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<p>This study presents a digital twin-integrated Economic Production Quantity (EPQ) optimization framework for real-time decision-making in smart manufacturing. The model features lot-based production planning guided by digital twin feedback on machine availability, defect rates, energy use, and emissions. The profit function includes setup, production, holding, defect, energy, transportation, and emission-related costs, with penalties for exceeding carbon caps and refunds for staying below. Optimization is performed using Sequential Least Squares Quadratic Programming (SLSQP) to determine optimal selling prices, lot sizes, and production durations under operational disruptions. The key novelty lies in embedding carbon refund incentives into a real-time, lot-wise EPQ model powered by cyber-physical feedback, enabling sustainability-aware control under emission constraints. Simulation results confirm resilience–despite 20% machine failures, cumulative profit remains stable at USD&#xa0;160,778.81. Sensitivity analysis highlights production cost and emission rate as primary profit drivers, while ROI analysis shows up to 9% returns from emission refunds. Visual analytics–profit surfaces, heatmaps, and trade-off plots–offer managerial insights for digital twin-based inventory control. This work contributes a scalable and adaptive decision-support system for intelligent, emission-conscious production planning aligned with Industry&#xa0;4.0.</p>

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Digital Twin-Integrated Real-Time EPQ Optimization Under Emission Refund Policies for Smart Manufacturing

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Prabal Das,
  • Nabendu Sen

摘要

This study presents a digital twin-integrated Economic Production Quantity (EPQ) optimization framework for real-time decision-making in smart manufacturing. The model features lot-based production planning guided by digital twin feedback on machine availability, defect rates, energy use, and emissions. The profit function includes setup, production, holding, defect, energy, transportation, and emission-related costs, with penalties for exceeding carbon caps and refunds for staying below. Optimization is performed using Sequential Least Squares Quadratic Programming (SLSQP) to determine optimal selling prices, lot sizes, and production durations under operational disruptions. The key novelty lies in embedding carbon refund incentives into a real-time, lot-wise EPQ model powered by cyber-physical feedback, enabling sustainability-aware control under emission constraints. Simulation results confirm resilience–despite 20% machine failures, cumulative profit remains stable at USD 160,778.81. Sensitivity analysis highlights production cost and emission rate as primary profit drivers, while ROI analysis shows up to 9% returns from emission refunds. Visual analytics–profit surfaces, heatmaps, and trade-off plots–offer managerial insights for digital twin-based inventory control. This work contributes a scalable and adaptive decision-support system for intelligent, emission-conscious production planning aligned with Industry 4.0.