Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (22): 307-314.DOI: 10.3778/j.issn.1002-8331.2207-0227

• Engineering and Applications • Previous Articles     Next Articles

Improved COOT Algorithm to Solve Multi-Objective Flexible Jobshop Scheduling Problem

LING Fangping, JI Weixi   

  1. School of Mechanical Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2023-11-15 Published:2023-11-15



  1. 江南大学 机械工程学院,江苏 无锡 214122

Abstract: Aiming at the multi-objective flexible jobshop scheduling problem(FJSP), a model with the completion time, total machine load and energy consumption as the optimization objectives is established, and the multi-objective COOT algorithm combined with simulated annealing(MOCOOT-SA) is proposed to solve it. The algorithm optimizes the original single-objective COOT algorithm into a multi-objective algorithm by introducing the concept of archive set and Pareto solution, and selects a new neighborhood structure and update method for specific individuals, and then integrates the simulated annealing algorithm(SA) to optimize local search ability and convergence speed. Finally, the appropriate codec method is selected, the MOCOOT-SA algorithm is used to test the improved benchmark example, and compared with the results of the NSGA-II algorithm and the MOPSO algorithm, the average optimization ratio of each target is 0.013~0.047, the optimal value optimization ratio is 0.016~0.045. The results show the advantage of this algorithm that it can better solve the multi-objective FJSP.

Key words: flexible jobshop, production scheduling, multi-objective optimization, MOCOOT-SA algorithm

摘要: 针对柔性车间调度的多目标优化问题,建立了以完工时间、机器总负荷、能耗为优化目标的模型,并提出了一种结合模拟退火的多目标COOT算法(multi-objective COOT algorithm combined with simulated annealing,MOCOOT-SA)进行求解。该算法通过引入存档集和Pareto解的理念,将原有的单目标COOT算法优化成多目标算法,并为其中特定个体选择新的邻域结构和更新方式,再融合模拟退火算法(simulated annealing,SA)优化局部搜索能力和收敛速度。最后选用合适的编解码方式,用MOCOOT-SA算法测试改进的基准算例,并与NSGA-Ⅱ算法、MOPSO算法的结果进行对比,得到各目标上的平均值优化比为0.013~0.047,最优值优化比为0.016~0.045。结果表明,该算法的优点是能更好地解决多目标柔性车间调度问题。

关键词: 柔性车间, 生产调度, 多目标优化, MOCOOT-SA算法