Integrated optimization of railway freight operation planning and pricing based on carbon emission reduction policies

Cargo transportation is one of the major sources of carbon emissions. To reduce carbon emissions from inland freight, one way is to encourage rail transportation instead of truck transportation. In this paper, a new approach combining railway operation management, revenue management, and policy formulation is proposed to increase the market share of railway freight and reduce carbon emissions. Through the integrated optimization of operation planning and pricing, the competitiveness of railway freight services is improved. The carbon tax policy acting on the demand-side can affect consigners’ choice behavior. The low-carbon subsidy policy acting on the supply-side can encourage rail transport. Then a bi-level multi-objective model is proposed to achieve the trade-off among the three stakeholders (the government, rail service operators, and consigners). The complexity of the bi-level multi-objective problem motivates the development of a hybrid optimization algorithm that combines the Nondominated sorting genetic algorithm III (NSGA-III) and the Descent algorithm based on sensitivity analysis (SAB). Through a real case based on a rail service network with nine nodes in China, the rationality of the proposed methodology is verified. The results show that the approach proposed can achieve a 39.27% modal shift (MS) from road to rail, as well as a 37.09% carbon emission reduction.