Intelligent management systems in operations: a review
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Intelligent management systems in operations: a review NC Proudlove1, S VaderaÂ2 and KAH Kobbacy2
1
UMIST and 2 University of Salford
Operations management is an area that has recently started to bene®t from the use of AI techniques such as expert systems, neural networks and genetic algorithms. These techniques can extend the usefulness of OR modelling and enable new types of decision tasks to be supported by computer-based systems. This paper attempts to review `intelligent' decision support systems and their potential to address some of the problems faced in various areas of operations management. Some useful techniques developed in the ®eld of arti®cial intelligence are outlined and examples of attempts to use these approaches to support decision making in various areas of operations management are described. Recognising the scale of a complete review of all these areas, emphasis has been given to the most signi®cant and more recent publications. Keywords: arti®cial intelligence; decision support systems; expert systems; management; operations; production
Introduction The management of operations often involves seeking to resolve several, sometimes con¯icting, goals such as increasing quality, ¯exibility, variety, responsiveness to customers and integration with other business functions, and reducing costs, inventory and product life-cycles. Historically, there has been a tendency for researchers from the Operational Research (OR) and Arti®cial Intelligence (AI) communities to attempt to address such problems independently. The OR community has developed various models based on mathematical principles such as those for inventory management, maintenance, scheduling and replacement policies. The successful use of such models in practice can require the simpli®cation of a problem so that a model's assumptions are satis®ed, the estimation of the model's parameters and the careful interpretation of results. In contrast, the AI community has taken a knowledge based approach to similar problems. Instead of developing sound models that are applicable to a well de®ned but limited range of problems, the AI community has focused on developing and using heuristics that use qualitative criteria as well as numeric parameters to obtain solutions. Heuristic approaches may not guarantee optimal solutions but their use does enable solutions to be obtained even when there is uncertainty and missing information about the problem.
Correspondence: Dr KAH Kobbacy, Centre for Operational Research and Applied Statistics, University of Salford, Salford, M5 4WT, UK. E-mail: [email protected]
Given the nature and complexity of problems faced in operations management, one can therefore expect considerable bene®ts from cross-fertilisation between the OR and AI communities. As one example of the potential bene®ts, consider the problem of assimilating vast quantities of data and turning it into useful inform
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