基于混合教与学优化算法的炼钢连铸调度  

Steelmaking and continuous casting scheduling based on hybrid teaching-learning-based optimization algorithm

作　　者：马文强[1] 张超勇[1] 唐秋华[2] 邵新宇[1] 贾艳[3] 

机构地区：[1]华中科技大学机械学院数字制造装备与技术国家重点实验室,湖北武汉430074 [2]武汉科技大学机械自动化学院,湖北武汉430081 [3]西华大学机械工程与自动化学院,四川成都610039

出　　处：《计算机集成制造系统》2015年第5期1271-1278,共8页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金重点资助项目(51035001);国家自然科学基金资助项目(51275190);中央高校基本科研业务费资助项目(2014TS038);教育部"春晖计划"合作科研资助项目(Z2012017)~~

摘　　要：根据炼钢厂的实际生产环境,建立了无等待多工艺路线的炼钢连铸模型,提出一种混合教与学优化算法求解该模型。在混合算法中,引入转换规则的教与学优化算法求解离散问题;采用变邻域搜索调整机器选择,教与学优化算法调整调度顺序的方式,将并行问题串行化。对具体实例进行测试,将人工调度方法、遗传算法、教与学优化和混合教与学优化算法的结果进行比较,验证了所提算法的可行性和有效性。According to the real-world process environment, a no-wait multi-process steelmaking and continuous cast- ing scheduling model -was built. A hybrid Teaching-Learning-Based Optimization （TLBO） algorithm was introduced to solve this model. TLBO algorithm with mapping rule was used for solving discrete problems. By using Variable Neighborhood Search （VNS）, machine selection was adjusted, and TLBO was used to adjust scheduling sequences. With testing for cases, the results of manual scheduling method, genetic algorithm, TLBO and hybrid TLBO were compared with each other to verify the effectiveness and feasibility of proposed algorithm.

关 键 词：炼钢连铸 教与学优化算法 转换规则 浇次 炉次 

分 类 号：F224[经济管理—国民经济]