摘要
针对炼钢生产组炉计划编制问题,建立了相应的数学模型,并提出了基于PBIL与网络最大流的求解算法.该算法首先利用启发式规则获取炉次上界,并以此为基础,设计0-1染色体编码的PBIL算法,每个染色体代表一个炉次选择方案,并使用网络最大流理论求解染色体的具体组炉策略,给出染色体适应值,迭代后得到合同与炉次的最优匹配方案.经实际生产数据测试,利用该算法可以在较短的时间内给出较优的组炉方案,为计划员提供足够的决策支持.
A mathematical model and an optimization algorithm, which is based on PBIL （population-based incremental learning） and network maximum flow, were proposed for the charge design problem of steel-making. The algorithm first finds an upper bound of the number of charges, which serves as the baseline for designing PBIL with 0- 1 chromosome encoding, through a heuristic rule. Each chromosome represents a selection scheme of charges, and the network maximum flow theory is used to calculate the fitness value for chromosome. The optimal order-furnace matching strategy could be obtained after several iterations. Simulations on real production data indicated that the proposed algorithm can obtain an optimized matching solution within reasonable time, and can provide enough decision support for planners.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2012年第1期52-55,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(71021061)
关键词
炼钢
组炉
计划编制
PBIL算法
网络最大流
steel-making
charge design
plan-making
PBIL algorithm
network maximum flow
