Research on green multimodal transportation path optimization based on distributionally robust chance constraints with uncertain demand

Multimodal container transportation is an effective method for reducing carbon footprint levels. Considering the scenario of uncertain customer demand as well as carbon emission restrictions and cargo damage rate constraints, based on the probability distribution of uncertain demand mean and variance, this paper proposes the worst-case target chance constraint with customer demand being satisfied, establishes a green multimodal transportation route optimization model based on distributionally robust chance constraints with uncertain customer demand, and uses the DR-VaR concept to transform the model into a mixed integer linear programming model, which is then solved precisely. Through sensitivity analysis of the chance-constraint probability level of customer demand, it has been found that changes in the chance-constraint probability level lead to non-linear changes in the total cost. The higher the requirement of the chance-constraint probability level, the more it will increase the total cost of intermodal transportation. Compared to changing the transportation plan, the transportation network generally chooses to increase transportation costs to counter the disturbances of uncertain customer demands, and as much as possible, adhere to the original transportation routes and modes.