加热炉优化调度模型及算法研究

加热炉是热轧生产中主要的能源消耗设备,其合理调度对于降低生产过程的能耗和生产成本都具有重要作用.根据加热炉的生产工艺和约束条件建立了加热炉优化调度数学模型,针对模型特点提出了分散搜索（scattersearch,SS）算法,设计了基于随机变量序列的投票组合算子和单点交叉组合算子.根据国内某钢铁企业加热炉生产过程的实绩随机生成40个测试案例,进行实验,分析了参考集规模及不同组合算子对SS算法性能的影响,并与遗传局域搜索（genetic local search,GLS）算法的求解结果进行了比较.结果表明所提出的模型和算法对解决本文研究的加热炉调度问题有效.

Model and algorithm for scheduling reheating furnace

Reheating furnace is the major equipment in the hot-rolled production. Improving the scheduling of reheating furnace is an effective way to reduce the energy consumption and production costs. According to the production process and constraints on the reheating furnace, we propose a mathematical model for scheduling the reheating furnace, and present a scatter search(SS) algorithm to solve this model. We also design the random-variable-sequence-based voting combination operator(RVSBVCO) and the one-point-crossover combination operator(OPCCO). From the production data of an ironand-steel production enterprise, we randomly generate 40 instances for testing the model and the algorithm. The impact on the effectiveness and efficiency of the algorithm from the sizes of reference sets and two combination operators is evaluated and compared with the results obtained from the genetic local search(GLS) algorithm. Results show that the proposed model and algorithm are effective for solving the reheating furnace scheduling problem.