摘要
确定每台机器上各工件的投入顺序与投入时间是车间作业调度所要解决的问题,这种顺序必须和技术约束相容,使某一性能指标达到最优是其最终目的 .寻找高效的调度方法,可以极大的提高资源的利用率和生产效益。遗传算法具有自组织性,并行性和自适应性,对于组合优化问题的求解有着自己的独特的优势,很快便被引入到了车间调度问题的研究领域车间调度问题是典型的NP难题,为了克服传统遗传算法解决车间作业调度问题的局限性,综合遗传算法和局部搜索的优点,提出一种改进的遗传算法,即贪心算法与遗传算法相结合,并通过实验数据证明了该方法的有效性。
The work piece invested order to invest time on each machine job shop scheduling problem to be solved,this order must be compatible and technical constraints,the optimal is the ultimate purpose of a performance indicator.Looking for efficient scheduling method,can greatly improve the utilization of resources and production efficiency.The genetic algorithm has self-organization,parallel and adaptability has its own unique advantages for solving combinatorial optimization problems,will soon be introduced to the research areas of shop scheduling problem shop scheduling problem is NP-hard,in order to overcome the limitations of traditional genetic algorithm to solve the job shop scheduling problem,a comprehensive genetic algorithms and the advantages of local search,proposed an improved genetic algorithm,greedy algorithm and genetic algorithm that combined,and by the experimental data to prove the effectiveness of this method.
出处
《电子测试》
2013年第3X期141-143,共3页
Electronic Test
关键词
遗传算法
贪心算法
车间调度
greedy algorithm,genetic algorithm,anneal algorithm,job shop scheduling
