A multi-objective optimization algorithm for flow shop group scheduling problem with sequence dependent setup time and worker learning

The optimization of production systems has become increasingly important in manufacturing industries due to the growing competition and market demands. One of the overriding concerns of managers is the efficient exploitation of workers' learning to increase output and decrease downtime. The learning effect symbolizes the improvement of workers' ability and performance through the repetition of similar jobs. On the other hand, it is a critical requirement for decision-makers to have effective management of the transportation phase to achieve an optimal production plan. This paper considers a flow shop sequence-dependent group scheduling problem (FSDGS) with a learning effect to minimize two contradictory objective functions, namely makespan and energy consumption. A mixed-integer linear programming model is proposed to find optimal jobs, group schedules, and appropriate production and transportation speeds to enhance the overall performance of the system. Due to the complexity of the planning process, we propose lower bounds and an efficient resolution method based on multi-objective simulated annealing metaheuristic (MOSA), enhanced by a local search procedure to tackle this problem. The proposed method is evaluated through several experiments based on a real case study, using different learning rates and setup time ratio levels. The obtained results demonstrate the effectiveness of the algorithm in improving the performance of production systems by reducing processing time and energy consumption. These findings have significant implications for the design and optimization of production systems in manufacturing industries.