| 基于GSA-GA神经网络的铸造企业订单准入评价研究 |
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| 引用本文： | 唐红涛,方博,高晓灵,李香怡,殷伟铭. 基于GSA-GA神经网络的铸造企业订单准入评价研究[J]. 工业工程, 2020, 23(4): 121-130. DOI: 10.3969/j.issn.1007-7375.2020.04.016 |
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| 作者姓名： | 唐红涛 方博 高晓灵 李香怡 殷伟铭 |
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| 作者单位： | 武汉理工大学 机电工程学院，湖北 武汉 430070 |
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| 基金项目： | 国家自然科学基金资助项目(51705384) |
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| 摘 要： | 为指导铸造企业在市场中科学地选择订单进行生产加工，以砂型铸造企业为研究对象。在综合考虑企业各个关键部门对订单的评价指标的基础上，设计了砂型铸造企业订单准入评价指标体系，并构建一种基于GSA-GA神经网络的订单准入评价模型。算法中使用Tent映射初始化种群保证随机特征，并引入遗传算法的交叉、变异算子保持对全局最优粒子的获取且提升算法探索能力。为验证模型的有效性，以某砂型铸造企业订单准入评价问题实例进行实验分析，进行参数实验并与其他算法进行对比。结果表明，所建模型的平均相对误差为1.945%，能帮助砂型铸造企业进行科学的订单准入评价决策，提高砂型铸造企业生产效率。
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| 关 键 词： | GSA-GA算法 砂型铸造 神经网络优化 订单准入评价 |
| 收稿时间： | 2019-05-29 |
A Research on the Order Acceptance Evaluation of Foundry Enterprises Based on GSA-GA Neural Networks |
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| TANG Hongtao,FANG Bo,GAO Xiaoling,LI Xiangyi,YIN Weiming. A Research on the Order Acceptance Evaluation of Foundry Enterprises Based on GSA-GA Neural Networks[J]. Industrial Engineering Journal, 2020, 23(4): 121-130. DOI: 10.3969/j.issn.1007-7375.2020.04.016 |
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| Authors: | TANG Hongtao FANG Bo GAO Xiaoling LI Xiangyi YIN Weiming |
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| Affiliation: | School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China |
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| Abstract: | In order to guide the foundry enterprises to scientifically select orders for production in the market, sand casting enterprises are the research object. Based on the comprehensive consideration of the evaluation indicators of the key departments of the enterprise, the order acceptance evaluation index system of sand casting enterprises is designed, and an order acceptance evaluation model based on hybrid GSA-GA neural network is constructed. In the algorithm, the Tent map is used to initialize the population to guarantee the random features, and the crossover and mutation operators of the genetic algorithm are introduced to maintain the global optimal particle acquisition and improve the algorithm exploration ability. To verify the validity of the model, an example of a sand casting company's order acceptance evaluation problem is used for experimental analysis, and the parameter experiment is carried out and compared with other algorithms. The results show that the average relative error of the model is 1.945%, which can help sand casting enterprises to make scientific order acceptance evaluation decisions and improve the production efficiency of sand casting enterprises. |
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| Keywords: | GSA-GA algorithm sand casting neural network optimization order acceptance evaluation |
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