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Stochastic simulation studies of dispatching rules for production scheduling in the capital goods industry. Chris Hicks, Business School Fouzi Hossen, Mechanical & Systems Engineering. Introduction. Dispatching rules are used to choose which part to process next when there is a queue.
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Stochastic simulation studies of dispatching rules for production scheduling in the capital goods industry Chris Hicks, Business School Fouzi Hossen, Mechanical & Systems Engineering
Introduction • Dispatching rules are used to choose which part to process next when there is a queue. • Typical rules are: first come first served, earliest due date first, shortest operation first etc. • Most research has focused upon simple situations such as job shops • Previous research has ignored uncertainties and other operational factors.
Objectives • To investigate the significance of operational parameters (minimum set-up, machining and transfer times and the data update period) in companies that produce complex products in low volume, with Beta distributed processing times and infinite capacity. • Find the configuration which gives the best performance under infinite capacity conditions. • To investigate the relative significance of these factors and dispatching rules with finite capacity. • To find the best/worst dispatching rules at component and product levels. • To find the importance of dispatching rules relative to the operational parameters.
Capital Goods Companies • Produce complex products with many levels of assembly in low volume. • Produce different product families e.g. main product, spares and subcontract products. These families are subject to different time scales, competitive criteria etc. • There is a lot of contention for resources. • There are many sources of uncertainty: process times, machine breakdown, delivery of materials, engineering changes etc.
Case Study • Based upon an 18 months schedule obtained from a collaborating company. • 56 products from 3 product families. • 3,360 components with 5,539 operations processed on 36 resources. • Main products had up to 8 levels of assembly. Other product families involved the manufacture of components or single level assemblies. • Measure performance in terms of mean tardiness.
Regression equations Only statistically significant factors included. Predictive models identify ‘optimum’ performance and impact of the factors
Results • Company significantly behind schedule at the start of the simulated period. • Not possible to meet due dates, even with infinite capacity. • All coefficients were positive indicating that the best results would be obtained with the low levels of the factors. • The Beta distribution was statistically significant in all cases, but the impact was small. • Beta distribution used was most important for the main products, which had many levels of assembly.
Results • The Beta distribution did not change the relative significance of the factors • The best rule was different at component and product level and varied by product family • Minimum transfer time was the most significant factor, followed by the data update period.
Conclusions Infinite capacity experiments • Minimum transfer time most important factor, followed by data update period. • The Beta distribution used was statistically significant, but the difference in mean tardiness was small. Finite capacity experiments • Relative performance of dispatching rules not affected by the Beta distribution used. • The ‘best’ rule varied by product family and was different for components and products.
