大宗商品供应链管理系统，数字供应链

Hybrid evolutionary approach for multi-objective optimization in a real-life transportation problem

A project showcasing a scheduling algorithm to optimize warehouse robot coordination for efficient product delivery and order fulfillment.

AI-powered pharmaceutical supply chain management using LangGraph agents, OpenAI GPT-4, and optimization algorithms.

AI-Enhanced Supply Chain Optimization (AIESCO) is an AI-based algorithm designed to optimize supply chain operations and logistics.

Analyze shipping data to improve delivery performance, customer satisfaction, and logistics.

Predictive optimization model designed to improve logistics efficiency for Lavazza by identifying cost-effective warehouse locations and distribution strategies, ultimately saving 15% on transportation costs.

Engineered a BI framework to drive growth and operational efficiency. Segmented 90+ customers for churn mitigation, audited logistical ROI to optimize shipping costs, and automated margin protection for discontinued inventory. Delivered actionable KPIs for sales attribution and revenue forecasting using 830+ order records.

A revenue optimization system combining Association Rule Mining and Logistics Simulation.

Logistics performance dashboard analyzing last-mile delivery friction and customer churn. Built with SQL Server and Power BI to optimize rider efficiency.

An interactive Dash web app that solves and visualizes complex vehicle routing problems for a simulated NYC medical supply chain, comparing Google OR-Tools optimization against a baseline greedy algorithm.

This project focuses on enhancing biomass management efficiency through advanced AI and time series analysis for accurate prediction. The platform utilizes React Native for the frontend, Node.js and Express.js for the backend, MongoDB as the database, and incorporates additional technology with Python.