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Potential of artificial neural networks for resource scheduling

Published online by Cambridge University Press:  27 February 2009

Nabil Kartam and
Tanit Tongthong
Nabil Kartam
Affiliation:
Department of Civil Engineering, Kuwait University, P.O. Box 5969, Safat, 13060, Kuwait
Tanit Tongthong
Affiliation:
Department of Civil Engineering, Vongchavalitkul University, Muang, Nakhonratchasima, Thailand
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Abstract

In a construction project, resource leveling techniques are necessary as a primary schedule-improvement tool to reduce overall project cost by decreasing day-to-day fluctuation in resource usage and resource idleness. There are, however, some limitations in traditional resource leveling techniques. Conventional heuristic approaches cannot guarantee a near-optimum solution for every construction project; a given heuristic may perform well on one project and poorly on another. The existing optimization approaches, such as linear programming and enumeration methods, are best applicable only to small size problems. Recently, there has been success in the use of Artificial Neural Networks (ANNs) for solving some optimization problems. The paper discusses how state-of-the-art ANNs can be a functional alternative to traditional resource leveling techniques. It then investigates the application of different ANN models (such as backpropagation networks, Hopfield networks, Boltzmann machines, and competition ANNs) to resource leveling problems. Because the development of ANNs involves not only science but also experience, the paper presents various intuitive yet effective ways of mapping resource leveling problems on different applicable ANN architectures. To demonstrate the application of ANNs to resource leveling, a simple ANN model is developed using a Hopfield network. The conclusion highlights the usefulness and the limitations of ANNs when applied to resource leveling problems.

Keywords

Artificial Neural NetworksResource SchedulingResource LevelingParallel Computing

Information

Type
Articles
Information
AI EDAM , Volume 11 , Issue 3 , June 1997, pp. 171 - 185
Copyright
Copyright © Cambridge University Press 1997

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