加热炉优化调度模型及算法研究  

Model and algorithm for scheduling reheating furnace

作　　者：谭园园[1] 宋健海[2] 刘士新[1] 

机构地区：[1]东北大学信息科学与工程学院流程工业综合自动化国家重点实验室,辽宁沈阳110819 [2]上海宝信软件股份有限公司,上海201900

出　　处：《控制理论与应用》2011年第11期1549-1557,共9页Control Theory & Applications

基　　金：国家自然科学基金资助项目(70771020;70721001);国家"863"计划/先进制造技术领域专题资助项目(2007AA04Z194);新世纪优秀人才支持计划资助项目(NCET-06-0286)

摘　　要：加热炉是热轧生产中主要的能源消耗设备,其合理调度对于降低生产过程的能耗和生产成本都具有重要作用.根据加热炉的生产工艺和约束条件建立了加热炉优化调度数学模型,针对模型特点提出了分散搜索(scattersearch,SS)算法,设计了基于随机变量序列的投票组合算子和单点交叉组合算子.根据国内某钢铁企业加热炉生产过程的实绩随机生成40个测试案例,进行实验,分析了参考集规模及不同组合算子对SS算法性能的影响,并与遗传局域搜索(genetic local search,GLS)算法的求解结果进行了比较.结果表明所提出的模型和算法对解决本文研究的加热炉调度问题有效.Reheating furnace is the major equipment in the hot-rolled production.Improving the scheduling of reheating furnace is an effective way to reduce the energy consumption and production costs.According to the production process and constraints on the reheating furnace,we propose a mathematical model for scheduling the reheating furnace,and present a scatter search（SS） algorithm to solve this model.We also design the random-variable-sequence-based voting combination operator（RVSBVCO） and the one-point-crossover combination operator（OPCCO）.From the production data of an iron-and-steel production enterprise,we randomly generate 40 instances for testing the model and the algorithm.The impact on the effectiveness and efficiency of the algorithm from the sizes of reference sets and two combination operators is evaluated and compared with the results obtained from the genetic local search（GLS） algorithm.Results show that the proposed model and algorithm are effective for solving the reheating furnace scheduling problem.

关 键 词：加热炉调度 住炉时间 候选板坯集合 分散搜索算法 组合算子 遗传局域搜索算法 

分 类 号：TG307[金属学及工艺—金属压力加工]