AI and OR in management of operations: history and trends

Kobbacy, KAH, Vadera, S ORCID: https://orcid.org/0000-0001-6041-2646 and Rasmy, MH 2007, 'AI and OR in management of operations: history and trends' , Journal of the Operational Research Society, 58 (1) , pp. 10-28.

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Abstract

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested.

Item Type:	Article
Themes:	Subjects / Themes > H Social Sciences > HD Industries. Land use. Labor > HD0028 - 0070 Management. Industrial Management Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science Subjects / Themes > T Technology > TS Manufactures > TS155-194 Production management. Operations management Subjects outside of the University Themes
Schools:	Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title:	Journal of the Operational Research Society
Publisher:	Palgrave Macmillan
Refereed:	Yes
ISSN:	01605682
Depositing User:	H Kenna
Date Deposited:	21 Aug 2007 13:52
Last Modified:	16 Feb 2022 07:36
URI:	https://usir.salford.ac.uk/id/eprint/274

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