This project focuses on simulation-based optimization to identify the optimal delivery center location for last-mile delivery. It aims to enhance parcel distribution efficiency by considering customer locations, delivery routes, operational costs, and prioritization through Python simulations.
To simulate and optimize delivery center placement that minimizes delivery time and operational costs, improving last-mile delivery services in urban setups.
- 🗺️ Generate map data with nodes, edges, and customer locations
- 📦 Simulate delivery data including warehouse location and parcel demand
- 🛣️ Use A-star algorithm to find shortest paths and delivery routes
- 🔄 Apply Monte Carlo optimization to select the best delivery center location
- ✅ Verify model accuracy through multi-day simulations
- 📊 Visualize working time, route length, and leftover parcels for analysis
- Incorporate traffic, road conditions, and real-time demand data
- Integrate machine learning for adaptive route optimization
- Explore eco-friendly delivery methods to reduce carbon footprint
- Enhance model flexibility for varied operational environments
- Python (Jupyter Notebook)
- NetworkX for graph and pathfinding
- Monte Carlo Simulation
- Data visualization with Matplotlib and Pandas
- Debayan Biswas –
