# Multi-Attribute Collaborative Optimization for Multimodal Transportation Based on User Preferences
## 2 Literature Review and Research Gap  
The optimization of multimodal transportation routes is a persistent topic of interest among researchers, with scholars having conducted extensive studies in this field. Overall, from the perspective of modeling, the core objective of most research is to minimize generalized costs, which encompass expenses, time, risk, and carbon emission costs, among others. For instance, researchers such as [PERSON] [2] and [PERSON] [3] have considered both expense and time costs, while [PERSON] [4] and [PERSON] [5] have taken into account expense and carbon emission costs. [PERSON] [6] focused on cold-chain logistics, incorporating both carbon emission and time costs into the study. [PERSON] [7] and colleagues, on the other hand, investigated the transportation of hazardous goods, introducing risk costs to identify the least risky routes while keeping costs within control. The primary methodologies employed in the aforementioned studies involve consolidating multiple costs into a single objective problem in need of a solution. However, given the independence of each cost component, some research has opted to examine components separately, thereby establishing multi-objective optimization models. Notably, [PERSON] [8] developed a bi-objective optimization model focusing on expense and carbon emission costs. Similarly, [PERSON] [9] constructed a bi-objective model that considers both expense and time costs. [PERSON] [10] went a step further by independently accounting for expense, time, and carbon emission costs, establishing a standalone multi-objective model and proceeding to solve it. Single-objective problems typically aim to simplify complex issues during the modeling process. In contrast, the solution methods for the aforementioned multi-objective prob lems mostly involve presenting Pareto solutions. The major drawback of Pareto solutions, however, is that they can only provide references, not effective decisions. Consequently, some researchers have begun to investigate the outcomes of each objective individually, focusing on the decision-making problem. For instance, [PERSON] [11] proposed a multi-criteria decision-making system that facilitates optimal decision making by weighting the Pareto solutions. [PERSON] [12] suggested ranking the objectives according to their importance and making decisions sequentially. [PERSON] [13] proposed assigning different preferences to each objective based on the decision maker's habits to guide the decision-making process. Additionally, researchers such as [PERSON] [14], [PERSON] [15], [PERSON] [16], and [PERSON] [17] have proposed reasonable decision-making methods from various perspectives. In terms of solving algorithms, since the multimodal transportation path problem is an NP-hard problem, intelligent algorithms remain the mainstream approach for its solution [18; 19]. For instance, researchers such as [PERSON] [20], [PERSON] [21], [PERSON] [22], and [PERSON] [23] have respectively designed improved versions of GA, Simulated Annealing (SA), Ant Colony Optimization (ACO), and Artificial Fish Swarm Algorithms (AFSAs) to tackle this problem. Building on these foundations, to avoid becoming trapped in local optima and to enhance algorithm efficiency, some researchers have combined the advantages of multiple algorithms to devise improved hybrid algorithms. For example, [PERSON] [24] designed a Genetic Algorithm-Particle Swarm Optimization (GA-PSO) hybrid algorithm, [PERSON] [25] proposed a hybrid algorithm that integrates the greedy algorithm with Particle Swarm Optimization (PSO), and researchers like [PERSON] [26], [PERSON] [27], [PERSON] [28], and [PERSON] [29], among others, have developed their own respective algorithms, all of which have achieved favorable results. In summary, this paper associates various costs with different decision-making indicators. Based on independently studying different types of transportation costs, it proposes a comprehensive decision-making method tailored to the preferences of different decision makers.