Dual objective multimodal transportation path optimization based on different carbon tax mechanisms under uncertain demand

By delving into low-carbon transportation research, we can address the imperative for the high-quality development of transportation services, while simultaneously advancing the realization of the dual-carbon objective. This study focuses on optimizing multimodal transport routes under varying carbon tax frameworks, taking into account the demand uncertainty that arises from unforeseen events such as abrupt restocking or seasonal fluctuations. We formulate a dual-objective 0-1 path optimization model under both a unified carbon tax mechanism and a piecewise progressive carbon tax scheme. The model aims to minimize total cost and carbon emissions in the face of stochastic demand. Utilizing Monte Carlo simulation and the laws of large numbers, we convert the model to maximize the expected value of the uncertain objective. An enhanced non-dominated sorting genetic algorithm is then developed to solve this model, yielding solutions that more effectively meet our objectives. This algorithm is designed to expand the search space, mitigating the “"premature convergence” issue and thereby generating superior individuals and solutions. Finally, we assess the applicability of our model and algorithm to transportation challenges within the context of the dual-carbon initiative through a numerical example. We also explore the influence of different carbon tax mechanisms on total cost and emissions, as well as their applicability and efficacy in the face of demand uncerainty. The findings indicate that companies can achieve emission reductions with minimal cost increases under dual-target cost scenarios, ideal for dual carbon transportation contexts. Moreover, carbon tax rates significantly impact emission control, with segmented progressive taxes proving more effective, especially in high-demand uncertainty. Decision-makers should consider technological capabilities to set optimal tax rates and thresholds, fostering corporate enthusiasm. This research informs policy and decision-making for authorities and firms.