Rolling Conformal Prediction for Trigger-Aware Dynamic Rescheduling of Battery-Pack Production under Material-Arrival Uncertainty
DOI:
https://doi.org/10.54097/zg03ma82Keywords:
Battery-pack manufacturing, dynamic rescheduling, adaptive conformal inference, distribution shift, multi-objective scheduling, conditional value-at-riskAbstract
Material-arrival uncertainty in battery-pack assembly is not stationary: inbound performance drifts with carrier capacity, seasonal peaks and supplier disruptions, so a production schedule that was well protected last month may be unprotected today. Data-driven scheduling pipelines typically calibrate the arrival-delay model once, offline, and then either freeze the plan or reschedule on a fixed clock. This paper proposes RCDR, a rolling-conformal, trigger-aware rescheduling framework that keeps the uncertainty model calibrated online and uses the calibration state itself as the disruption signal. Conditional median and upper-quantile arrival models are trained under a strict leakage-controlled chronological protocol; adaptive conformal inference updates the protective buffer at every realised arrival; and an exponentially weighted control chart on the conformal miscoverage indicator, combined with a buffer-shift test and an incremental plan-health check, decides when a four-objective frozen-prefix NSGA-II reoptimisation is worth its cost. On 67,138 held-out real inbound records containing genuine distribution shift, a frozen split-conformal bound drifts between 63% and 98% rolling coverage, whereas the rolling calibration holds 90.0% coverage with a worst half-month block coverage of 89.9% and 12.7% lower pinball loss. Across 189 rolling-horizon runs spanning three real logistics regimes, RCDR matches periodic rescheduling on realised weighted tardiness (-1.2%, p = 0.750) while using 32.8% fewer reoptimisations and generating 33.4% less schedule nervousness (both p < 0.001), and it improves on a classical deviation trigger by 13.3% in tardiness (p = 0.005). Calibration monitoring costs 0.28% of optimiser time.
Downloads
References
[1] Koelmel, A., Sauer, A., & Lanza, G. (n.d.). Quality‑oriented production.
[2] Zhang, H. (2024). Graph‑aware multi‑task learning for early prediction of timing violations and routing congestion in digital IC physical design. World Journal of Information Technology, 2(1), 13–20.
[3] Wang, J., Tan, Y., Jiang, B., Wu, B., & Liu, W. (2025). Dynamic marketing uplift modeling: A symmetry‑preserving framework integrating causal forests with deep reinforcement learning for personalized intervention strategies. Symmetry, 17(4), 610.
[4] Zhao, X., Liu, J., Wang, Y., & Wang, J. (2026). CryptoMamba‑SSM: Linear complexity state space models for cryptocurrency volatility prediction. IEEE Open Journal of the Computer Society, 7, 226–243.
[5] Wang, J., Liu, J., Zheng, W., & Ge, Y. (2025). Temporal heterogeneous graph contrastive learning for fraud detection in credit card transactions. IEEE Access.
[6] Sun, T., Yang, J., Li, J., Chen, J., Liu, M., Fan, L., & Wang, X. (2024). Enhancing auto insurance risk evaluation with transformer and SHAP. IEEE Access, 12, 116546–116557.
[7] Zhang, H. (2025). Reinforcement learning approaches for layout optimization in electronic design automation with electromagnetic compatibility constraints. Frontiers in Robotics and Automation, 2(2), 77–93.
[8] Han, X., Yang, Y., Chen, J., Wang, M., & Zhou, M. (2025). Symmetry‑aware credit risk modeling: A deep learning framework exploiting financial data balance and invariance. Symmetry, 17(3), 341.
[9] Chen, J., Cui, Y., Zhang, X., Yang, J., & Zhou, M. (2024). Temporal convolutional network for carbon tax projection: A data‑driven approach. Applied Sciences, 14(20), 9213.
[10] Zhang, C., & Wang, Y. (2024). A data‑driven multi‑objective production scheduling framework for battery pack manufacturing under material arrival uncertainty. AI and Data Science Journal, 1(1), 86–93.
[11] Chen, J., Wang, M., & Sun, T. (2025). Intelligent tax systems and the role of natural language processing in regulatory interpretation. American Journal of Machine Learning, 6(4), 74–94.
[12] Chen, Z., Liu, J., & Chen, J. (2025). Machine learning methods for financial forecasting in enterprise planning: Transitioning from rule‑based models to predictive analytics. Frontiers in Artificial Intelligence Research, 2(3), 541–564.
[13] Chen, J., Liu, J., Liang, Y., & Zhou, M. (2026). KE‑MLLM: A knowledge‑enhanced multi‑sensor learning framework for explainable fake review detection. Applied Sciences, 16(6), 2909.
[14] Yue, X., Yang, J., & Liu, W. (2026). FraudDebate‑Agent: A multi‑agent LLM framework with an evidence‑based debate mechanism for financial statement fraud detection. Mathematics, 14(15), 2695.
[15] Wang, Y., & Zhang, C. (2023). AERIS: Adaptive executor‑scoped resource management for privacy‑preserving data processing in Apache Spark. Eurasia Journal of Science and Technology, 5(2), 25–33.
[16] Yue, X., & Zhu, R. (2024). An explainable hybrid machine learning framework for cash‑flow‑conditioned multi‑horizon liquidity risk early warning: An SME‑oriented benchmark study. World Journal of Management Science, 2(1), 63–72.
[17] Zou, J., Qin, Y., & Rhee, M. (2024). Beyond model scale: A task‑aware reliability benchmark for large language models in clinical decision support. Innovation and Technology Studies, 1(1), 39–47.
[18] Zhang, S., & Qiu, L. (2023). An interpretable, uncertainty‑aware framework for bridge deterioration prediction and risk‑based maintenance prioritization. Academic Journal of Architecture and Civil Engineering, 1(2), 21–28.
[19] Jin, J., Xing, S., Ji, E., & Liu, W. (2025). Xgate: Explainable reinforcement learning for transparent and trustworthy API traffic management in IoT sensor networks. Sensors, 25(7), 2183.
[20] Xing, S., Wang, Y., & Liu, W. (2025). Multi‑dimensional anomaly detection and fault localization in microservice architectures: A dual‑channel deep learning approach with causal inference for intelligent sensing. Sensors, 25(11), 3396.
[21] Xing, S., & Wang, Y. (2025). Proactive data placement in heterogeneous storage systems via predictive multi‑objective reinforcement learning. IEEE Access, 13, 117986–117998.
[22] Ji, E., Wang, Y., Xing, S., & Jin, J. (2025). Hierarchical reinforcement learning for energy‑efficient API traffic optimization in large‑scale advertising systems. IEEE Access.
[23] Xing, S., & Wang, Y. (2025). Cross‑modal attention networks for multi‑modal anomaly detection in system software. IEEE Open Journal of the Computer Society.
[24] Liu, Y., Ren, S., Wang, X., & Zhou, M. (2024). Temporal logical attention network for log‑based anomaly detection in distributed systems. Sensors, 24(24), 7949.
[25] Ren, S., Jin, J., Niu, G., & Liu, Y. (2025). ARCS: Adaptive reinforcement learning framework for automated cybersecurity incident response strategy optimization. Applied Sciences, 15(2), 951.
[26] Li, P., Ren, S., Zhang, Q., Wang, X., & Liu, Y. (2024). Think4SCND: Reinforcement learning with thinking model for dynamic supply chain network design. IEEE Access, 12, 195974–195985.
[27] Hu, X., Zhao, X., Wang, J., & Yang, Y. (2025). Information‑theoretic multi‑scale geometric pre‑training for enhanced molecular property prediction. PLOS One, 20(10), e0332640, [https://doi.org/10.1371/journal.pone.0332640](https://doi.org/10.1371/journal.pone.0332640).
[28] Wang, M., Zhang, X., Yang, Y., & Wang, J. (2025). Explainable machine learning in risk management: Balancing accuracy and interpretability. Journal of Financial Risk Management, 14(3), 185–198.
[29] Yang, J., Li, P., Cui, Y., Han, X., & Zhou, M. (2025). Multi‑sensor temporal fusion transformer for stock performance prediction: An adaptive Sharpe ratio approach. Sensors, 25(3), 976.
[30] Zhao, X., Sun, T., Ren, S., Yang, J., & Liu, Y. (2025). RAG‑based AI agents for enterprise software development: Implementation patterns and production deployment. Frontiers in Artificial Intelligence Research, 2(3), 501–520.
[31] Sun, T., Wang, M., & Chen, J. (2025). Leveraging machine learning for tax fraud detection and risk scoring in corporate filings. Asian Business Research Journal, 10(11), 1–13.
[32] Bian, M., Wang, Y., & Lin, Y. (2024). Explainable supplier risk prediction for critical telecommunications infrastructure procurement: An empirical XGBoost–SHAP framework. Social Science and Management, 1(1), 64–77.
[33] Rhee, M., Zou, J., Mo, T., Teng, D., & Yang, J. S. (2026). Enhancing web search agents with self‑play contrastive fine‑tuning. IEEE Access.
[34] Wang, B., Zhao, W., & Wang, Z. (2024). DAS‑GNN: A scalable disagreement‑aware graph neural framework for anomaly detection in large‑scale networks. AI and Data Science Journal, 1(1), 67–75.
[35] Wang, Z., & Shen, Z. (2024). Flex‑Talent: An explainable and fair machine learning framework for high‑stakes leadership‑pipeline evaluation. World Journal of Management Science, 2(1), 73–82.
[36] Guo, Z., Chen, T., & Shang, W. (2022). Class‑conditional intermediate‑domain adversarial adaptation for cross‑device acoustic scene classification. Journal of Computer Science and Electrical Engineering, 4(1), 26–35.
[37] Wang, Y., Bian, M., & Lin, Y. (2022). An explainable temporal graph neural network for disruption prediction and risk propagation in multi‑tier supply chains. Journal of Computer Science and Electrical Engineering, 4(1), 17–25.
[38] Zhao, W., & Wang, B. (2022). CAPA: A prediction‑driven autoscaling framework for SLO‑aware cloud‑native machine learning inference. Journal of Computer Science and Electrical Engineering, 4(1), 8–16.
[39] Jiao, Y., & Liang, Y. (2024). An integrated process‑mining and explainable machine‑learning framework for bottleneck and internal‑control risk detection in the multi‑entity financial close. Social Science and Management, 1(3), 96–107.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Advanced Engineering and Technology Research
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
