Optimization of domestic inland transportation routes for imported iron ore with the consideration of carbon costs – A case study of central China

Abstract

This paper aims to address the challenges encountered with the iron ore transportation system in China’s Central region, which relies heavily on an overburdened single-mode transport system. To enhance the overall capacity of iron ore transportation, a multimodal transport route optimization solution is proposed, which seeks to minimize transport costs, while considering time window penalties. A BP neural network time series forecasting method is employed to predict the transport demand and a non-dominated sorting genetic algorithm with an elite strategy (NSGA-II) is utilized to optimize the transport routes. The findings indicate that, for improved economic efficiency, a higher proportion of waterway and railway transport in the overall transport plan is recommended. For enhanced timeliness, a greater share of road transport within the scheme is advisable. By comparing these strategies with those that neglect carbon tax costs, this paper underscores the pivotal role of carbon emission costs (CEC) in shaping transport route designs, offering a balanced approach to optimize the multimodal transport network of iron ore transportation to Central China, promoting economic, environmental, and operational sustainability for steel enterprises.