Yaxin Cui

Yaxin Cui, Yuanqiang Zeng, Jiapeng Yan et al.

Large Language Models (LLMs) have achieved remarkable success in general benchmarks, yet their competence in commodity supply chains (CSCs) -- a domain governed by institutional rule systems and feasibility constraints -- remains under-explored. CSC decisions are shaped jointly by process stages (e.g., planning, procurement, delivery), variety-specific rules (e.g., contract specifications and delivery grades), and reasoning depth (from retrieval to multi-step analysis and decision selection). We introduce CSCBench, a 2.3K+ single-choice benchmark for CSC reasoning, instantiated through our PVC 3D Evaluation Framework (Process, Variety, and Cognition). The Process axis aligns tasks with SCOR+Enable; the Variety axis operationalizes commodity-specific rule systems under coupled material-information-financial constraints, grounded in authoritative exchange guidebooks/rulebooks and industry reports; and the Cognition axis follows Bloom's revised taxonomy. Evaluating representative LLMs under a direct prompting setting, we observe strong performance on the Process and Cognition axes but substantial degradation on the Variety axis, especially on Freight Agreements. CSCBench provides a diagnostic yardstick for measuring and improving LLM capabilities in this high-stakes domain.

Genliang Li, Yaxin Cui, Jinyu Su

Metaheuristic algorithms have gained widespread application across various fields owing to their ability to generate diverse solutions. One such algorithm is the Snake Optimizer (SO), a progressive optimization approach. However, SO suffers from the issues of slow convergence speed and susceptibility to local optima. In light of these shortcomings, we propose a novel Multi-strategy Improved Snake Optimizer (MISO). Firstly, we propose a new adaptive random disturbance strategy based on sine function to alleviate the risk of getting trapped in a local optimum. Secondly, we introduce adaptive Levy flight strategy based on scale factor and leader and endow the male snake leader with flight capability, which makes it easier for the algorithm to leap out of the local optimum and find the global optimum. More importantly, we put forward a position update strategy combining elite leadership and Brownian motion, effectively accelerating the convergence speed while ensuring precision. Finally, to demonstrate the performance of MISO, we utilize 30 CEC2017 test functions and the CEC2022 test suite, comparing it with 11 popular algorithms across different dimensions to validate its effectiveness. Moreover, Unmanned Aerial Vehicle (UAV) has been widely used in various fields due to its advantages of low cost, high mobility and easy operation. However, the UAV path planning problem is crucial for flight safety and efficiency, and there are still challenges in establishing and optimizing the path model. Therefore, we apply MISO to the UAV 3D path planning problem as well as 6 engineering design problems to assess its feasibility in practical applications. The experimental results demonstrate that MISO exceeds other competitive algorithms in terms of solution quality and stability, establishing its strong potential for application.

Faez Ahmed, Yaxin Cui, Yan Fu et al.

Graph Neural Networks have revolutionized many machine learning tasks in recent years, ranging from drug discovery, recommendation systems, image classification, social network analysis to natural language understanding. This paper shows their efficacy in modeling relationships between products and making predictions for unseen product networks. By representing products as nodes and their relationships as edges of a graph, we show how an inductive graph neural network approach, named GraphSAGE, can efficiently learn continuous representations for nodes and edges. These representations also capture product feature information such as price, brand, or engineering attributes. They are combined with a classification model for predicting the existence of the relationship between products. Using a case study of the Chinese car market, we find that our method yields double the prediction performance compared to an Exponential Random Graph Model-based method for predicting the co-consideration relationship between cars. While a vanilla GraphSAGE requires a partial network to make predictions, we introduce an `adjacency prediction model' to circumvent this limitation. This enables us to predict product relationships when no neighborhood information is known. Finally, we demonstrate how a permutation-based interpretability analysis can provide insights on how design attributes impact the predictions of relationships between products. This work provides a systematic method to predict the relationships between products in many different markets.