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Title: Simulation studies of the use of heuristic rules for machine shop sequencing
Author: Baker, T. A.
Awarding Body: University College of Swansea
Current Institution: Swansea University
Date of Award: 1981
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Two machine shop computer models are developed to evaluate a set of heuristic decision rules. The first is of a small job shop where machine set up times can be either sequence-dependent or independent. Simple loading rules and combinations thereof are examined, together with a new heuristic rule which provides a 'look-ahead' capability. Results indicate firstly that in job shops with sequence dependent set-up times, the set-up time based loading rules give the best throughput performance, either when the ratio of set up time to processing time is high, or when there is a heavy work load. Secondly, the combining of loading rules, rather than using them separately, can produce better results for both throughput and delivery date criteria. The second model includes many characteristics of actual industrial machine shops, such as grouping of machines with similar capabilities, more complex job processes in the form of assembly networks, and the inclusion of manufacturing costs, inventory/work-in-progress costs, and penalty costs associated with late delivery. Using this model a set of cost-based decision rules is proposed and its performance compared with established loading rules. Results indicate, firstly, that relative performances of the loading rules used singly and combined are similar to those obtained in the smaller machine shop model. Secondly, the performance of the cost based decision rules generally equals or surpasses that of established loading rules, particularly with regard to the minimisation of inventory/work-in-progress costs. The results of the 'look ahead' heuristic rule tested in both models are disappointing, and did not appear to justify the extra computation required.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available