An Area-Decomposition Method for a Nesting System

Nesting is primarily composed of two processes: One is the determination of permutation; another is the placement for the pieces unto the sheet. These two processes determine the quality for nesting. If the permutation is right and placement also can satisfy the requirements, then we can locate the proper solution  or the optimum one; but most of the time, there is no way to know the correct permutation beforehand, therefore the search algorithm is needed to locate the optimum solution instead. Nonetheless, if the placement is not good enough, even the permutation is correct; still, the nesting result would not be perfect. Hence this research is to target the nesting requirement for pieces with gaps in between and develop the placement algorithm of innovation which integrates the simulated annealing characteristics so as to proceed to the optimum solution for nesting system with the best possible nesting results. The purpose of optimizing two-dimensional problem is to place the same or different number of pieces onto the sheet, enabling the usage ratio for sheet reaching the highest possible or cutting down the waste in the end, thus, with the high usage ratio of the sheet would result with cutting down the cost for materials.

[1]  Daniele Vigo,et al.  Approximation algorithms for the oriented two-dimensional bin packing problem , 1999, Eur. J. Oper. Res..

[2]  K. Dowsland,et al.  A tabu thresholding implementation for the irregular stock cutting problem , 1999 .

[3]  S. Somasundaram,et al.  A sliding algorithm for optimal nesting of arbitrarily shaped sheet metal blanks , 1995 .

[4]  L. Fogel,et al.  European Journal Ofoperational Research on Genetic Algorithms for the Packing of Polygons , 1996 .

[5]  José Fernando Oliveira,et al.  A 2-exchange heuristic for nesting problems , 2002, Eur. J. Oper. Res..

[6]  John E. Beasley,et al.  An evolutionary heuristic for the index tracking problem , 2003, Eur. J. Oper. Res..

[7]  John E. Beasley,et al.  Scatter Search and Bionomic Algorithms for the aircraft landing problem , 2005, Eur. J. Oper. Res..

[8]  Zafer Bingul,et al.  Hybrid genetic algorithm and simulated annealing for two-dimensional non-guillotine rectangular packing problems , 2006, Eng. Appl. Artif. Intell..

[9]  E. Hopper,et al.  An empirical investigation of meta-heuristic and heuristic algorithms for a 2D packing problem , 2001, Eur. J. Oper. Res..

[10]  John E. Beasley A population heuristic for constrained two-dimensional non-guillotine cutting , 2004, Eur. J. Oper. Res..

[11]  Cihan H. Dagli,et al.  An approach to two-dimensional cutting stock problems , 1987 .

[12]  Cihan H. Dagli,et al.  Employing subgroup evolution for irregular-shape nesting , 2004, J. Intell. Manuf..

[13]  Jacek Blazewicz,et al.  Parallel Tabu Search Approaches For Two-Dimensional Cutting , 2004, Parallel Process. Lett..