A Reliability- and Energy-Aware Decision-Support Framework for Production–Maintenance Scheduling in Parallel CNC Machining Systems

In parallel CNC machining systems, machine deterioration can simultaneously increase energy-related operating costs, affect delivery performance, and change the timing of preventive maintenance. This study develops a reliability- and energy-aware decision-support framework for production-maintenance scheduling in a two-machine parallel CNC cell. The model integrates priority sequencing, reliability-based machine assignment during decoding, degradation-dependent energy cost, tardiness penalties, and threshold-triggered preventive maintenance in a unified cost-minimization formulation. A normalized reliability index is updated by a short-horizon exponential degradation function, and the energy term is amplified when machines operate in degraded states. Preventive maintenance is triggered when post-job reliability falls below a specified threshold, restoring the machine’s condition for subsequent production or the next planning horizon. The computational study combines application-inspired machining instances, decoder-space full enumeration for small cases, repeated GA/PSO comparisons, a new algorithm-budget sensitivity experiment, and adapted OR-Library weighted-tardiness benchmarks. Across 270 paired budget-sensitivity runs, GA obtained a lower total cost in 265 cases, whereas PSO retained a shorter average runtime in the matched-budget experiments. Across 90 adapted public-benchmark comparisons, GA obtained a lower total cost in 86 cases. These results show that the framework generates feasible schedules and reveals energy–maintenance–tardiness trade-offs. The algorithmic findings are interpreted as a quality–time trade-off under the tested scalarized cost model, not as a claim of universal algorithmic superiority.