Today's Continuous Flow Manufacturing (CFM) systems are the integration of several manufacturing components found in traditional manufacturing facilities. They contain groups of machines (facilities, work centers or cells) linked together by transport systems (material handlers, conveyors, etc.). Series of parts are transported from one facility to the next depending on operation sequences. Usually, a major problem encountered in the optimal design of a CFM system is the assignment of these manufacturing components onto appropriate locations in the layout to obtain efficient CFM configurations with preferable flow of products and resource utilization. In this research, optimal operation allocations to groups of machines in a facility is termed the Resource Assignment Subproblem or RAS. The task of locating facilities onto predefined locations in the layout, taking into consideration operation sequences, is termed the Location Assignment Subproblem or LAS. Both the RAS and LAS generally involve complicated discrete mathematical models, thus, many past researchers have chosen to investigate them separately. Recent research conducted by Ketcham (1992a) has led to a mathematical representation which integrates LAS and RAS into a single model, called the Configuration Problem (CP). The solution method, which is a heuristic called the Resource Assignment algorithm, is also found to provide acceptably good solutions for complex models using reasonable computational efforts. The current implementation of the Resource Assignment algorithm, however, becomes inadequate for today's large-scale CFM facilities, where many products are produced at different sites. Thus, enhanced methodologies are developed in this research so that systems of various complexity and characteristics can be optimized more efficiently. The study of algorithm performance and solution characteristics has led to several enhancement techniques. The collection of these techniques, called the Meta-algorithm is a set of decision rules that suggests an optimization strategy, for the Resource Assignment algorithm, based on the characteristics of a given CFM system. The robustness of the Meta-algorithm is tested against a wide range of trial cases representing large-scale CFM systems found in industrial practice. Overall improved performance has been achieved by the Meta-algorithm, which is found most effective for segmenting and solving large-scale problems infeasible for the existing Resource Assignment algorithm.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:dissertations-8273 |
| Date | 01 January 1992 |
| Creators | Punyagupta, Pirasan |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Language | English |
| Detected Language | English |
| Type | text |
| Source | Doctoral Dissertations Available from Proquest |
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