Shadow price based genetic algorithms for the cutting stock problem
International Journal of Artificial Intelligence and Soft Computing
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One-dimensional cutting-stock is one of the classic NP-hard problems in combinatorial optimization. It is widely applied in engineering technology and industrial production. In this paper, an improved ant colony optimization is proposed based on the optimized one-dimensional cutting-stock model. Aiming at the specific characteristics of the problem, a series of improvement strategies are proposed, including part encoding, solution path, state transition probability and pheromone updating rules. Then the concrete steps of algorithm are described. Through the analysis and comparison of experimental results, this method is proved high efficiency.