Personal experiments on Reinforcement Learning
A detialed analysis on the customers, products, orders and shipments of the Brazilian E-commerce giant Olist.
Optimal routing and delivery solutions using Google Maps and Python.
Gen AI–powered sprint risk analysis prototype for Technical Program Management. Simulates automated detection of delivery blockers and cross-team dependencies, demonstrating potential to reduce manual status review time by ~30–40% and improve early risk visibility in engineering programs
A dynamic Python-based routing and package management system for optimizing delivery schedules and operations. Designed for WGUPS, it showcases efficient use of algorithms and data structures for real-world logistics solutions.
🚚 "2021 Huawei Delivery Optimization Competition" - Using a genetic model to minimize the multi-vehicle transportation cost with vehicle capacity constraints（“2021华为配送优化竞赛” - 使用遗传算法在车辆运力限制下最小化多车辆的运输成本）
Hermes Logistics - Fleet Management & Route Optimization
An Operations Research portfolio project solving the Capacitated Vehicle Routing Problem (CVRP) for last-mile logistics using PyVRP and Python.
🚚 A genetic model written in Python for minimizing the delay time of delivery routes（使用Python编写的用于最小化物流配送时间的遗传算法）
BandwidthGuard — a free, open-source Windows utility by Yoshie Shiraishi that stops Windows Update, Delivery Optimization, and BITS from silently eating your bandwidth. One command to lock it down, one to restore Windows defaults.
Building a Semantic Delivery Coordination Engine
Business analytics & data visualization project analyzing Blinkit’s quick commerce model-delivery speed, urgency marketing, customer behavior, and revenue insights using Tableau.
Live trace collector for Intune Company Portal Win32/MSIX/LOB app deployments. Captures baseline, network trace, IME log delta, time-filtered event logs, and content-distribution stack (WinGet/DO/WU) into an ODC-compatible ZIP.
University final project - "Веб-приложение для оптимизации авиамаршрутов доставки почтовых отправлений с использованием роевого интеллекта" (Grade A)
A machine learning-powered platform to accurately predict delivery times in hyperlocal logistics by fusing Google Maps, live weather, and historical trip data.
Engineering leadership playbook covering delivery standards, flow metrics, Agile practices, AI in SDLC, and scalable team governance.
A Python-based delivery optimization model using nearest neighbor heuristics for order grouping and route efficiency, with included CSV datasets and test results.
Multi-objective Vehicle Routing Problem solver using NSGA-II — 4-objective optimization (distance, lateness, idle time, fairness) with Kathmandu traffic modeling, demand forecasting, and FastAPI REST API
TypeScript based engine for optimally assigning delivery orders to riders using multi-stage optimization, intelligent batching, and dynamic surge handling.
A delivery optimization project using causal inference and logistic optimization
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