Scheduling is one of the most important functions in a factory and it is determining when and with what resources jobs should be accomplished. An important factor that affects the scheduling of jobs is the dynamic variation of factory status. Existing computer based scheduling systems do not address the need of making effective decisions dynamically with the variations in factory status. Traditionally, Operations Research techniques have provided an effective tool in solving manufacturing planning problems. But these methods have not been able to effectively address real time control problems in the manufacturing environment.
To address some of these problems, this research investigates applying an expert system approach to develop an interactive real time dynamic scheduling system. Specifically, a knowledge base structure is developed and applied to a case study representing a two stage production system.
A Blackboard concept has been utilized to organize and maintain the dynamic data base. The major knowledge representation schemes used in the system include, frame structures, relational tables, and production rules. The system was tested on a case study, by conducting a sample interactive session on a set of simulated dynamic situations. The test demonstrated the viability of implementing knowledge based systems for dynamic scheduling at the operational level of a plant. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/45907 |
| Date | 20 November 2012 |
| Creators | Salgame, Rangnath R. |
| Contributors | Industrial Engineering and Operations Research, Sarin, Subhash C., Sherali, Hanif D., Byler, Richard K. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis, Text |
| Format | viii, 121 leaves, BTD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | OCLC# 16679472, LD5655.V855_1987.S25.pdf |
Page generated in 0.002 seconds