Multi-Objective Optimization for Refined Oil Resource Allocation: Towards Energy and Carbon Saving

In light of the ambitious “dual carbon” targets, the refined oil supply chain faces challenges in balancing economic viability with environmental sustainability. Traditional resource allocation methods predominantly prioritize cost minimization, often overlooking significant environmental impacts and leading to carbon-intensive transportation practices. This paper proposes a multi-objective optimization model to simultaneously minimize total logistics costs and carbon emissions across the entire refined oil supply chain. The model encompasses key stages, including refinery production, external procurement, multimodal transport operations, and inventory management. The proposed framework integrates practical con straints such as sending and receiving capacities, inventory balance, and supply and demand requirements. The ε-constraint method is employed for model solution to generate a set of Pareto optimal solutions, highlighting the inherent trade-offs between economic and environmental objectives. A case study is carried out, involving a refined oil logistics system in Central China, which comprises five refineries, 31 depots, and two external purchasing nodes. Compared to a purely economic optimization, a balanced scenario (e.g., with an ε-constraint of 9000 tons/season for carbon emissions) achieves a substantial 10–15% reduction in emissions with only a marginal 1–2% increase in logistics costs. Furthermore, the optimization significantly reconfigures the transport structure, increasing pipeline utilization from 27.3% to 35% and leading to a 26.1% reduction in waterway-related carbon emissions. This study can offer an efficient decision-making tool that facilitates the green transformation of the refined oil supply chain, bridging the gap between corporate logistics cost efficiency and ambitious carbon neutrality targets.