Optimizing Multimodal Logistics in Petroleum Supply Chains Using Linear Goal Programming: A Case Study on South Pars Gas Field Development

The optimization of transportation logistics is a critical challenge in industries with complex supply chain frameworks, such as the oil and gas petroleum sector. This study applies Linear Goal Programming (LGP) to address the multidimensional transportation planning challenges associated with the South Pars Gas Field Development (SPGFD) in Iran. The proposed model incorporates conflicting objectives, including minimizing transportation costs, maximizing refinery capacity utilization, and fulfilling depot demands under logistical and operational constraints. A multi-objective optimization framework was developed to balance cost efficiency, supply chain reliability, and operational goals. Furthermore, sensitivity analyses were conducted to evaluate the model’s robustness under fluctuating transportation costs, varying refinery outputs, and dynamic demand conditions.The results demonstrate the LGP model’s effectiveness, achieving a 16% reduction in transportation costs compared to traditional methods, while ensuring full compliance with refinery output capacities and depot demands. In crisis-mode simulations, characterized by reduced refinery outputs and increased costs, the model maintained 98% demand fulfillment and achieved a 9% cost saving, underscoring its resilience and adaptability. The study highlights the potential of LGP to optimize supply chain operations in dynamic and uncertain environments, offering a scalable framework applicable to other industries.