Article Details

Abstract

In response to the problem of distorted supply chain demand forecasting and unbalanced resource allocation caused by the sharp fluctuations in the international trade environment, this paper presents a decision-making framework that combines feature-enhanced Logit and multi-objective programming. First, this paper constructs a Logit prediction model based on the generalized cost-utility function, and explicitly quantifies the impact of external factors on market share by introducing a supply chain weighting factor. Subsequently, a dual-objective programming model for resource allocation based on dynamic demand constraints is established. The Pareto optimal frontier is obtained by applying the fuzzy normalization strategy and the interior point method. The experimental results show that the decision-making framework proposed in this paper has achieved good results in demand forecasting and resource allocation optimization, and also confirm the robustness of this cascaded framework in complex system modeling.

Keywords

feature-enhanced Logit; multi-objective linear programming; supply chain resource allocation; fuzzy normalization; data-driven

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About the authors

Zhao Youqi (2005-), male, Han ethnicity, from Linyi, Shandong Province, undergraduate studies, research direction: machine learning; Corresponding author: Yang Shanliang (1989-), male, Han ethnicity, from Zibo, Shandong, with a PhD in Engineering. His research interests include multimodal deep learning and image processing.