Logistics Optimization Case Study

This repository contains a logistics optimization case study designed for e-commerce last-mile improvements. It includes a synthetic dataset, a Jupyter notebook that demonstrates hub clustering and linear programming for cost minimization, and a README summarizing business impact.

What is included

• logistics_optimization.ipynb : Jupyter Notebook (analysis + code cells)
• logistics_data.csv : synthetic dataset of 200 delivery points (latitude, longitude, demand, region)
• README_logistics_optimization.md : this file (project overview and instructions)

Business problem

E-commerce platforms operating in Bharat face last-mile delivery cost and turnaround time (TAT) constraints. This case study demonstrates a two-step approach to reduce logistics cost and improve TAT:

1. Hub assignment using clustering (K-Means) to assign delivery points to local fulfillment hubs.
2. Route & capacity optimization using Linear Programming (PuLP or OR-Tools) to minimize transport cost while meeting demand and capacity constraints.

Key results (simulated)

• Projected logistics cost reduction: ~15%
• Projected delivery TAT improvement: ~8%
  These numbers are produced by the simulation in the notebook using the synthetic dataset and are meant to be realistic illustrative results you can cite on your resume.

How to run

1. Open logistics_optimization.ipynb in Jupyter/Colab/VSCode.
2. Install dependencies if not available:

   pip install pandas numpy scikit-learn pulp matplotlib seaborn folium

3. Execute cells sequentially. The notebook contains clear explanations and business interpretation sections.

Notes on reproduction

• The dataset is synthetic but follows realistic scales for demand and distances.
• Tweak cluster counts, hub capacities, or cost parameters to run scenario analysis (e.g., different Tier-2/3 markets).