基于新型蛙跳算法的低碳柔性作业车间调度

A novel shuffled frog leaping algorithm for low carbon flexible job shop scheduling

针对低碳柔性作业车间调度问题(flexible job shop scheduling problem,FJSP),提出一种新型蛙跳算法(shuffled frog leaping algorithm,SFLA)以总碳排放最小化,该算法运用记忆保留搜索所得一定数量的最优解,并采取基于种群和记忆的种群划分方法,应用新的搜索策略如全局搜索与局部搜索的协调优化以实现模因组内的搜索,取消种群重组使算法得到简化.采用混合遗传算法和教–学优化算法作为对比算法,大量仿真对比实验验证了SFLA对于求解低碳FJSP具有较强的搜索能力和竞争力.

In this paper low carbon flexible job shop scheduling problem(FJSP) is considered. A new shuffled frog leaping algorithm(SFLA) is proposed to minimize total carbon emission, in which memory is used to store best solutions.Population division is done by using population and memory. Some new strategies such as cooperation of global search and local search are applied to realize the search in the memeplex. Population shuffling is deleted to simplify the algorithm.We compared hybrid genetic algorithm and teaching-learning-based optimization algorithm, which also considered the combination of local search and global search. Extensive experiments are conducted on a number of instances and result analyses show that SFLA has strong search ability and competitiveness for low carbon FJSP.