Parallel dynamic NSGA-II with multi-population search for rescheduling of Seru production considering schedule changes under different dynamic events

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Seru production is usually accompanied by various dynamic events in the real world, such as worker tool breakdowns, new batch arrivals, batch cancellations, and batch size changes. Therefore, dynamic Seru production is proposed. To cope with the dynamic seru production, we adopt an event-driven predictive-reactive scheduling method, i.e., when a dynamic event occurs, rescheduling is triggered to produce a new schedule with good efficiency. However, the new schedule may deviate significantly from the previous schedule. Therefore, it is critical to minimize schedule changes while maximizing efficiency (e.g. makespan) simultaneously. First, we formulate a multi-objective dynamic seru production model that applies to various dynamic events to minimize both schedule changes and makespan. Then, we develop dynamic NSGA-II to produce a new schedule. To effectively respond to dynamic events, a population re-initialization strategy is proposed to fast-track non-dominated solutions by making use of the characteristics of dynamic events, and a communication mechanism is designed to accelerate the convergence speed. However, these two mechanisms may lead to reducing its diversity. To maintain population diversity, a multi-population search strategy based on hyper-heuristic is adopted. The hyper-heuristic algorithm is incorporated to evolve an additional seru rescheduling population, which employs NSGA-ΙΙ as the high-level strategy to manipulate several low-level heuristics to generate efficient local search heuristic sequences. However, this approach also results in a longer computation time. Therefore, a parallel mechanism is designed to save computational time. Finally, we have conducted extensive experiments to demonstrate that our algorithm outperforms existing algorithms and provides several management insights.