改进遗传算法在磁性材料组炉优化问题中的应用

Application of improved genetic algorithm to magnetic materials group furnace optimization problem

中文关键词  组炉  遗传算法  逆转算子  伪旅行商问题  磁性材料

英文关键词  furnace-grouping  genetic algorithm  reverse operator  traveling salesman problem  magnetic materials

中文摘要

      牌号、交货日期、优先级、需求量等是磁性材料生产工单的属性, 计划员需要依据上述属性寻求最优的生 产工单组合以最小化生产成本并提高生产效率. 针对磁性材料企业人工组炉存在的组炉时间长, 组炉结果不优化问 题. 本文建立了磁性材料生产工单组炉优化模型. 提出将该组炉问题转化为伪旅行商问题, 并采用一种改进遗传算 法求解. 染色体编码采用从1到N的自然数编码方式, 并设计一种基于最早完工日期规则的初始种群产生方法. 引 入精英选择策略和改进的贪心三交叉算子, 优化遗传算法收敛速度和精度; 引入逆转算子, 提高遗传算法全局搜索 能力. 基于实际生产数据的仿真实验表明, 建立的磁性材料组炉优化模型是合适的, 所提改进算法是有效的.

英文摘要

      Grade, due date, priority, and demand are attributes of magnetic material production work orders. Planners are required to seek the optimal combination of production work orders to minimize the cost and improve the efficiency based on these attributes. To deal with the lengthy furnace-grouping time and the non-optimal furnace-grouping result in magnetic materials enterprises, we build a mathematical model for solving the furnace-grouping optimization problem with unknown charge number. This problem is first transformed into a pseudo traveling salesman problem and then is solved by using an improved genetic algorithm. Natural number coding from 1 to N is the coding scheme that this paper adopted. An initial population generation method is designed following the sort criteria of Earliest Completion Date (ECD). An elite strategy and an improved greedy three-crossover operator (3PM) are introduced to enhance the convergence speed and precision, whereas a reverse operator is applied to improve the exploitation for the presented algorithm. Simulation results based on practical production data show that the built model is appropriate and the proposed algorithm is effective.