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http://dx.doi.org/10.6109/jicce.2010.8.1.013

A Distributed Stock Cutting using Mean Field Annealing and Genetic Algorithm  

Hong, Chul-Eui (Department of Computer Science, Sangmyung University)
Publication Information
Journal of information and communication convergence engineering / v.8, no.1, 2010 , pp. 13-18 More about this Journal
Abstract
The composite stock cutting problem is defined as allocating rectangular and irregular patterns onto a large composite stock sheet of finite dimensions in such a way that the resulting scrap will be minimized. In this paper, we introduce a novel approach to hybrid optimization algorithm called MGA in MPI (Message Passing Interface) environments. The proposed MGA combines the benefit of rapid convergence property of Mean Field Annealing and the effective genetic operations. This paper also proposes the efficient data structures for pattern related information.
Keywords
Genetic Algorithm; Mean Field Annealing; Optimizations; Parallel and Distributed Computing; Stock Cutting;
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