A Hybrid Pareto-Based Tabu Search for the Distributed Flexible Job Shop Scheduling Problem With E/T Criteria

During recent years, distributed manufacturing optimization problems have been researched and applied in many fields, such as steelmaking system and textile production process. To solve the multi-objective distributed flexible job shop scheduling problem, a hybrid Pareto-based tabu search algorithm (HPTSA) is investigated to minimize four objectives simultaneously, i.e., the makespan, the maximal workload, the total workload, and the earliness/tardiness (E/T) criteria. In the proposed algorithm, several approaches considering both the problem characteristics and the objective features are used to initialize the group of solutions. Then, five types of neighborhood structures that consider both problem structures are developed to enhance the exploitation and exploration capabilities. In addition, a well-designed backward method is proposed to optimize the E/T criteria. Based on the realistic production data in the steelmaking system, several instances with different problem scales are randomly generated. Four efficient multi-objective optimization algorithms are selected to make detailed comparisons with the proposed HPTSA algorithm. After detailed tests on the realistic instances, the experimental comparison results show that the proposed algorithm shows competitive performance compared with the selected efficient algorithms.