Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343180
Title: An expert system for material handling equipment selection
Author: Al-Meshaiei, Eisa Abdullah Eisa S.
ISNI:       0000 0001 3407 2174
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 1999
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Abstract:
Manufacturing Systems are subject to increasingly frequent changes in demand in terms of number and type of products they produce. It is impractical to continually reconfigure the facilities, but it is possible to modify the material handling arrangements so that the selected equipment is the most appropriate for the current requirements. The number of decisions that need to be made coupled with the rate at which decisions must be taken adds significant difficulty to the problem of equipment selection. Furthermore there are relatively few experts who have the necessary range of knowledge coupled with the ability to use this knowledge to select the most appropriate material handling solution in any situation. Access to such experts is therefore greatly restricted and decisions are more commonly made by less experienced people, who depend on equipment vendors for information, often resulting in poor equipment selection. This research first examines the significance of appropriate material handling equipment choice in dynamic environments. The objective is to construct a computer based expert system utilising knowledge from the best available sources in addition to a systematic procedure for selection of material handling equipment. A new system has been produced, based on the Flex language, which elicits from the inexperienced user details of the handling requirements in order to build an equipment specification. It then selects from among 11 handling solution groups and provides the user with information supporting the selection. Original features of the system are the way in which the knowledge is grouped, the ability of the procedure to deal with quantifiable and non-quantifiable equipment and selection factors, selection of decision analysis method and the validation of the final choice to establish confidence in the results. The system has been tested using real industrial data and has been found in 81% of cases to produce results which are acceptable to the experts who provided the information.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.343180  DOI: Not available
Keywords: QA76 Electronic computers. Computer science. Computer software ; TS Manufactures Computer integrated manufacturing systems Computer software Artificial intelligence
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