Energy-Aware Scheduling in Flow Shops: a Novel Artificial Neural Network-Driven Multi-Objective Optimization

Abstract

Group technology is a managerial strategy used to optimize production by reducing setup times, lead times, and work-in-process inventories. Research on flow-shop sequence-dependent group scheduling problems (FSDGSPs) has primarily focused on minimizing makespan and total flow time to improve efficiency. However, the need for energy-efficient scheduling in FSDGSPs remains underexplored despite increasing sustainability concerns. To address this, the energy-efficient flow-shop sequence-dependent group scheduling problem (EEFSDGSP) is introduced. A novel multi-objective optimization (MOO) technique, the artificial neural network-based multi-objective genetic algorithm (ANN-MOGA), is proposed to minimize makespan and energy consumption in EEFSDGSP. ANN-MOGA advances MOO by using a neural network to evaluate fitness and guide selection, reducing computational complexity versus traditional methods like NSGA-II and SPEA2. A post-processing step (PPANNS) further enhances solution diversity and distribution. Results show ANN-MOGA, especially with PPANNS, outperforms NSGA-II and competes effectively with SPEA2 in larger problem instances.