Robust integrated multi-modal network design for green petroleum distribution under uncertainty

Area of Science:

• Operations Research
• Supply Chain Management
• Environmental Engineering

Background:

• Global petroleum logistics face challenges with reliability and environmental impact.
• Existing models often lack integration of multiple transport modes and uncertainty handling.
• Economic and environmental sustainability are critical for modern supply chains.

Purpose of the Study:

• To develop a comprehensive, robust mathematical model for integrated green multi-modal petroleum logistics planning.
• To synchronize strategic facility development with operational flow allocation under uncertainty.
• To optimize network topology considering both economic and environmental factors.

Main Methods:

• Development of a multi-period mathematical model for network planning.
• Integration of pipeline, rail, and road transportation modes.
• Application of the Augmented Epsilon Constraint method for optimization.
• Validation through a real-world case study in central Iran.

Main Results:

• Targeted rail and pipeline investments reduced total transportation costs and CO₂ emissions by approximately 27.5%.
• The robust model demonstrated 100% feasibility across uncertainty scenarios, outperforming the nominal model (50% feasibility).
• Average cost decreased by 4.7%, average emissions by 30.4%, and uncertainty regret improved by up to 92%.

Conclusions:

• The developed model provides a resilient and effective approach for sustainable and cost-efficient petroleum logistics.
• Integrated planning considering multiple transport modes and uncertainty is crucial for optimizing logistics networks.
• The model supports strategic decisions on infrastructure investment and operational efficiency for greener supply chains.