Graduate Thesis Or Dissertation
 

Schedule-based material requirements planning : an artificial intelligence approach

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/vx021h653

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  • The objective of this research project was to identify the limitations associated with schedule-based Material Requirements Planning (SBMRP) and to present a knowledge-based expert system (KBES) approach to solve these problems. In SBMRP, the basic strategy is to use backward or forward scheduling based on an arbitrary dispatching rule, such as First-In First-Out. One of the SBMRP weak points is that it does not use such job information as slack times, due dates, and processing times, information which otherwise is important to good scheduling decisions. In addition, the backward scheduling method produces a better schedule than the forward scheduling method in terms of holding and late costs. Dependent upon job characteristics, this may or may not be true and should be tested. This study focused on the means to overcome these two weak points by the use of a KBES. Heuristic rules were developed through an experiment-based knowledge acquisition process to generate better schedules, rather than relying solely upon forward or backward scheduling. Scheduling performance was measured, based on the minimization of the sums of holding and late costs. Due to complexities of the problem, heuristic methods were used rather than analytic methods. In particular, five loading rules were selected, based upon their close relationship to selected job characteristics, including processing times and due dates. Combined loading methods (CLMs) were developed to obtain better performance, derived by combining the two existing SBMRP scheduling strategies with five loading heuristic rules. This resulted in the generation of 10 CLMs for further evaluation. Since this study proposed a new approach, an expert human scheduler was not available. To overcome this problem, knowledge acqusition through computer experimentation (KACE) was utilized, based upon an architecture of five components: job generator, scheduler, evaluator, rule generator (an extended version of ID3), and the KBES. The first three components were used to generate a large number of examples required by the rule generator to derive knowledge. This derived knowledge was incorporated into the KBES. Experimental results indicated that the KBES outperformed the two existing SBMRP methods. Based on sensitivity analysis, the KBES exhibited robust performance with regard to every job parameter except number of parts. As the number of parts was increased, KBES performance was subject to degradation since the possibility of interactions or conflicts between parts tended to increase, resulting in shifting the threshold ratio of total available time to total processing time. Thus, it is strongly recommended that a new KBES capable of accommodating 30 parts or more should be developed using the KACE method.
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