基于蚁群优化算法的步进式加热炉调度  

Scheduling of Walking Beam Reheating Furnaces Based on Ant Colony Optimization Algorithm

作　　者：屠乃威[1] 罗小川[1] 柴天佑[2] 

机构地区：[1]东北大学流程工业综合自动化教育部重点实验室,辽宁沈阳110819 [2]东北大学自动化研究中心,辽宁沈阳110819

出　　处：《东北大学学报（自然科学版）》2011年第1期1-4,9,共5页Journal of Northeastern University(Natural Science)

基　　金：国家重点基础研究发展计划项目(2009CB320601);国家自然科学基金资助项目(60974091;61020106003)

摘　　要：针对钢铁企业生产中的步进式加热炉调度问题,同时考虑到加热炉的生产能耗与热轧机的生产效率,以板坯的实际加热时间、热轧机等待加热板坯的时间以及加热炉内冷热板坯混装次数最小化为目标,建立了冷热板坯混装模式下的步进式加热炉调度问题的数学模型.并针对模型的特点,设计了求解模型的蚁群优化算法.算法中嵌入基于邻域搜索的局部搜索过程,提高蚁群优化算法的收敛速度.使用钢厂实际生产数据的仿真实验验证了所提模型及算法的有效性.To solve the scheduling problem of walking beam reheating furnaces with the energy consumption and productivity of hot rolling mills taken into account,a mathematical model was developed for the mixed charging of cold and hot slabs.The objectives were to minimize the practical heating time,waiting time for heated slabs during hot rolling and the number of the mixed chargings.An ant colony optimization（ACO） algorithm was therefore designed to solve the model,which was embedded in the local search process based on neighbourhood search,thus improving the convergence rate.Results of the simulation in according to actual production data of an iron/steel plant showed the effectiveness of both the model and algorithm proposed.

关 键 词：步进式加热炉 调度 混装 邻域搜索 蚁群优化算法 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]