Multi-Attribute Collaborative Optimization for Multimodal Transportation Based on User Preferences

Given the differing interests and demands of various participants in multimodal transportation, this paper proposes a multi-attribute decision-making method driven by user preferences. Firstly, a four-dimensional optimization model is established with the objectives of minimizing transportation costs, transportation time, carbon emissions, and transportation risks. Furthermore, considering the practical aspects of transportation, differentiated time window constraints are designed based on the continuous time windows of highway transportation, railway train schedules, and the arrival and departure time characteristics of waterway vessels. In terms of solution methods, an improved Genetic Algorithm (GA) and Aptenodytes Forsteri Optimization (AFO) hybrid algorithm (GA-AFO) is proposed, which introduces GA to generate a high-quality initial population to accelerate convergence. By replacing the traditional gradient estimation strategy with a random mutation strategy based on probability distribution, the local search mechanism of AFO is enhanced. Furthermore, in response to the aforementioned multi-objective problem, a multi-attribute decision-making method is devised to reconcile the subjective preferences of decision makers with objective weights, thereby yielding more scientifically valid decision outcomes. Numerical experiments have shown that the designed hybrid algorithm can quickly find solutions and demonstrates good robustness. The proposed multi-attribute decision-making method is able to generate decision schemes tailored to the preferences of different decision makers, thus providing a scientific basis for the formulation of personalized transportation schemes.