How shreya374/supply-chain-optimization works
This repository presents a supply-chain facility-location optimization: cost, capacity, and demand inputs feed a linear program that selects facilities and shipment flows, then reports the minimum-cost network. The README documents the modeling stages and visualizations, but no source excerpts are available; the notebook’s internal wiring and the spreadsheets’ precise contents cannot be verified.
Main entry points: Supply Chain py.ipynb Analysis notebook · Supply chain logisitcs problem.xlsx Logistics data · capacity.xlsx Capacity data · freight_costs.xlsx Freight costs
Components 14
Model inputs
- Logistics data
Supply chain logisitcs problem.xlsx - Capacity data
capacity.xlsx - Freight costs
freight_costs.xlsx - Variable costs
variable_costs.xlsx - Total costs
total_costs.xlsx
Optimization
- Analysis notebook
Supply Chain py.ipynb - Facility and flow decisions
Supply Chain py.ipynb - Cost minimization
Supply Chain py.ipynb - Demand and capacity limits
Supply Chain py.ipynb - PuLP / CBC solver
Results and analysis
- Open sites and shipments
Supply Chain py.ipynb - Demand scenarios
Supply Chain py.ipynb - Network visualizations
Supply Chain py.ipynb
Other components
- Supply-chain planner
Connections 13
- Supply-chain planner → Analysis notebook: runs analysis
- Logistics data → Analysis notebook: provides inputs
- Capacity data → Analysis notebook: provides capacity
- Freight costs → Analysis notebook: provides costs
- Variable costs → Analysis notebook: provides costs
- Total costs → Analysis notebook: provides costs
- Analysis notebook → Facility and flow decisions: defines variables
- Analysis notebook → Cost minimization: sets objective
- Analysis notebook → Demand and capacity limits: adds constraints
- Analysis notebook → PuLP / CBC solver: solves model
- PuLP / CBC solver → Open sites and shipments: returns solution
- Analysis notebook → Demand scenarios: evaluates scenarios
- Analysis notebook → Network visualizations: visualizes results