A hybrid optimization method for the production of work order group furnaces was proposed in this paper. First, a working order group furnace model was constructed including performance index, the constraint condition and the decision variable to fit the problems in working order group furnaces. The hybrid optimization method consists of an optimal priority and a variable neighborhood search algorithm. In the algorithm, we have adopted a lot of rules and corresponding grade limits on stock production. Based on the proposed algorithm’s calculation results, the delivery time deviation, grade deviation and priority deviation of the 20 group furnace production orders are reduced from 58 to 42 with reduction rate of 27.59%. The satisfaction rate for grade preparation is increased from 4 to 6, which has a rate of increase of 50%. In order to prove the effectiveness of the proposed algorithm, the proposed algorithm is compared with other literature algorithms such as a discrete particle swarm algorithm, a variable neighborhood search algorithm and an adaptive variable neighborhood search algorithm.

提出了一种用于工单组炉生产的混合优化方法。首先，建立了工单群炉模型，包括性能指标，约束条件和决策变量，以适应​​工单群炉的问题。混合优化方法由最优优先级和可变邻域搜索算法组成。在算法中，我们对库存生产采用了许多规则和相应的等级限制。根据该算法的计算结果，将20组炉生产订单的交货时间偏差，等级偏差和优先级偏差从58个减少到42个，减少率为27.59％。成绩准备的满意度从4提高到6，增长率为50％。