Knowledge-based improvement: simulation and artificial intelligence for identifying and improving human decision-making in an operations system
| Autor: | Stewart Robinson, Anthony Waller, John S. Edwards, John Ladbrook, Thanos Alifantis |
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| Rok vydání: | 2005 |
| Předmět: |
Marketing
Decision support system 021103 operations research business.industry Computer science Strategy and Management 0211 other engineering and technologies Information technology 02 engineering and technology Management Science and Operations Research computer.software_genre Purchasing Expert system Management Information Systems Knowledge base Information and Communications Technology 0202 electrical engineering electronic engineering information engineering Information system 020201 artificial intelligence & image processing Artificial intelligence Project management business computer |
| Zdroj: | Journal of the Operational Research Society. 56:912-921 |
| ISSN: | 1476-9360 0160-5682 |
| DOI: | 10.1057/palgrave.jors.2601915 |
| Popis: | The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as 'knowledge-based improvement' (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant. |
| Databáze: | OpenAIRE |
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