Abstract
Material handling in special cable manufacturing remains highly inefficient, with manual logistics accounting for nearly 90% of product cycle time. Existing scheduling methods commonly rely on oversimplified assumptions and fail to integrate machine processing with autonomous mobile robot (AMR) transportation constraints, limiting practical applicability. This study proposes a comprehensive scheduling framework that explicitly incorporates AMR movement dynamics—covering empty-load travel and loaded transportation—into flexible job shop scheduling. A dual-objective model is formulated to minimize makespan and total equipment load, providing a more realistic evaluation of workshop performance. To solve this model, an enhanced Sparrow Search Algorithm (SSA) is developed, featuring Pareto dominance sorting, harmonic mean crowding, an external elite archive, and adaptive discoverer–follower scaling to improve convergence stability and avoid premature stagnation. Using real production data from a cable workshop, the proposed method achieves a 15.0% reduction in completion time and a 36.3% reduction in equipment load compared with the traditional SSA. The results demonstrate that the integrated model and improved algorithm offer an effective solution for AMR-constrained multi-objective workshop scheduling.