This repository represents Multi-Factory Network Scheduling MILP optimizer using Python language
This project models and solves a Multi-Factory Network Scheduling Problem in a supply chain with:
- Multiple plants producing subassemblies/components
- Further processing at other plants or Distribution Centers (DCs)
- Synchronization of production and transportation schedules across locations
- Incorporation of decoupling points, buffer stocks, and just-in-time (JIT) constraints
- Objective to maximize service level or minimize total cost/lead time
- Plants: 2
- Distribution Centers (DCs): 7
- Products: 11 distinct SKUs moving through the system
- Synchronization requires planning production batches and transport flows together to meet demand timely
- Multi-echelon production scheduling: Batch start times and quantities for each plant-product combination
- Transportation scheduling and lead times between plants and DCs
- Buffer stocks to smooth flow between production and delivery stages
- JIT constraints to minimize inventory while ensuring service
- Minimize makespan (total schedule length) or weighted cost (production + transportation)
- Python 3.13 > PuLP library
- Visual Studio Code
- ChatGPT & Perplexity Pro
- Concepts of Network optimization
- Knowledge on prompt engineering
- Basics of coding
"True optimization is the revolutionary contribution of modern research to process decision" - George Dantzig