A Dual-Loop Multi-Strategy Migrating Birds Optimization Algorithm for Flexible Job Shop Scheduling in Power Grid Inspection Bases

To address the challenges posed by the structural complexity and frequent resource conflicts in flexible job shop scheduling (FJSP) at practical power grid inspection bases, this paper formulates a makespan-minimization optimization model and proposes a dual-loop multi-strategy migrating birds optimization algorithm (DMMBO). The algorithm first generates a high-quality initial population by integrating the MOR operation priority rule with a weighted shortest processing time (SPT). Then, a four-phase perturbation mechanism for leader birds is designed to accelerate convergence toward optimal solution. To handle the critical path structure, four targeted strategies—time window relaxation, entropy-driven migration, path topology reconfiguration, and temporal shifting of key operations—are introduced to effectively alleviate bottlenecks. Within the algorithmic framework, DMMBO adopts a dual-loop optimization structure: the inner loop conducts local search based on simulated annealing criteria and an adaptive perturbation acceptance mechanism to enhance convergence accuracy, while the outer loop incorporates global perturbations and a restart strategy to maintain population diversity and prevent premature convergence. To evaluate the effectiveness of the proposed DMMBO algorithm, simulation experiments were conducted on both real-world dry-type transformer inspection tasks and Brandimarte instances. The results indicate that DMMBO outperforms other comparative algorithms in terms of solution quality and result stability, demonstrating its practical value in solving complex power grid scheduling problems.