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<p>Closed-loop supply chains (CLSCs) face significant challenges in multi-period production planning due to uncertainties in demand and product returns. While existing research has explored pricing and production decisions in CLSCs, few studies address dynamic cost-plus pricing strategies that adapt to fluctuating market conditions across multiple products and periods. This study develops a novel optimization model for multi-product CLSC production planning by integrating a dynamic cost-plus pricing strategy which adjusts prices based on real-time cost and demand variations with a Lagrange relaxation approach to balance manufacturing and remanufacturing operations under uncertainty. Numerical results demonstrate the model’s effectiveness, showing 18–22% improved profitability over static pricing approaches, enhanced waste reduction through optimal reintegration of returned products, and sustained operational flexibility under ±30% demand/return rate fluctuations. Theoretically, this work establishes a new framework for joint pricing-production optimization in stochastic CLSCs, while practical implementations reveal how manufacturers can achieve sustainable operations without sacrificing profitability.</p>

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Multi-period multi-product production planning in closed-loop supply chain system under demand and return uncertainty: a cost-plus pricing strategy

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Yunusa Aliyu Hadejia,
  • Sani Rabiu,
  • Majid Khan Majahar Ali

摘要

Closed-loop supply chains (CLSCs) face significant challenges in multi-period production planning due to uncertainties in demand and product returns. While existing research has explored pricing and production decisions in CLSCs, few studies address dynamic cost-plus pricing strategies that adapt to fluctuating market conditions across multiple products and periods. This study develops a novel optimization model for multi-product CLSC production planning by integrating a dynamic cost-plus pricing strategy which adjusts prices based on real-time cost and demand variations with a Lagrange relaxation approach to balance manufacturing and remanufacturing operations under uncertainty. Numerical results demonstrate the model’s effectiveness, showing 18–22% improved profitability over static pricing approaches, enhanced waste reduction through optimal reintegration of returned products, and sustained operational flexibility under ±30% demand/return rate fluctuations. Theoretically, this work establishes a new framework for joint pricing-production optimization in stochastic CLSCs, while practical implementations reveal how manufacturers can achieve sustainable operations without sacrificing profitability.