Schedule Optimization with Python

Schedule optimization maximizes productivity, minimizes costs and reduces delays. It is widely used in manufacturing, logistics, transportation, construction, project management, and healthcare. By optimizing schedules, organizations can allocate resources effectively, improve delivery times, and meet customer demands efficiently, leading to increased competitiveness and profitability.

🎓Schedule Optimization https://apmonitor.com/me575/index.php...

Manufacturing facilities employ expert schedulers and tools to help visualize and plan for production cycles, scheduled downtime, transitions, etc. This example is a comparison of three methods for scheduling problems:

1. Exhaustive search
2. Heuristic (Johnson's Rule)
3. Integer Programming

Objective: Minimize the delay for the production of 5 products on 2 machines. Each product requires a different amount of time to process for each step. The example demonstrates Gekko for solving scheduling problems and compares to exhaustive search and heuristic method.

There are 5 products that require 2 processing steps on separate machines. The two machines (machine Y and machine Z) work one after the other. A product must go through machine Y first and after that through machine Z. Both machines can work simultaneously, but each machine cannot work on more than 1 product at a time. The factory needs to minimize the time of production of all given products. This is equivalent to minimizing the idle time of machine Z.

Product P0 P1 P2 P3 P4
Machine Y: 10 20 15 40 8
Machine Z: 20 30 10 25 18