离散车间能效数据挖掘及调度优化

doi:10.16182/j.issn1004731x.joss.19-FZ0257

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (12): 2702-2711.doi: 10.16182/j.issn1004731x.joss.19-FZ0257

离散车间能效数据挖掘及调度优化

林雨谷, 王艳

江南大学,物联网技术应用教育部工程中心,江苏无锡 214122

收稿日期:2019-03-08 修回日期:2019-06-26 发布日期:2019-12-13
作者简介:林雨谷(1994-),男,江苏盐城,硕士生,研究方向为智能调度优化; 王艳(1978-),女,江苏盐城,博士,教授,研究方向为基于大数据知识自动化的离散制造能耗网络协同优化。
基金资助:
国家自然科学基金(61973138)

Energy Efficiency Data Mining and Scheduling Optimization of Discrete Workshop

Lin Yugu, Wang Yan

Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China

Received:2019-03-08 Revised:2019-06-26 Published:2019-12-13

摘要/Abstract

摘要： 离散车间的加工过程是由不同零件的加工子过程串联或者并联构成的复杂柔性加工过程。面向离散车间的能效优化,建立了离散车间能效优化模型;关联数据挖掘和知识发现关系;通过对调度数据预处理和C4.5决策树学习算法,实现对调度知识的发现;将调度知识和改进后的差分进化算法(IDE)相结合用于离散车间的能效优化计算。通过和TLBO、GA和PSO算法仿真比较验证了IDE算法的可行性。

关键词: 能效, 数据挖掘, 调度知识, 差分进化算法

Abstract: This paper addresses the optimization of energy consumption in discrete workshops and establishes the energy efficiency optimization model of discrete workshops. The relationship between data mining and knowledge discovery is established. Through scheduling data preprocessing and C4.5 decision tree learning algorithm, the discovery of scheduling knowledge is realized. Energy efficiency optimization calculation is achieved in discrete workshops by the combination of scheduling knowledge and improved differential evolution algorithm (IDE). By comparing with TLBO, GA and PSO, the feasibility of IDE algorithm is verified.

Key words: energy efficiency, data mining, scheduling knowledge, differential evolution algorithm

中图分类号:

TP391.9

林雨谷, 王艳. 离散车间能效数据挖掘及调度优化[J]. 系统仿真学报, 2019, 31(12): 2702-2711.

Lin Yugu, Wang Yan. Energy Efficiency Data Mining and Scheduling Optimization of Discrete Workshop[J]. Journal of System Simulation, 2019, 31(12): 2702-2711.

参考文献

[1] 王凌. 车间调度及其遗传算法[M]. 北京: 清华大学出版社, 2003.
Wang Ling.Job shop scheduling and its genetic algorithm[M]. Beijing: Tsinghua University Press, 2003.
[2] 刘博. 柔性作业车间动态生产调度问题的研究[D]. 沈阳: 东北大学, 2007.
Liu Bo.Research on dynamic production scheduling in flexible job shop[D]. Shenyang: Northeastern University, 2007.
[3] 王景华, 韩江洪, 刘征宇, 等. 面向对象Petri网离散制造系统生产调度建模研究[J]. 系统仿真学报, 2008, 20(15): 4159-4162.
Wang Jinghua, Han Jianghong, Liu Zhengyu, et al.Modeling of production scheduling for a discrete manufacturing system based on object - oriented Petri net[J]. Journal of system simulation, 2008, 20(15): 4159-4162.
[4] Boulle M.Khiops: A Statistical Discretization Method of Continuous Attributes[J]. Machine Learning (S0885-6125), 2004, 55(1): 53-69.
[5] Wang Y, Ji D.Data- and Rule-Based Integrated Mechanism for Job Shop Scheduling[J]. International Journal of Computer and Communication Engineering (S2010-3743), 2015, 4(3): 180-186.
[6] Mohamed A W, Sabry H Z, Khorshid M.An alternative differential evolution algorithm for global optimization[J]. Journal of Advanced Research (S2090-1232), 2012, 3(2): 149-165.
[7] Wisittipanich W, Kachitvichyanukul V.Differential Evolution Algorithm for Job Shop Scheduling Problem[J]. International Journal of Production Research (S0020-7543), 2011, 50(10): 2757-2773.
[8] 徐军辉, 王艳. 基于改进教与学算法的离散制造车间能效优化[J]. 系统仿真学报, 2016, 28(12): 3019-3026.
Xu Junhui, Wang Yan.Optimization of energy efficiency of discrete manufacturing workshop based on improved teaching and learning algorithm[J]. Journal of system simulation, 2016, 28(12): 3019-3026.
[9] 解书琴. 基于多目标混合人工蜂群算法的能效优化调度研究[D]. 武汉: 华中科技大学, 2014.
Xie Shuqin.Study on energy efficiency optimization scheduling based on multi-objective hybrid artificial swarm algorithm[D]. Wuhan: Huazhong University of Science and Technology, 2014.
[10] 王成龙, 李诚, 冯毅萍, 等. 作业车间调度规则的挖掘方法研究[J]. 浙江大学学报(工学版), 2015, 49(3): 421-429.
Wang Chenglong, Li Cheng, Feng Yiping, et al.Study on the mining method of job-shop scheduling rules[J]. Journal of Zhejiang University (Engineering), 2015, 49(3): 421-429.
[11] 冯亚. 数据挖掘中决策树分类算法研究与应用[D]. 西安: 西北大学, 2007.
Feng Ya.Research and application of decision tree classification algorithm in data mining[D]. Xi'an: Northwestern University, 2007.
[12] Boulle M.Khiops: A Statistical Discretization Method of Continuous Attributes[J]. Machine Learning (S0885-6125), 2004, 55(1): 53-69.

[1]	余伟, 梁恒辉, 罗映. 一种改进差分进化算法的分数阶系统辨识研究[J]. 系统仿真学报, 2021, 33(5): 1157-1166.
[2]	韩驰, 熊伟. 航天侦察装备体系指标关联信息挖掘研究[J]. 系统仿真学报, 2021, 33(10): 2372-2380.
[3]	梅盼盼, 吴亮红, 张红强, 王慧莹. 含风电电力系统动态经济调度的差分纵横交叉优化[J]. 系统仿真学报, 2020, 32(6): 1179-1187.
[4]	王艳, 丁宇. 动态柔性作业车间优化调度与决策方法[J]. 系统仿真学报, 2020, 32(11): 2073-2083.
[5]	吴敬兵, 唐汉卿, 胥军. 水泥窑协同处置生活垃圾的燃烧特性分析优化[J]. 系统仿真学报, 2020, 32(1): 35-43.
[6]	王杰, 陈彬, 袁鹏, 马亮, 邱晓刚. 数据驱动的最优互惠避碰模型偏好速度研究[J]. 系统仿真学报, 2019, 31(12): 2731-2739.
[7]	吕盛坪, 乐强生, 刘涛. 基于数据挖掘的印制电路样板投料优化[J]. 系统仿真学报, 2018, 30(7): 2656-2665.
[8]	程艳云, 边荟凇, 边长生. 基于图数据模型的聚类方法及可信度检测[J]. 系统仿真学报, 2018, 30(6): 2102-2109.
[9]	张静华, 韩璞. 多算法多种群协同优化差分进化算法[J]. 系统仿真学报, 2018, 30(5): 1690-1699.
[10]	陈冰, 张亨, 程泽凯, 董鹏, 林超. Robocup2D仿真对抗中进攻行为的挖掘与验证[J]. 系统仿真学报, 2018, 30(12): 4718-4726.
[11]	陈文杰, 王艳. 离散制造过程生产协同能耗优化调度方法[J]. 系统仿真学报, 2018, 30(11): 4367-4375.
[12]	彭竹清, 王艳, 纪志成. 基于改进熵值法的离散制造系统能效评价方法[J]. 系统仿真学报, 2017, 29(9): 2121-2127.
[13]	单鑫, 王艳, 纪志成. 基于参数知识鸽群算法的离散车间能效优化[J]. 系统仿真学报, 2017, 29(9): 2140-2148.
[14]	李芹, 张浩, 彭道刚, 郭义波, 王念龙, 孙宇贞. 基于改进差分算法的电站锅炉主蒸汽温度多变量建模研究[J]. 系统仿真学报, 2017, 29(8): 1712-1718.
[15]	张妍, 王东风, 韩璞. 基于IDE-LSSVM的风电场短期风速预测[J]. 系统仿真学报, 2017, 29(7): 1561-1571.

离散车间能效数据挖掘及调度优化

Energy Efficiency Data Mining and Scheduling Optimization of Discrete Workshop

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价