A hybrid NSGA-II for energy-efficient flexible job shop scheduling problem with finite multi-load AGV resources

Existing research on flexible job shop scheduling (FJSP) often overlooks critical logistics constraints, particularly the limited availability and multi-load capacity of Automated Guided Vehicles (AGVs), resulting in a significant disconnect between theoretical models and the operational realities of service-oriented smart manufacturing systems. To bridge this gap, this study proposes a Non-dominated Sorting Genetic Algorithm II with Local Search Algorithm (NSGA-II_LS) for green flexible job shops with multi-load AGV coordination (GFJSP-MA), which integrates autonomous optimisation of makespan, machine utilisation, and carbon emissions through intelligent knowledge integration and dynamic operational adaptation. The three core innovations aligned with autonomous manufacturing system design are as follows: (1) Mining the domain knowledge of GFJSP-MA and designing a novel encoding and decoding method; (2) adaptively improving the genetic operators to enhance the global exploration capability; (3) developing eight local search operators based on the problem characteristics to enhance the convergence of the algorithm. This study was tested and validated on a generalised dataset and compared with three other multi-objective algorithms, and the experimental results verified the superiority of NSGA-II_LS for solving GFJSP-MA.