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<p>This study develops a dynamic, interdisciplinary inventory optimization framework for modular electronics grounded in the 6R sustainability principles–<i>Reduce, Reuse, Remanufacture, Refurbish, Recycle, and Recover</i>. The proposed model integrates engineering-based production optimization with environmental and policy dimensions of the circular economy to promote resource-efficient and low-emission manufacturing. Inventory dynamics are represented through differential equations across production and consumption phases, ensuring system continuity and zero terminal inventory. The framework jointly optimizes production quantity, recovery flow rates, and phase durations to minimize total cost, encompassing procurement, setup, holding, production, and sustainability-oriented expenditures. A Genetic Algorithm (GA) is employed to solve the nonlinear optimization problem, demonstrating stable convergence and robust exploration. Numerical analysis yields a minimum total cost of INR&#xa0;24,240.16, with cost savings primarily from reuse (23.9%), remanufacturing (7.5%), and refurbishing (4.8%), while recovery contributes an additional 1.6%. Sensitivity analysis confirms model robustness across key operational parameters. The model is extended to modular laptops to assess scalability, revealing similar optimality patterns despite greater production complexity. Comparative benchmarking against traditional Economic Production Quantity (EPQ) and partial 3R systems indicates that the 6R framework achieves over 23% cost reduction and a higher return on investment. By bridging operational decision science with sustainability policy, the study provides a mathematically rigorous yet practically implementable decision-support model for sustainable electronics manufacturing, aligning with Extended Producer Responsibility (EPR), net-zero production targets, and the broader transition toward a circular economy.</p>

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A 6R-Based Inventory Optimization Framework for Modular Electronics: Benchmarking Remanufacturing and Recovery Strategies in a Circular Economy Context

  • 期刊论文

  • 发表日期：

    2026年3月27日

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  • Prabal Das,
  • Nabendu Sen,
  • Nilanjana Chakrabarty,
  • Amit Kumar Das

摘要

This study develops a dynamic, interdisciplinary inventory optimization framework for modular electronics grounded in the 6R sustainability principles–Reduce, Reuse, Remanufacture, Refurbish, Recycle, and Recover. The proposed model integrates engineering-based production optimization with environmental and policy dimensions of the circular economy to promote resource-efficient and low-emission manufacturing. Inventory dynamics are represented through differential equations across production and consumption phases, ensuring system continuity and zero terminal inventory. The framework jointly optimizes production quantity, recovery flow rates, and phase durations to minimize total cost, encompassing procurement, setup, holding, production, and sustainability-oriented expenditures. A Genetic Algorithm (GA) is employed to solve the nonlinear optimization problem, demonstrating stable convergence and robust exploration. Numerical analysis yields a minimum total cost of INR 24,240.16, with cost savings primarily from reuse (23.9%), remanufacturing (7.5%), and refurbishing (4.8%), while recovery contributes an additional 1.6%. Sensitivity analysis confirms model robustness across key operational parameters. The model is extended to modular laptops to assess scalability, revealing similar optimality patterns despite greater production complexity. Comparative benchmarking against traditional Economic Production Quantity (EPQ) and partial 3R systems indicates that the 6R framework achieves over 23% cost reduction and a higher return on investment. By bridging operational decision science with sustainability policy, the study provides a mathematically rigorous yet practically implementable decision-support model for sustainable electronics manufacturing, aligning with Extended Producer Responsibility (EPR), net-zero production targets, and the broader transition toward a circular economy.