Article Abstract

	Title:	An intelligent decision-support system for rough mills
	Author:	Eman Elghoniemy, Ozge Uncu, William A. Gruver, Dilip B. Kotak, Martin Fleetwood
	Address:	Intelligent/Distributed Enterprise Automation Laboratory, School of Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada. ' Intelligent/Distributed Enterprise Automation Laboratory, School of Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada. ' Intelligent/Distributed Enterprise Automation Laboratory, School of Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada. ' Institute for Fuel Cell Innovation, National Research Council of Canada, 3250 East Mall, Vancouver, BC V6T 1W5, Canada. ' Institute for Fuel Cell Innovation, National Research Council of Canada, 3250 East Mall, Vancouver, BC V6T 1W5, Canada
	Journal:	International Journal of Manufacturing Technology and Management 2006 - Vol. 8, No.1/2/3  pp. 203 - 225
	Abstract:	A rough mill is where wood components are cut from lumber to produce wooden doors and windows. Because lumber is a natural material it contains various types of defects (e.g. knots and splits) but their distribution is not known in advance. Furthermore, only the approximate content and dimension of each board in a load are known ahead of time. Thus, producing required components from different types of lumber in a rough mill is quite a complex challenge. Several operations that occur in the rough mill are closely related and are analysed in this paper. An Intelligent Decision-Support System (IDSS) is proposed to improve the overall performance of the rough mill by presenting recommendations to operators. A rough mill simulator helps operators view the effect of these recommendations before implementing them on the production floor. Two key challenges for rough mills are identified, namely: selection of appropriate jags and cut-list scheduling and alternative solutions are proposed.
	Keywords:	decision support systems; intelligent DSS; rough mills; distributed systems; cut-list scheduling; jag selection; fuzzy systems; genetic algorithms; wood manufacturing; intelligent systems; industrial automation; fuzzy logic; simulation; lumber.
	DOI:	10.1504/IJMTM.2006.008796
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