求解扩展双资源约束作业车间调度的分支种群遗传算法
Branch Population Genetic Algorithm for Extension Dual Resource Constrained Job Shop Scheduling Problem

根据扩展双资源约束作业车间调度问题的特点，构造了一种混合遗传算法进行求解:以分支种群为载体继承遗传进化经验，利用精英进化算子、基于扇形分割的轮盘赌选择算子及邻域搜索等机制，进一步优化了算法性能。通过分析策略对比仿真、算法性能对比仿真等实验，结果表明上述各种优化机制可行，且对于算法运算效率与寻优性能的优化效果均有良好表现。
In this paper, a hybrid genetic algorithm was proposed for solving extension dual resource constrained job shop scheduling problem. The algorithm was constructed based on inheriting evolution experience of parent population with the branch population. In addition, this algorithm used some optimization operators to optimize algorithm performance, such as the elite evolutionary operator, the roulette selection operator based on sector partition, the variable neighbourhood search operator, and so on. Finally, the optimization performances of above mechanisms were validated according to the statistical analysis on the simulation results of strategies comparison simulation and algorithm performance comparison simulation