本研究旨在結合低程式碼平台與數學最佳化技術，開發一套應用於生技製藥產業的雲端排程系統。過去企業多仰賴紙本作業或市售套裝軟體進行排程管理，惟此類系統在高變異、高客製化場域中導入與維護成本高，亦難以彈性應對現場變動。本研究參考JJC（Jidoka-JIT Cycle）理念，導入低程式碼工具強化資訊流可視性與排程回饋機制，以實現簡易智慧化。以某微生物發酵代工廠為實證對象，運用Microsoft Power Platform（包含Power Apps、Power Automate、SharePoint與Power BI）建構具雲端性、即時性與可視化的排程架構，支援現場作業回報與異常顯示。透過iSM（Information Stream Mapping）資訊流程分析與五項指標進行量化評估，結果顯示資訊傳遞自動化與集中性大幅提升，其中排程作業的Scheduling Lead Time（含製程前準備、工單建立與排程核對等流程）由120分鐘縮短至30分鐘，顯著改善決策回應速度與跨部門溝通效率。本研究進一步針對具「齊料」特性的益生菌產品建構MILP（Mixed-Integer Linear Programming）模型，以最小化總完工時間（makespan）為目標，考量設備資源與工時差異進行求解。實驗結果顯示優化排序可使總完工時間縮短約10%，平均閒置時間亦下降約33%，顯著提升資源使用效率與整體排程效能。綜合上述，本研究提出一套融合低程式碼系統建構與最佳化模型的排程解決方案，經過10個月現場驗證，具備彈性、高應用性與實務導入價值。

This study presents a cloud-based scheduling system for the biopharmaceutical industry by integrating low-code platforms with mathematical optimization. Traditional paper-based operations or commercial scheduling software often involve high implementation and maintenance costs, and lack flexibility in high-variability, highly customized environments. Adopting the Jidoka-JIT Cycle (JJC) concept, the system was developed on Microsoft Power Platform (Power Apps, Power Automate, SharePoint, Power BI) to provide real-time progress reporting, abnormality alerts, and enhanced information transparency.
Using a microbial fermentation contract manufacturing plant as a case study, the system was evaluated through Information Stream Mapping (iSM) and five key performance indicators. Results show that automation and centralization improved significantly, while scheduling lead time—including preprocessing, work order creation, and verification—was reduced from 120 to 30 minutes, greatly improving decision response time and cross-departmental communication. Furthermore, a Mixed-Integer Linear Programming (MILP) model was developed to address the “simultaneous readiness” requirement for multi-strain probiotic products, minimizing total completion time under equipment constraints and process time differences. Experimental results indicate reductions of ~10% in total completion time and ~33% in average idle time. A 10-month field validation confirms the system’s flexibility, applicability, and practical deployment value.

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