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<p>Sustainable management of petrochemical supply chains (PCSC) requires a balance of production planning, inventory control, and emissions reduction to minimize waste, reduce costs, and enhance resilience against demand dynamics and raw material availability. This paper presents a Mixed-Integer Linear Programming (MILP) optimization model for a multi-echelon PCSC that integrates production planning, inventory management, and gas emission control to achieve economic efficiency while satisfying embedded environmental constraints that limit flaring and emissions. The model maximizes overall profitability while minimizing environmental impact, incorporating feedforward and reverse logistics to optimize production scheduling, inventory levels, and material flow across the supply chain echelons. The model considers decisions such as production quantities, transportation flows, recycling rates, and emissions control while adhering to constraints, including capacity limits, demand fulfillment, inventory balances, and sustainability regulations. The Coin-OR branch-and-cut (CBC) algorithm is employed to solve this problem, leveraging branch-and-bound and cutting-plane techniques. The model’s effectiveness is validated through a real-world case study of a C4 supply chain. The optimized system achieved a daily profit of $49.56&#xa0;M while eliminating flaring costs of $0.64&#xa0;M, demonstrating how efficient decision-making can improve profitability while reducing environmental impact. A sensitivity analysis also highlights the model’s adaptability to varying operational conditions, reinforcing its value for decision-makers aiming to enhance profitability and environmental responsibility.</p>

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Optimization of a Sustainable Petrochemical Supply Chain: A MILP Framework for Integrated Economic and Environmental Performance

  • 期刊论文

  • 发表日期：

    2026年1月31日

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  • Ahmed M. Attia,
  • Nabeel S. Alanbar,
  • Omar G. Alsawafy,
  • Chairil Akbar

摘要

Sustainable management of petrochemical supply chains (PCSC) requires a balance of production planning, inventory control, and emissions reduction to minimize waste, reduce costs, and enhance resilience against demand dynamics and raw material availability. This paper presents a Mixed-Integer Linear Programming (MILP) optimization model for a multi-echelon PCSC that integrates production planning, inventory management, and gas emission control to achieve economic efficiency while satisfying embedded environmental constraints that limit flaring and emissions. The model maximizes overall profitability while minimizing environmental impact, incorporating feedforward and reverse logistics to optimize production scheduling, inventory levels, and material flow across the supply chain echelons. The model considers decisions such as production quantities, transportation flows, recycling rates, and emissions control while adhering to constraints, including capacity limits, demand fulfillment, inventory balances, and sustainability regulations. The Coin-OR branch-and-cut (CBC) algorithm is employed to solve this problem, leveraging branch-and-bound and cutting-plane techniques. The model’s effectiveness is validated through a real-world case study of a C4 supply chain. The optimized system achieved a daily profit of $49.56 M while eliminating flaring costs of $0.64 M, demonstrating how efficient decision-making can improve profitability while reducing environmental impact. A sensitivity analysis also highlights the model’s adaptability to varying operational conditions, reinforcing its value for decision-makers aiming to enhance profitability and environmental responsibility.