A genetic algorithm approach for the cutting stock problem

In this paper, a genetic algorithm approach is developed for solving the rectangular cutting stock problem. The performance measure is the minimization of the waste. Simulation results obtained from the genetic algorithm-based approach are compared with one heuristic based on partial enumeration of...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2003-04, Vol.14 (2), p.209
Main Authors: Onwubolu, Godfrey C, Mutingi, Michael
Format: Article
Language:English
Subjects:
Algorithms
Manufacturing
Nesting
Operations research
Optimization
Studies
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Summary:In this paper, a genetic algorithm approach is developed for solving the rectangular cutting stock problem. The performance measure is the minimization of the waste. Simulation results obtained from the genetic algorithm-based approach are compared with one heuristic based on partial enumeration of all feasible patterns, and another heuristic based on a genetic neuro-nesting approach. Some test problems taken from the literature were used for the experimentation. Finally, the genetic algorithm approach was applied to test problems generated randomly. The simulation results of the proposed approach in terms of solution quality are encouraging when compared to the partial enumeration-based heuristic and the genetic neuro-nesting approach. [PUBLICATION ABSTRACT]
ISSN:0956-5515
1572-8145
DOI:10.1023/A:1022955531018