Abstract:

Production furnace grouping is critical in routine operation of NdFeB processes and the grouping results directly affect production efficiency. Based on actual requirements of a production unit, a mathematical model of grouping performance index, constraint conditions and decision variable was established and algorithm of best priority and variable neighborhood search was proposed for furnace grouping. There were three components of the algorithm, which included multi-layer quick sorting algorithm to determine furnace sequences, best priority and variable neighborhood search algorithm, and heuristic algorithm based on production rules for various grade of raw materials in stock. By using this algorithm for furnace grouping of 20 production work orders, sum of deviations of delivery time, product grade, and production priority was decreased from 58 to 42 with a reduction rate of 27.59% and satisfaction rate of raw material preparation was increased from 4 to 6 with an increase rate of 50%. Compared to results of manual furnace grouping for 40 production work orders, the furnace grouping results of this algorithm was reduced by 2 times of furnace usage. Further comparison with discrete particle swarm algorithm, acoustic variable neighborhood search algorithm, and adaptive variable neighborhood search algorithm demonstrated effectiveness of the proposed algorithm and validity of the mathematical model.

Key words: sintered NdFeB, furnace grouping, best priority, global optimization, manufacturing, model

摘要：

钕铁硼生产企业的生产工单组炉问题是企业生产组织面临的首要问题，组炉结果的好坏直接影响企业的生产效率。本文基于生产工单组炉的实际需求，建立了组炉的目标、面临的约束和相应决策变量的数学模型。针对生产工单组炉的具体问题，提出了基于最佳优先和变邻域搜索的生产工单组炉算法。该算法有3个组成部分，分别是确定生产工单组炉顺序的多层快速排序算法，生产工单组炉的最佳优先和变邻域搜索算法，不同牌号库存备料生产的规则的启发式算法。采用本文的算法，针对20个甩带生产工单的组炉问题，生产工单组炉的交货期偏差、各订单加工的优先级偏差和不同订单的牌号偏差和由58降低到42，下降率为27.59%；牌号的备料达标率由4个上升为6个，提高率为50%。通过对40个生产工单的组炉结果与人工组炉结果的对比，发现组炉结果减少2个炉次。将本文算法与改进的离散粒子群算法，和声变邻域搜索算法和自适应变邻域搜索算法的对比分析，也表明了本文算法的有效性，证明了所建立数学模型的正确性。

关键词: 钕铁硼, 组炉, 最佳优先, 整体优化, 生产制造, 模型