To read this content please select one of the options below:

A survey of AI in operations management from 2005 to 2009

Khairy A.H. Kobbacy (School of Built Environment, University of Salford, Salford, UK)
Sunil Vadera (School of Computing, Science and Engineering, University of Salford, Salford, UK)

Journal of Manufacturing Technology Management

ISSN: 1741-038X

Article publication date: 26 July 2011




The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence, the purpose of this paper is to present a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.


The paper builds upon our previous survey of this field which was carried out for the ten‐year period 1995‐2004. Like the previous survey, it uses Elsevier's Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case‐based reasoning (CBR), fuzzy logic (FL), knowledge‐Based systems (KBS), data mining, and hybrid AI in the four application areas are identified.


The survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.


This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.



Kobbacy, K.A.H. and Vadera, S. (2011), "A survey of AI in operations management from 2005 to 2009", Journal of Manufacturing Technology Management, Vol. 22 No. 6, pp. 706-733.



Emerald Group Publishing Limited

Copyright © 2011, Emerald Group Publishing Limited

Related articles