Comparison of stochastic and approximation algorithms for one-dimensional cutting problems

Authors:
Zafer Bingul;Cuneyt Oysu
Affiliations:
Department of Mechatronics Engineering, Kocaeli University, Kocaeli, Turkey;Department of Mechatronics Engineering, Kocaeli University, Kocaeli, Turkey
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

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Abstract

The paper deals with the new algorithm development and comparison of three one-dimensional stock cutting algorithms regarding trim loss. Three possible types of problems used in this study are identified as easy, medium and hard. Approximate method is developed which enables a comparison of solutions of all three types of problems and of the other two stochastic methods. The other two algorithms employed here are Genetic Algorithms (GA) with Improved Bottom-Left (BL) and Simulated Annealing (SA) with Improved BL. Two examples of method implementation for comparison of three algorithms are presented. The approximate method produced the best solutions for easy and medium cutting problems. However, GA works very well in hard problems because of its global search ability.