The next step on our Innovation Roadmap!

In addition to the practical implementation of our technology roadmap, improving our (theoretical) knowledge base is of vital importance.


TU/e partnership

KMWE participates in a fruitful partnership with the University of Technology Eindhoven, to further improve our theoretical knowledge base.

One of our current projects investigates data-driven algorithms to improve planning & scheduling methods of a manufacturer in the high-mix, low-volume, high-complexity industry.

Recently, this resulted in another published paper titled:


Other published papers are:


Paper on planning & scheduling

This paper addresses the problem of scheduling jobs on identical parallel machines with tool switches in a high-mix, low-volume manufacturing environment.

The objective is to maximize the profit generated by the manufacturing system, which is composed of revenue generated by the finished operations minus tool switching costs and penalty costs of unfinished priority jobs.

In other words: maximizing profit while minimizing operator workload and satisfying customer requirements.

Highlights of the paper:

  • A genetic algorithm and mixed-integer linear program to solve the problem is developed;
  • The proposed genetic algorithm yields around 26% profit improvement with respect to traditional practices;
  • We use real world data and test in a digital factory to achieve maximum validity.

We are very pleased with the results and we try to embed them in our roadmap.


Download the paper

A) The paper is available on: “Computers and Operations Research

B) The paper is available in: Journal of Computers and Operations Research (publisher: Elsevier), Volume 160, December 2023


Other related posts to our technology roadmap



<< Back to overview