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板坯库实时生产物流管理系统的研发
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摘要
在钢铁企业炼钢-连铸-热轧一体化生产模式下,板坯库作为连铸、热轧生产工序间的缓冲环节,其管理自动化水平的高低,直接影响着连铸-热轧生产的连续性和一体化生产的成本。在以往的研究中,人们主要集中在炼钢、连铸和热轧的计划与调度中,忽视了对板坯库管理与控制的研究。因此,本文根据重钢热轧厂1780mm热连轧生产线的实际情况,对板坯库实时生产物流管理系统进行研究,采用Justep Business业务架构平台和Oracle数据库,以Delphi为开发语言,设计并开发满足重钢热轧厂生产需要的板坯库实时管理系统软件。
     本文的主要工作如下:
     ①分析了板坯入库选择垛位的原则,对影响板坯库作业效率的主要因素进行了归纳,以板坯库有限储位和垛位选择原则为约束,建立了一种基于铸轧作业计划协同优化的板坯入库决策模型,实现需入库的板坯批次的全局优化运算,算法可快速优化出板坯入库垛位和铸机板坯产出序。为了克服遗传算法和模拟退火算法在实际应用中显现的不足,构造了遗传模拟退火算法,该算法充分发挥了遗传算法良好的全局搜索能力和模拟退火算法有效避免陷入局部极小的优点,从而提高了算法的全局寻优能力。基于遗传模拟退火算法实现了板坯入库优化程序,并进行了大量的计算。
     ②详细分析板坯库实时管理系统的需求、热轧和炼钢的物流和信息流,完成了板坯库实时管理系统总体设计和系统实现。
     ③基于Justep Business业务架构平台和Oracle数据库在MES系统中实现所提出的板坯库管理系统。该生产管理系统对热连轧产线的物料库存、技术质量、生产计划、设备、数据采集等进行管理,实现物流和信息流的协调统一。
     重钢热轧厂板坯库实时管理系统软件开发采用软件工程设计方法,依次经过需求分析、详细设计、软件编程、和软件测试等步骤,应用图形界面友好,较好地实现设计目标,得到重钢现场专家们的肯定。
Under the incorporate product model of steel-making, casting, hot-rolling in steel enterprise, slab-yard is used to amortise the product line between the casting and hot-rolling, and the continuity and cost of slab operating is directly effected by the automatic level of its management。In previous Research, people concentrated in continuous casting and hot rolling of the planning and scheduling, ignored the storage management system Research and control.Based the demand of information constructing of Chongqing Iron and Stee Company Limited(CISC),the paper researched the designing and actualizing of the steel company 1780mm hot strip rolling manufacturing management system.It especially researched the slab-yard management.Based on the Justep Business platform and Oracle database,delphi as the development of language,the storage management system in MES was developed.
     The contribution aspects can be embodied:
     ①The rules of slab location and pile position selection are analyzed. According to the factors we induce that affect the efficiency, based on the collaborative optimization of CC-HR operating plan, a slab location decision model which is subject to the limited HCR slab-yard storage and the rules of slab location and pile position selection is suggested. The model is a general optimal operation to a slab lot of entering slab-yard and can quickly optimize the pile position for each slab and the casting sequence. The advantages and disadvantages of genetic algorithms and simulated annealing algorithm are analyzed, and a genetic simulated annealing algorithmx is applied to solve the model. In the algorithm, it makes full use of the excellent whole searching ability of genetic algorithm, and the advantage that simulated annealing algorithm can efficiently avoid getting into part minimum, thus the global searching ability of is improved. The slab location optimization program based on GSA is realized and calculated many times.
     ②Analysed the demand for the storage management system and the production logistics management information system of the Third Steel-Making Mill and the Second Hot Strip Mill rolling of CISC the storage management system has been designed and realized.
     ③Based on the Justep Business platform and Oracle database,the storage management system in MES was developed.The material storage,technical quality, manufacturing plan,equipment,data gathering in the company were took charge by the manufacturing management system.It made the material flow and the information flow harmonious.
     The method of software engineering was adopted in system development, and requirement analysis, system design, detail design, programming and testing were applied to the system. finally, the storage management system was developed in the slab continuous casting direct hot charge rolling (CC-DHCR) integrated management system for CISC. The software run online now, and it work stability and reliability, have friendly graphic user interface, and reach the target of the project. Steel-Making Experts are very satisfied with this system in CISC.
引文
[1]郑秉霖,胡馄元,常春光.一体化钢铁生产计划系统的研究现状与展望[J].控制工程,2003,10(1):6-10.
    [2]孙福权.炼钢-热轧一体化生产管理模型体系及算法研究[D].沈阳:东北大学博士论文,1999.11.
    [3]徐心和,陈雄,郭令忠,谈金东.炼钢-连铸-热轧一体化管理[J].冶金自动化,1997,3:1-4.
    [4]李耀华,胡国奋,王伟,宁树实.炼钢-连铸-热轧一体化生产计划编制方法研究[J].控制工程. 2005,(06).
    [5]何志林,李苏剑.武钢2250mm热轧厂生产物流管理系统的研究与开发[J].物流技术. 2004,(02).
    [6]薛伟红.武钢-热轧板坯库生产物流管理系统研究[J].北京科技大学,2001.
    [7]翁卫兵.武钢二热轧板坯库生产物流管理系统分析与研究[J].北京科技大学,2003.
    [8]王冰洁,李苏剑.武钢二热轧板坯库实时生产物流管理系统研究与设计[J].物流技术, 2004,(12).
    [9]何志林,李苏剑.面向实时物流作业的板坯库管理系统[J].物流技术与应用, 2004,(08).
    [10]乐峻,黄豪.热轧板坯库计算机系统在生产中的运用[J].安徽冶金科技职业学院学报,2005,(S2).
    [11]李苏剑,常志明著.连铸-连轧生产物流管理[J].北京:冶金工业出版社,2001.
    [12]李苏剑,陈宗海.板坯的ABC分类与管理[J].宝钢技术,1994,(05).
    [13]吕志民,刘文仲,徐金梧等.业务流程重组在热送热装生产组织中的应用[J].钢铁,2003,38(1):71-75.
    [14]李苏剑,陈宗海.宝钢板坯库入库决策模型[J].物流技术,1995,6:25-27.
    [15] [李耀华,徐乐江,胡国奋,王伟,宁树实.基于混沌遗传算法的板坯入库决策优化方法[J].系统仿真学报,2005,17(11):2620-2623.
    [16]施光林,史维祥.遗传算法及其研究与应用新进展[J].科技导报,1997,(04).
    [17]谢云.模拟退火算法原理及实现[J].高等学校计算数学学报,1999,9,212-218.
    [18]尤矢勇,谢云.模拟退火算法冷却进度表的参数选择[J].武汉大学学报(并行计算专刊),1991,3,71-82.
    [19]谢云.用模拟退火算法并行求解整数规划问题[J].高技术通讯,1991,1(10),21-26.
    [20]周丽,黄素珍.基于模拟退火的混合遗传算法研究[J].计算机应用研究,2005,(09).
    [21]王雪梅,王义和.模拟退火算法与遗传算法的结合[J].计算机学报,1997,(04).
    [22]余冬梅,张秋余,伊华伟.一种融合改进模拟退火技术的新型遗传算法[J].计算机应用,2005,(10).
    [23]李苏剑.宝钢连铸一连轧物流模型及其应用研究[D].北京科技大学博士论文,1993.
    [24]内部资料:吕志民.重钢炼钢-热轧MES生产组织工艺设计方案.
    [25]蒋杨虎,肖坤伟等.连铸坯热送热装工艺热技术概述[J].武钢技术. 1998(7): 21~22.
    [26]李婧等.武钢二热轧CC—DHCR生产模式研究[J].钢铁. 2002(10): 74~77.
    [27]董绍华.连铸坯直接热装(DHCR)轧制计划计算机编制系统[J].物流技术与应用. 1997(2): 17~20.
    [28]杨越等.钢铁厂综合化生产调度系统[J].冶金自动化. 1998(5): 22~25.
    [29]宋立东,田乃媛,唐洪华,许剑桦.薄板坯连铸连轧流程中故障与缓冲的研究[J].钢铁. 2002(11): 30~35.
    [30]彭其春,田乃媛,萧忠敏,王德城.新一代炼钢-连铸-热轧区段配置[J].炼钢. 2002,18(5) : 31~34.
    [31]武钢二热轧三级机计算机系统附件1.北京科技大学. 2001.
    [32]张海藩.软件工程导论[M].清华大学出版社. 1998.
    [33]赵瑞雪.论信息系统的开发与建模[M]. 1999(8): 5~7.
    [34] [美]Mark Fewster, Dorothy Graham.软件测试自动化技术与实例详解[M].电子工业出版社. 2000.
    [35]美Roger S pressman.软件工程-实践者的研究方法[J].机械工业出版社. 1999: 543~549.
    [36]赵京胜.软件工程新型开发方法探讨[J].现代计算机. 2001(1): 20~23.
    [37] Yeniay O . Penalty Function Methods for Constrained Optimization with Genetic Algorithms[J].Mathematical & Computational Applications,2005,10(1):45-56.
    [38] Jian P L . A Species Conserving Genetic Algorithm for Multimodal Function Optimization[J].Evolutionary Computation,2002,10(3):207-234.
    [39] Deng J J.A Hybrid Genetic Algorithm for Function Optimization[J].Journal of Software,1999,10(8):819-823.
    [40] De Jong K A . An Analysis of the Behavior of a Class of Genetic Adaptive System[J].University of Michigan,1975,No.76-9381
    [41] Lester Ingber,Bruce Rosen.Genetic Algorithms and Very Fast Simulated Reannealing:AComparison[J].Math.And Comp.Modeling,1992,16(1l):.87-100.
    [42] Tang Renyuan,Yang Shiyou,Li Yan,etc.Combined Strategy of Improved SimulatedAnnealing and Genetic Algorithm for Inverse Problem[J] . IEEE TRANSACTION ON MAGNETICS.1996,VOL.32,NO.3.
    [43] B.Hajek.Cooling Schedules for Optimal Annealing[J].Mathematics of OperationsResearch,1988,13:311-329.
    [44] Aarts E.HL,Korst J.H.M.Simulated Annealing and Boltzmann Machines[M].1989,NewYork:John Wiley&Sons Inc.36-42.
    [45] Johnson D.S.,Aragon C.R.,McGeoch L.A.,etc.Optimization by Simulated Annealing: AnExperimental Evaluation[M].1987,New Jersey:Murray Hill Inc.
    [46] Szu H.H.,Hartley RL.Fast Simulated Annealing[J].Physics Letters A.1987, 122:157-162.
    [47] C. R. Zacharias, M. R. Lemes, A. D. Pino.Combining Genetic Algorithm and Simulated Annealing:a Molecular Geometry Optimization Study. Journal of Molecular Structure (Theochem),1998,430:29-39.
    [48] R. N. Bailey, K. M. Garner, M. F. Hobbs.Using Simulated Annealing and Genetic Algorithms to Solve staff Scheduling Problems.Asia-Pacific Journal ofOperational Research,1997,14:27-43.
    [49] P I Cowling. A Flexible Decision Support System for Steel Hot Rolling Mill Scheduling. Accepted for publication in Computers and Industrial Engineering. 2000, 6(5): 26~48 .
    [50] Henderson-Sellers B, Edwards J H. The O-O methodology for object-oriented life cycle[J]. ACM SIGSOFT Software Engineering Notes. 1993(4): 32~36.
    [51] Pollalis Y A. A Sysmatic Approach to Change Management. Information systems Management[J]. New York spring, 1996(4): 51~58.
    [52] H. C. Huang, J. S. Pan, Z. M. Lu, S. H. Sun, H. M. Hang.Vector Quantization Based on Genetic Simulated Annealing.Signal Processing,2001,81:1513-1523.

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