of Newcastle IGLS04/1 Stochastic simulation of dispatching rules in the...
© C.Hicks, University of Newcastle
IGLS04/1
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
© 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.
© 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.
© C.Hicks, University of Newcastle
IGLS04/4
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
© 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
© C.Hicks, University of Newcastle
IGLS04/6
© C.Hicks, University of Newcastle
IGLS04/7
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
© 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)
© C.Hicks, University of Newcastle
IGLS04/9
© 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
© C.Hicks, University of Newcastle
IGLS04/11
Infinite capacity experiments
© C.Hicks, University of Newcastle
IGLS04/12
© C.Hicks, University of Newcastle
IGLS04/13
© C.Hicks, University of Newcastle
IGLS04/14
© C.Hicks, University of Newcastle
IGLS04/15
© 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.
© C.Hicks, University of Newcastle
IGLS04/17
Finite Capacity Experiments
© C.Hicks, University of Newcastle
IGLS04/18
© C.Hicks, University of Newcastle
IGLS04/19
© C.Hicks, University of Newcastle
IGLS04/20
© C.Hicks, University of Newcastle
IGLS04/21
© C.Hicks, University of Newcastle
IGLS04/22
© 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.
© 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.
© 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.
© 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.