Mathematical optimisation as a specialised solution for specialised planning problems

Mathematical optimisation is used in situations where standard planning problems can no longer be solved using standard software applications. Highly specialised planning problems often require specially developed algorithms. We are happy to assist you in determining whether, and to what extent, an optimisation solution using mathematical or heuristic methods can help you.

Whether it involves optimising your operational performance to make better and therefore more cost-effective use of your raw materials and resources on a daily basis, or optimising your strategic positioning to continuously improve your production and/or logistics.

Our consultants’ many years of expertise in production planning and business intelligence, combined with their comprehensive understanding of your production structure, provide an excellent foundation for sustainable optimisation success. You can count on us at every stage of an optimisation project:

Orientation phase – sorting and evaluating

We are happy to assist you in sorting out the problem area and assessing the general feasibility of the optimisation project. In doing so, we examine both functional aspects (e.g. setting realistic targets) and technical aspects (e.g. data availability and quality). On request, we are also happy to help you describe the planning task in a comprehensive and vendor-neutral manner, so that you can put out a precise and, therefore, promising call for tenders.

Potential phase – evaluating costs and benefits

We help you quantify the potential of an optimisation in reliable figures, so that you have a clear picture of the costs and expected benefits. In collaboration with your business department and your IT team, we create a bespoke optimisation model that focuses on the key aspects of the planning problem. You can evaluate this optimisation model in detail using a prototype to reach a well-informed decision.

Production phase – Implementing solutions

If the optimisation solution demonstrably delivers added value and you wish to integrate it into your day-to-day operations, we offer a software platform for production use. The optimisation model created during the potential phase can usually be reused directly or extended to include further aspects required for production. We are happy to assist you with integration into an existing IT landscape by configuring the built-in connectors together with you or, if required, by programming a bespoke interface.

Frequently Asked Questions

When is a mathematical optimisation solution preferable to traditional detailed planning?
As soon as a planning problem is so specific that it can no longer be modelled using the standard rules of detailed planning software – for example, in the case of complex conflicts of objectives between several constraints simultaneously – a specially developed mathematical algorithm can provide a better solution. During the orientation phase, SimPlan assesses whether a standard approach is sufficient or whether the effort involved in developing a bespoke optimisation solution is worthwhile.

How does a mathematical optimisation project typically proceed?
In the orientation phase, the planning problem is analysed and assessed; in the potential phase, a cost-benefit analysis is carried out with realistic estimates of the effort involved; and in the implementation phase, the developed solution is integrated into ongoing operations. This ensures transparency regarding whether the effort is worthwhile before the actual implementation takes place.

For what kind of planning problems is mathematical optimisation frequently used instead of simulation?
Simulation is well suited to testing the behaviour of a system under various scenarios. If, on the other hand, the aim is to mathematically filter out the best or a near-optimal solution from a very large number of possible solutions – for example, in complex assignment or sequencing problems – an optimisation algorithm is more likely to be used. Often, both approaches are combined: optimisation provides a proposed solution, whilst simulation tests it under realistic conditions.