Scheduling for Smart Manufacturing Systems Under Transportation and Energy Management Constraints

The flexible job shop scheduling problem with automated guided vehicles (FJSP–AGV) couples production and transport decisions, making scheduling and energy management computationally challenging. Conventional genetic algorithms apply a single decoder throughout the search and thus cannot adapt when instance characteristics or battery constraints change. We propose a portfolio–island decoding genetic algorithm (PID-NSGA-II) that shifts the focus from modifying evolutionary operators to learning which decoding strategies work best. Five heterogeneous decoders run in parallel on separate islands, and an upper-confidence-bound multi-armed bandit measures each island’s contribution to makespan improvement and adaptively reallocates population resources, automatically balancing exploration and exploitation. The framework is tested under two settings—pure makespan minimization and energy-aware scheduling with AGV battery considerations. Experiments on benchmark datasets show that PID-NSGA-II consistently improves solution quality and stability compared with single-decoder genetic algorithms, with greater gains when energy constraints are present. Adaptive learning of decoders delivers more robust scheduling decisions for complex FJSP-AGV environments and provides a scalable platform for smart manufacturing applications, achieving up to 25 % makespan reduction and substantial improvements in AGV battery levels across small, medium and large problem instances.