👆 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

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🧏 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

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💇 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

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🧑‍🦽‍➡️ Technologies used

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

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🧦 Requirements

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