Case study on scheduling cyclic conveyor belts

TL;DR

This paper presents a case study on optimizing a manufacturing conveyor belt system, demonstrating that practical solutions can be efficiently achieved through aggressive random scheduling despite the NP-hard nature of the problem.

Contribution

The study shows that in real-world scenarios, heuristic random scheduling can effectively optimize complex production line problems.

Findings

Random scheduling yields near-optimal throughput improvements.

Practical solutions outperform traditional optimization methods.

NP-hardness does not preclude efficient real-world solutions.

Abstract

We optimise the production line of a manufacturing company in southern Germany in order to improve throughput. While the optimisation problem is NP-hard in general, analysing production data we find that in practice the problem can be solved very efficiently by aggressive generation of random machine schedules.