Knowledge-based improvement: simulation and artificial intelligence for identifying and improving human decision-making in an operations system

被引:18
作者
Robinson, S [1 ]
Alifantis, T
Edwards, JS
Ladbrook, J
Waller, A
机构
[1] Univ Warwick, Warwick Business Sch, Operat Res & Syst Grp, Coventry CV4 7AL, W Midlands, England
[2] Univ Birmingham, Birmingham B15 2TT, W Midlands, England
[3] Ford Motor Co Ltd, Dunton Engn Ctr 154AF04D, Basildon, Essex, England
基金
英国工程与自然科学研究理事会;
关键词
simulation; artificial intelligence; human decision; making; knowledge elicitation; expert system;
D O I
10.1057/palgrave.jors.2601915
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
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.
引用
收藏
页码:912 / 921
页数:10
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