Cable manufacturing is a typical hybrid production system characterized by the deep coupling of continuous processes and discrete logic. However, the unique “Reel-Splitting Constraint”—where continuous cables must be segmented into strictly sequenced sub-reels—along with high energy consumption and frequent dynamic disturbances render traditional Hybrid Flexible Flow Shop scheduling models ineffective in this context. To address these challenges, this paper proposes a novel Multi-Objective Dynamic Scheduling Framework tailored for the cable industry. First, a mathematical model is constructed that explicitly formalizes the rigid logic of sub-reel sequencing and continuous material flow, aiming to simultaneously minimize total energy consumption, makespan, and changeover times. Unlike generic models, this formulation introduces a constraint-handling mechanism to ensure the physical continuity of sub-reels during optimization. Second, a two-stage hybrid swarm intelligence algorithm is developed to solve this NP-hard problem. An improved Ant Colony Optimization (ACO) algorithm is employed for “population seeding” to generate feasible initial schedules and avoid deadlocks, while a Variable Neighborhood Search (VNS) executes deep evolutionary operations—such as setup reduction and critical operation insertion—to escape local optima. Case studies based on real-world industrial data demonstrate the superior performance of the proposed method. The hybrid strategy reduces the makespan by approximately 9.8% compared to traditional approaches and effectively mitigates energy waste in bottleneck processes. Furthermore, the proposed event-driven dynamic rescheduling mechanism exhibits exceptional responsiveness, reducing rescheduling time for unexpected equipment breakdowns from 18 h to 0.83 h, thereby enabling within-shift decision-making and robust operation in volatile manufacturing environments.
Zhu et al. (Fri,) studied this question.