An Iterative Greedy Algorithm for Solving a Multiobjective Distributed Assembly Flexible Job Shop Scheduling Problem With Fuzzy Processing Time
针对实际生产中加工时间不确定的情况，研究了带二型模糊时间的多目标分布式装配柔性作业车间调度问题，提出一种基于Q学习的迭代贪婪算法，在30个实例上优于现有算法。
Deterministic processing time are no longer applicable under realistic circumstances because of the uncertainties involved in manufacturing and production processes. The present study aims to address a multiobjective distributed assembly flexible job shop scheduling problem with type-2 fuzzy time (DAT2FFJSP), focusing on the optimization objectives of minimizing the makespan and total energy consumption. To address this problem, a mixed-integer linear programming model is presented. Then, a population-based iterative greedy algorithm (PBIGA) with a Q-learning mechanism is proposed, which possesses the following characteristics: 1) a hybrid initialization method is used to generate the population; 2) six local search operators, crossover operators, and mutation operators are applied to explore and exploit the solution space; and 3) the Q-learning mechanism intelligently utilizes historical information on the success of local search operator updates to determine the most suitable perturbation operator; and 4) an energy-saving strategy is applied to improve the candidate solutions. Finally, the effectiveness of the proposed components is validated through extensive experiments that are conducted on 30 instances. The PBIGA outperforms the state-of-the-art algorithms on the DAT2FFJSP.