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👆 Order-Driven-Scheduling-MILP-Optimizer-using-Python

This MILP optimizer demonstrates how to compute an order-drive production schedule accounting for priorities, due dates, and penalties and provides a base for dynamic re-scheduling

🧏 Problem Context

In Make-To-Order and Custom production environments, the schedule is often disrupted by last-minute high-priority (rush) orders. We must dynamically trade off:

  • Due dates (to minimize total tardiness or lateness penalties),
  • Rush-order-priorities, and
  • Sequence-depenedent setup costs

💇 Model features

  • Machines: parallel identical machines

  • Orders: multiple orders, each with:

    • Processing time,
    • Due date,
    • Priority weight,
    • Setup time (if preceding order differs).

  • Decision variables:

    • Sequencing of orders (start/finish times),
    • Assignment to machines,
    • Order lateness/tardiness

  • Objective: Minimise weighted tardiness + Setup costs

🧑‍🦽‍➡️ Technologies used

  • Python 3.13 > PuLP library
  • Visual Studio Code
  • Basics of coding

🧦 Requirements

  • Concepts of Production Scheduling
  • Knowledge on prompt engineering
  • Basics of coding