Multi-item and small-lot-size production modes lead to frequent setup, which involves significant setup times and has a substantial impact on productivity. In this study, we investigated the optimal flexible job-shop scheduling problem with a sequence-dependent setup time. We built a mathematical model with the optimal objective of minimization of the maximum completion time (makespan). Considering the process sequence, which is influenced by setup time, processing time, and machine load limitations, first, processing machinery is chosen based on machine load and processing time, and then processing tasks are scheduled based on setup time and processing time. An improved quantum cat swarm optimization (QCSO) algorithm is proposed to solve the problem, a quantum coding method is introduced, the quantum bit (Q-bit) and cat swarm algorithm (CSO) are combined, and the cats are iteratively updated by quantum rotation angle position; then, the dynamic mixture ratio (MR) value is selected according to the number of algorithm iterations. The use of this method expands our understanding of space and increases operation efficiency and speed. Finally, the improved QCSO algorithm and parallel genetic algorithm (PGA) are compared through simulation experiments. The results show that the improved QCSO algorithm has better results, and the robustness of the algorithm is improved.

High-end manufacturing supply chains are increasingly exposed to disruption risks and environmental uncertainty, yet how Industrial Internet (II) empowerment builds supply chain resilience (SCR) and when such benefits are most pronounced remain unclear. Grounded in the resource-based view and ambidextrous innovation logic, this study investigates whether II empowerment—captured by connectivity capability (CC), integration capability (IC), and analytics capability (AC)—enhances SCR through supply chain collaborative innovation (SCCI), including supply chain breakthrough innovation (SCBI) and supply chain incremental innovation (SCII), and whether environmental uncertainty (EU) conditions these relationships. Survey data from 293 Chinese high-end manufacturing firms were analyzed using structural equation modeling and bootstrapped mediation tests, supplemented by moderated regression analysis. The results indicate that CC, IC, and AC all directly and positively affect SCR. CC and AC significantly promote SCBI, whereas the effect of IC on SCBI is not significant; meanwhile, CC, IC, and AC all significantly foster SCII. Both SCBI and SCII are positively associated with SCR. SCBI mediates the effects of CC and AC (but not IC) on SCR, while SCII mediates the effects of all three II dimensions. Furthermore, EU strengthens the impacts of CC, AC, SCBI, and SCII on SCR, whereas the IC × EU interaction is not significant. These findings clarify the innovation-based mechanisms and boundary conditions of II-enabled resilience and offer actionable implications for high-end manufacturers seeking resilient supply chains under uncertainty.