An Iterative Greedy Algorithm for Solving a Multiobjective Distributed Assembly Flexible Job Shop Scheduling Problem With Fuzzy Processing Time

Deterministic processing time are no longer applicable under realistic circumstances because of the uncertainties involved in manufacturing and production processes.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.