Please use this identifier to cite or link to this item:
Title: Pattern nesting on irregular-shaped stock using genetic algorithms
Authors: Tay, F.E.H. 
Chong, T.Y.
Lee, F.C.
Keywords: Boundary
Genetic Algorithms
Issue Date: Dec-2002
Citation: Tay, F.E.H., Chong, T.Y., Lee, F.C. (2002-12). Pattern nesting on irregular-shaped stock using genetic algorithms. Engineering Applications of Artificial Intelligence 15 (6) : 551-558. ScholarBank@NUS Repository.
Abstract: Pattern nesting aims to position 2-D shapes on a sheet so as to achieve maximum usage of a stock, or equivalently to minimise wastage. There are different methods used on computer to lay out the positions of the shapes on the stock, such as linear programming and heuristic method. A recent approach attempts to use Genetic Algorithms (GAs) to solve the problem of pattern nesting. The successful development of using GAs to nest 2-D shapes on regular-shaped stock has proved the feasibility of using GAs to solve pattern nesting problem. This work presents a new method of solving the pattern nesting problem on irregular-shaped stock using GAs, known as the evolutionary boundary nesting algorithm. This approach further generalises the scope of the pattern nesting problem by allowing nesting on stocks of any shapes and sizes. This implies that the nesting algorithm can be used universally in any industry, such as the garment, shipbuilding and aerospace industry. Basically, the shapes are nested sequentially in the stock and the evolutionary boundary nesting algorithm uses GAs to find the best position to nest each shape along the boundary of the stock. © 2003 Elsevier Science Ltd. All rights reserved.
Source Title: Engineering Applications of Artificial Intelligence
ISSN: 09521976
DOI: 10.1016/S0952-1976(03)00009-5
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Oct 16, 2018


checked on Oct 8, 2018

Page view(s)

checked on May 12, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.