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

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Proposed NSGA-II method optimizes makespan and carbon emissions in job shop scheduling with AGVs, indicating improved operational efficiency.

The NSGA-II_LS algorithm significantly reduces makespan and carbon emissions in energy-efficient job shop scheduling.
Experimental validation on a generalized dataset showed that NSGA-II_LS outperformed three other multi-objective algorithms.
The approach integrates adaptive genetic operators to improve global exploration and problem-solving efficiency.
Innovative local search operators tailored to problem characteristics enhanced algorithm convergence.

Shi et al. (2025) studied this question.

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Proposed NSGA-II method optimizes makespan and carbon emissions in job shop scheduling with AGVs, indicating improved operational efficiency.