Abstract: To solve the Hot Rolling Scheduling Problem in Compact Strip Production (HRSP_CSP),a Two-stage Intelligent Optimization Algorithm (TIOA) was proposed.To reasonably control costs and ensure benefits,the primary and secondary objectives were adopted for optimization.The primary objective was to add the minimum number of non-commissioned strips (strips produced outside the order) to form the minimum the number of  rolling batch.The secondary objective was how to determine the set of strip steel and rolling sequence in each batch that determined by the primary objective,so as to minimize the average rolling smoothness (the average rolling smoothness of each batch of strip steel).Based on the characteristics of HRSP_CSP,TIOA was  designed as a two-stage optimization algorithm.In the previous stage of TIOA,a heuristic algorithm was proposed to obtain the optimal solution of the problem (minimum number of rolling batchs).In the later stage of TIOA,an Improved Artificial Bee Colony algorithm (IABC) was designed to obatin the high-quality solution of the problem in a short time.IABC adopted the set based coding method to design the optimal decoding strategy of the problem solution to improve the quality of the solution,and two types of swap neighborhood operations combined with the invalid neighborhood judgment strategy were designed to enhance the local search efficiency.The effectiveness of TIOA was verified by simulation and algorithm comparison.

Key words: heuristic algorithms, hot-rolling scheduling, artificial bee colony, compact strip production

摘要： 针对实际生产中广泛存在的紧凑型带钢生产热轧调度问题(HRSP_CSP),提出一种两阶段智能优化算法(TIOA)进行求解。为合理控制成本并确保效益,采用主次目标进行优化。主目标为如何加入最少的无委托带钢(即订单外生产的带钢)来构成最小轧制批次;次目标为如何在主目标确定的轧制批次中,确定各批次内的带钢集合和轧制顺序,以实现轧制平均平滑率(即各批次带钢轧制平滑率的平均值)最小。基于HRSP_CSP的特点,TIOA设计为两阶段优化算法。在TIOA的前一阶段,分析问题特征,提出启发式算法获取问题的最优解(即最小轧制批次)。在TIOA的后一阶段,分析问题性质,提出改进人工蜂群(IABC)算法在较短时间内获取问题的优质解。IABC算法采用基于集合的编码方式,并设计问题解的最优解码策略来提升解的质量,同时设计结合无效邻域判断策略的两类Swap邻域操作以增强局部搜索效率。通过仿真实验和算法对比,验证了TIOA的有效性。

关键词: 启发式算法, 热轧调度, 人工蜂群, 紧凑型带钢生产