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浙江大学学报(工学版)
J4  2013, Vol. 47 Issue (9): 1517-1523    DOI: 10.3785/j.issn.1008-973X.2013.09.001
计算机技术，无线电电子学     
磁性材料成型烧结生产调度优化方法及应用
刘业峰1，徐冠群1，潘全科1，柴天佑1,2
1. 东北大学 流程工业综合自动化国家重点实验室, 辽宁 沈阳 110819；2. 东北大学 自动化研究中心,
辽宁 沈阳 110819
Magnetic material molding sintering production scheduling optimization method and its application
LIU Ye-feng1, XU Guan-qun1, PAN Quan-ke1, CHAI Tian-you1,2
1. State Key Laboratory of Integrated Automation for Process Industries, Northeastern University,
Shenyang 110819, China; 2. Research Center of Automation, Northeastern University, Shenyang 110819, China
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摘要：

建立以最小化提前和拖期时间、最小化炉重偏差为目标的混合整数线性规划模型, 解决磁性材料成型-烧结两阶段生产调度问题. 提出一种混合粒子群优化算法(HPSO)进行模型的求解,该算法采用基于订单的编码方式. 针对粒子群算法易陷入局部最优, 在迭代过程中引入模拟退火思想. 改进粒子群算法的全局极值和个体极值选取方式, 使算法尽快收敛到非劣最优解. 生产现场实际数据仿真结果表明: 该混合粒子群算法无论在求解精度, 还是求解速度上均优于普通粒子群算法和遗传算法.
Abstract:

A mixed integer linear programming model was built to solve molding and sintering two stage production scheduling problem of magnetic material with the optimization objectives of minimizing earliness and tardiness time and minimizing furnace heavy deviation. A hybrid particle swarm optimization (HPSO) algorithm  was proposed to solve the model. Encoding based on the order was adopted in the algorithm. For the particle swarm optimization (PSO) algorithm is easy to fall into local minima, simulated annealing was introduced in the iteration process. In order to make the algorithm to converge to the non-inferior optimal solution as soon as possible, the selection mode of PSO′s global extreme and individual extreme was improved. Simulation results with actual data of production field showed that the proposed hybrid particle swarm algorithm is better than the general particle swarm algorithm and genetic algorithm (GA) either in solving precision or speed.
出版日期: 2013-09-01
:  TP 18  
基金资助:

国家自然科学基金资助项目(61174187)|教育部基本科研业务费资助项目(N110208001)；东北大学启动基金资助项目(29321006).
通讯作者: 潘全科，男，教授.     E-mail: panquanke@mail.neu.edu.cn
作者简介: 刘业峰(1981-), 男, 博士生, 从事智能优化算法及其应用研究. E-mail: lyf-327@163.com
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引用本文:

刘业峰，徐冠群，潘全科，柴天佑. 磁性材料成型烧结生产调度优化方法及应用[J]. J4, 2013, 47(9): 1517-1523.

LIU Ye-feng, XU Guan-qun, PAN Quan-ke, CHAI Tian-you. Magnetic material molding sintering production scheduling optimization method and its application. J4, 2013, 47(9): 1517-1523.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2013.09.001    或    http://www.zjujournals.com/eng/CN/Y2013/V47/I9/1517

［1］ MANSOURI S A. A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly lines ［J］. European Journal of Operation Research, 2005, 167(3): 696-716．
［2］ TAVAKKOLI-MOGHADDAM R, RAHIMI-VAHED A R. Multi-criteria sequencing problem for a mixed-model assembly line in a JIT production system ［J］. Applied Mathematics and Computation, 2006, 181(2): 1471-1481．
［3］ HE Y, HUI C W. Genetic algorithm for large-size multi-stage batch plant scheduling ［J］. Chemical Engineering Science, 2007, 62(5): 1504-1523．
［4］ FAHIMI-VAHED A R, MIRGHORHANI S M, RABBANI M. A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem ［J］. Soft Computing: A Fusion of Foundations, Methodologies and Applications, 2007, 11(10):997-1012．
［5］ 董巧英, 阚树林, 桂元坤, 等.基于改进离散微粒群优化算法的混流装配线多目标排序［J］. 系统仿真学报, 2009,21(22): 7103-7108．
DONG Qiao-ying, KAN Shu-lin, GUI Yuan-kun, et al. Mixed model assembly line multi-objective sequencing based on modified discrete particle swarm optimization algorithm［J］. Journal of System Simulation, 2009,21(22): 7103-7108．
［6］ 刘炜琪, 刘琼, 张超勇, 等. 基于混合粒子群算法求解多目标混流装配线排序［J］. 计算机集成制造系统, 2011,17(12): 2590-2598．
LIU Wei-qi, LIU Qiong, ZHANG Chao-yong, et al. Hybird particle swarm optimization for multi-objective sequrencing problem in mixed model assembly lines ［J］. Computer Integrated Manufacturing Systems, 2011,17(12): 2590-2598．
［7］ 张利彪, 周春光, 马铭, 等. 基于粒子群算法求解多目标优化问题［J］. 计算机研究与发展, 2004, 41(7): 1287-1292．
ZHANG Li-biao, ZHOU Chun-guang, MA Ming, et al. Solutions of multi-objective optimization problems based on particle swarm optimization［J］. Journal of Computer Research and Development, 2004, 41(7): 1287-1292．
［8］ KENNED Y J, EBERHART R C. Particle swarm optimization ［C］∥Proc of IEEE International Conference on Neural Networks. NewYork:IEEE,1995: 1942-1948．
［9］ KENNEDY J, EBERHART R. Particle swarm optimization［C］∥IEEE International Conference on Neural Networks. Perth, Australia:IEEE,1995．
［10］ EBERHART R, KENNEDY J. A new optimizer using particle swarm theory［C］∥ Proc of the 6th International Symposium on Micro Machine and Human Science. Piscataway, NJ : IEEE Service Center, 1995: 139-143．
［11］ SHIAND Y, EBERHART R. A modified particle swarm optimizer［C］∥IEEE International Conference on Evolutionary Computation. Anchorage, Alaska:IEEE,1998．
［12］ 张长胜, 孙吉贵, 杨轻云,等. 一种求解车间调度的混合算法［J］. 自动化学报, 2009, 35(3): 332-336．
ZHANG Chang-sheng, SUN Ji-gui, YANG Qing-yun. A hybrid algorithm for flowshop scheduling problem［J］. ACTA Automatic Sinica, 2009, 35(3): 332-336．
［13］ 潘全科, 朱剑英. 基于Petri 网和混合算法的作业车间优化［J］.计算机集成制造系统, 2007, 13(3): 580-584.
PAN Quan-ke, ZHU Jian-ying. Optimization of job shop scheduling based on Petri net & hybrid algorithm［J］. Computer Integrated Manufacturing Systems, 2007, 13(3): 580-584．
［14］ 邓慈云, 陈焕文, 刘泽文, 等. 一种求解Job-Shop调度问题的混合自适应变异粒子群算法［J］. 计算机工程与科学, 2010, 32(1): 47-50．
DENG Ci-yun, CHEN Huan-wen, LIU Ze-wen, et al. A hybrid adaptive mutation particle swarm optimization algorithm for Job-Shop scheduling ［J］. Computer Engineering & Science, 2010, 32(1): 47-50.
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