A Novel Multi-Objective Carbon-Efficiency Optimization Algorithm for Oil and Gas Supply Chains with Comparative Analysis and Graph-Based Performance Evaluation

Abstract

The oil and gas sector faces increasing pressure to simultaneously improve operational efficiency and reduce carbon emissions across complex supply chain networks. This study proposes a novel multi-objective optimization algorithm, the CarbonEfficiency Adaptive Optimization Algorithm (CEAOA), designed to jointly minimize total logistics cost and lifecycle carbon emissions while maintaining service reliability. The algorithm integrates adaptive weighting, dynamic constraint handling, and a hybrid search mechanism that combines evolutionary strategies with gradient-based refinement to improve convergence toward Pareto-optimal solutions. A comprehensive computational framework is developed to model upstream, midstream, and downstream operations using real-world-inspired network structures and stochastic demand profiles. The performance of CEAOA is evaluated against five established optimization techniques, including the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Strength Pareto Evolutionary Algorithm 2 (SPEA2), classical Genetic Algorithm (GA), and Linear Programming (LP). Results are presented through graph-based performance analysis, including Pareto front comparisons, convergence plots, emission-cost trade-off curves, and sensitivity analyses under varying carbon pricing scenarios. The findings demonstrate that CEAOA achieves superior solution diversity, faster convergence rates, and up to 18 percent improvement in emission-cost efficiency compared to benchmark models. The study highlights the practical applicability of the proposed approach for decision-makers seeking to balance sustainability goals with operational performance in carbon-constrained environments, offering a scalable and data-driven pathway toward greener supply chain optimization in the oil and gas industry.