Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization

• Manufacturing companies are facing the emergent challenges to meet the demand of sustainable manufacturing.
• Energy-efficient dynamic scheduling is a NP-hard problem presented in manufacturing systems.
• A novel particle swarm optimization algorithm based on Hill function is presented to minimize makespan and energy consumption.
• The relationship between makespan and energy consumption is conflicting.
• The results show that the proposed algorithm outperforms the behavior of state of the art algorithms.

Due to increasing energy requirements and associated environmental impacts, nowadays manufacturing companies are facing the emergent challenges to meet the demand of sustainable manufacturing. Most existing research on reducing energy consumption in production scheduling problems has focused on static scheduling models. However, there exist many unexpected disruptions like new job arrivals and machine breakdown in a real-world production scheduling. In this paper, it is proposed an approach to address the dynamic scheduling problem reducing energy consumption and makespan for a flexible flow shop scheduling. Since the problem is strongly NP-hard, a novel algorithm based on an improved particle swarm optimization is adopted to search for the Pareto optimal solution in dynamic flexible flow shop scheduling problems. Finally, numerical experiments are carried out to evaluate the performance and efficiency of the proposed approach.