Focus areas

Background

We take joy in mathe­ma­ti­cal optimi­zation and its pro­fi­ta­ble appli­ca­ti­on to prac­ti­cal pro­blems. Nume­ri­cal algo­rith­ms for optimi­zation are now capa­ble of sol­ving pro­blems that were con­side­red com­ple­te­ly hope­l­ess just a deca­de ago. Often, the­se algo­rith­ms are even free­ly available and can achie­ve imme­dia­te added value with some pro­gramming expe­ri­ence and mathe­ma­ti­cal back­ground know­ledge. We focus on making this added value more acces­si­ble for a ran­ge of spe­ci­fic tasks, espe­ci­al­ly for SMEs.

Problem statement

Optimi­zation is uni­ver­sal­ly useful yet some­ti­mes dif­fi­cult to divi­de into subareas.

Prac­ti­cal appli­ca­ti­ons from very dif­fe­rent sub­ject are­as often have the same mathe­ma­ti­cal for­mu­la­ti­on. And some­ti­mes, slight­ly dif­fe­rent tasks from the same area requi­re fun­da­men­tal­ly dif­fe­rent approaches.

We spe­cia­li­ze in opti­mal decis­i­ons under uncer­tain­ty. This aspect is par­ti­cu­lar­ly pro­mi­nent in the eva­lua­ti­on of mea­su­re­ment data. But not only the­re — with optimi­zation, we sol­ve pro­blems from the fol­lo­wing areas:

  • Opti­mal esti­ma­ti­on: Eva­lua­ti­on of mea­su­re­ment data, para­me­ter esti­ma­ti­on, quan­ti­fi­ca­ti­on of uncertainties.
  • Opti­mal design: Design of sche­du­les, mate­ri­al flows in net­works, design of expe­ri­ments and series of experiments.
  • Opti­mal con­trol : Sta­bi­liza­ti­on of time-vary­ing sys­tems, adap­ti­ve logi­stics decis­i­ons, rein­force­ment learning.
We pro­vi­de soft­ware solu­ti­ons. Hard­ware and phy­si­cal con­s­truc­tion are not our expertise.

Competencies

You are in the right place if you have a pro­blem that requi­res mathe­ma­ti­cal mode­ling and for which a qua­li­ty mea­su­re can be estab­lished. This pro­blem should be sol­va­ble through soft­ware inte­gra­ted into the busi­ness pro­cess or indi­vi­du­al mathe­ma­ti­cal ana­ly­ses. If you are unsu­re about any of the points men­tio­ned abo­ve, just ask us!