🏙️ UrbanLogistics AI: Strategic Dark Store Location Optimization

A Decision Support System (DSS) for optimizing last-mile delivery networks using Mixed Integer Linear Programming (MILP).

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🚀 Project Overview

UrbanLogistics AI is an analytical tool designed to solve the Capacitated Facility Location Problem (CFLP) for quick-commerce (q-commerce) companies. It helps strategic decision-makers minimize operational costs while maximizing customer coverage.

Unlike simple distance-based heuristics, this project uses a mathematical optimization engine (PuLP) to determine:

1. Where to open "Dark Stores" among candidate locations.
2. Which customers should be assigned to which depot.
3. How to balance capacity constraints and service level agreements (SLA).

📺 Project Visuals

1. Main Dashboard

2. Executive and Network Topology

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💼 Business Problem

In the competitive landscape of quick commerce (e.g., Getir, Gopuff, Gorillas), logistics costs and delivery speed are the key differentiators.

• Challenge: Opening too many depots increases CAPEX and OPEX (Rent). Opening too few leads to long delivery times and customer churn.
• Goal: Find the "sweet spot" that minimizes total cost (Rent + Transportation) while ensuring delivery times are under a specific threshold (e.g., <15 mins).

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⚙️ Solution Approach

This project bridges Data Science and Industrial Engineering:

1. Synthetic Data Generation: Simulates realistic customer clusters (Gaussian Blobs) and candidate sites with variable rent costs based on city center proximity.
2. Mathematical Modeling (MILP):
   • Objective Function: Minimize $Z = \sum (Fixed Costs) + \sum (Variable Transportation Costs)$.
   • Constraints:
     • Capacity Constraints (Depots cannot exceed max daily orders).
     • Assignment Constraints (Each customer assigned to exactly one depot).
     • Service Range Constraints (Max distance e.g., 8km).
     • Budget Constraints (Max number of depots).
3. Visualization: Interactive "Spider Maps" to visualize the supply chain network and KPI dashboards for financial impact analysis.

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🛠️ Tech Stack

• Language: Python
• Optimization: PuLP (Linear Programming Solver)
• Web Framework: Streamlit
• Geospatial Analysis: Folium, Geopy
• Visualization: Plotly Express, Plotly Graph Objects
• Data Manipulation: Pandas, NumPy

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💻 How to Run Locally

1. Clone the repository

   git clone https://github.com/Farslan-x/UrbanLogistics-AI.git
   cd UrbanLogistics-AI

2. Create a Virtual Environment (Optional but recommended)

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # Mac/Linux
   source venv/bin/activate

3. Install Dependencies

   pip install -r requirements.txt

4. Run the App

   streamlit run app.py

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📊 Future Improvements

• Integration with real-world traffic data (OSRM / Google Maps API).
• Demand forecasting module using Time Series Analysis (ARIMA/Prophet).
• Multi-objective optimization (minimizing CO2 emissions).

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