Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 912-921 |
| Number of pages | 10 |
| Journal | Journal of the Operational Research Society |
| Volume | 56 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - Aug 2005 |
Bibliographical note
This is a post-peer-review, pre-copied its version of an article published in Journal of the Operational Research Society]. The definitive publisher-authenticated version Robinson, S., Alifantis, T., Edwards, J. S., Ladbrook, J., & Waller, A. (2005). Knowledge based improvement: simulation and artificial intelligence for identifying and improving human decision-making in an operations systems. Journal of the Operational Research Society, 56(8), 912-921 is available online at: http://www.palgrave-journals.com/jors/journal/v56/n8/full/2601915a.htmlKeywords
- simulation
- artificial intelligence
- human decision-making
- knowledge elicitation
- expert system