改进混合多目标蚁群算法求解带运输时间和调整时间的柔性作业车间调度问题

Improved hybrid multi-objective ant colony optimization for flexibleJob-Shop scheduling problem with transportation time and setup time

柔性作业车间调度问题是一类重要的组合优化问题,实际生产过程中,产品搬运、机床换模、更换刀具等间接加工活动中存在运输时间和调整时间,会对生产周期产生影响。研究了同时考虑运输时间和调整时间的柔性作业车间调度问题,建立以最小化最大完工时间、机器总负载、机器关键负载和工件的交货期惩罚值为目标的数学模型,并提出一种改进的混合多目标蚁群算法。结合问题特征和算法特点设计了一种分布式编码方式,采用改进蚁群算法分别搜索各优化目标的最优调度方案,针对调度方案集进行非支配排序选择,为了提高算法的搜索精度,提出了突变和靠拢操作。最后通过基准实例和生产实例进行仿真实验,并与改进遗传算法、MOGATS算法进行对比,实验结果表明提出的改进混合多目标蚁群算法是有效和可行的。

Flexible job shop scheduling is an important combinatorial optimization problem.In the actual production process,there are transportation time and setup time in indirect processing activities such as product handling,and machine tool changing,which will affect the production cycle.This paper studied the flexible job shop scheduling problem considering both transportation time and setup time,established a mathematical model with the objectives of minimizing the maximum completion time,total workload,the workload of the critical machine,and penalties of earliness/tardiness,and proposed an improved hybrid multi-objective ant colony optimization.It designed a distributed coding method by combining the problem characteristics and algorithm features,using an improved ant colony optimization to search for the optimal scheduling solution for each optimization objective separately,performing non-dominated sorting selection for the set of scheduling solutions,and proposing mutation and closeness operations in order to improve the search accuracy of the algorithm.Finally,it conducted simulation experiments using benchmark and production instance and compared them with the improved genetic algorithm and MOGATS algorithm,and the experimental results showed that the proposed improved hybrid multi-objective ant colony optimization is effective and feasible.