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一种具有工艺约束的板材切割调度改进分层遗传算法。

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

作者信息

Rao Yunqing, Qi Dezhong, Li Jinling

机构信息

The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan, Hubei 430074, China.

Shenyang Donfon Titanium Industry Co., Ltd, Shenyang, Liaoning 110168, China.

出版信息

ScientificWorldJournal. 2013 Dec 24;2013:202683. doi: 10.1155/2013/202683. eCollection 2013.

DOI:10.1155/2013/202683
PMID:24489491
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3886606/
Abstract

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

摘要

首次在集成下料模型中提出了一种改进的分层遗传算法，用于解决板材切割问题，该问题涉及m台不同的并行机器和n种切割模式，且存在工艺约束。切割调度问题的目标是最小化加权完成时间。提出了该问题的数学模型，开发了一种改进的分层遗传算法（蚁群-分层遗传算法）以获得更好的解决方案，并根据问题的特点采用了分层编码方法。此外，为了加快收敛速度并解决局部收敛问题，该算法采用了一种自适应交叉概率和变异概率。计算结果和比较证明，所提出的方法对于所考虑的问题非常有效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/e9ae5367985b/TSWJ2013-202683.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/612033e6275f/TSWJ2013-202683.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/3ad244088c17/TSWJ2013-202683.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/48b16ab64fc8/TSWJ2013-202683.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/0529eb1bef3a/TSWJ2013-202683.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/1d97fcc2761c/TSWJ2013-202683.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/e9ae5367985b/TSWJ2013-202683.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/612033e6275f/TSWJ2013-202683.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/3ad244088c17/TSWJ2013-202683.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/48b16ab64fc8/TSWJ2013-202683.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/0529eb1bef3a/TSWJ2013-202683.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/1d97fcc2761c/TSWJ2013-202683.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/755e/3886606/e9ae5367985b/TSWJ2013-202683.alg.001.jpg

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