A Tri-Stage Cooperative Optimization Algorithm With Q-Learning Mechanism for the Multiobjective Distributed Flexible Job Shop Scheduling With Worker Factors

The production process of aluminum profiles is a typical distributed flexible job shop environment. The scheduling problem in distributed systems with worker factors is a complex combinatorial optimization problem. In this article, the multiobjective distributed flexible job shop scheduling with worker factors (MO-DFJSPWF), including proficiency and the learning–forgetting effect, is studied to minimize makespan and total resource load (TRL). A mixed-integer linear programming (MILP) model is established according to the degree of proficiency and the rate of learning–forgetting effect of the workers. A tri-stage cooperative optimization algorithm (TSCOA) is designed for the MO-DFJSPWF. First, a knowledge-based initialization method considering worker proficiency, machine load, and job priority is proposed to generate the initial population of the problem. Second, a bi-population cooperative strategy with a dynamic adaptive search strategy (DASS) is developed to balance the convergence speed and candidate diversity of the algorithm. Eight perturbation operators in the second and third stages are introduced to explore and exploit the solution space of the TSCOA. Third, the perturbation operators are selected dynamically via the Q-learning mechanism by leveraging the historical performance data of local search operators. The effectiveness and efficiency of the TSCOA are tested on a benchmark test suite. The experimental results indicated that the performance of the TSCOA outperforms certain state-of-the-art algorithms in solving MO-DFJSPWF.