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Alarcón, L F and Bastias, A (2000) A computer environment to support the strategic decision-making process in construction firms. Engineering, Construction and Architectural Management, 7(01), 63–75.
Edwards, D J and Holt, G D (2000) ESTIVATE: a model for calculating excavator productivity and output costs. Engineering, Construction and Architectural Management, 7(01), 52–62.
Huang, R-Y and Halpin, D W (2000) Graphically based LP modelling for linear scheduling analysis: the POLO system. Engineering, Construction and Architectural Management, 7(01), 41–51.
Kamara, J M, Anumba, C J and Evbuomwan, N F O (2000) Establishing and processing client requirements-a key aspect of concurrent engineering in construction. Engineering, Construction and Architectural Management, 7(01), 15–28.
Kumar, V S S, Hanna, A S and Adams, T (2000) Assessment of working capital requirements by fuzzy set theory. Engineering, Construction and Architectural Management, 7(01), 93–103.
Li, H, Cheng, E W L and Love, P E D (2000) Partnering research in construction. Engineering, Construction and Architectural Management, 7(01), 76–92.
Loh, W H and Ofori, G (2000) Effect of registration on performance of construction subcontractors in Singapore. Engineering, Construction and Architectural Management, 7(01), 29–40.
Naoum, S and Haidar, A (2000) A hybrid knowledge base system and genetic algorithms for equipment selection. Engineering, Construction and Architectural Management, 7(01), 3–14.
- Type: Journal Article
- Keywords: artificial intelligence; earthwork; equipment selection; genetic algorithms; knowledge base systems; optimization
- ISBN/ISSN: 0969-9988
- URL: http://www.blackwell-synergy.com/links/doi/10.1046/j.1365-232x.2000.00128.x/abs
- Abstract:
This paper describes the development of a hybrid knowledge base system and genetic algorithms to select the optimum excavating and haulage equipment in opencast mining. The knowledge base system selects the equipment in broad categories based on the geological, technical and environmental characteristics of the mine. To further identify the make, size and number of each piece of equipment that minimizes the total cost of the operation, the problem is solved using the genetic algorithms mechanism. Results of four case studies are presented to show the validation of the developed system.