The automatic solution of complex planning problems with partially optimal results is a topical topic in digitization. On the one hand, companies benefit from better planning results and, on the other hand, from the savings that can be made for other activities.
The automatic optimization of planning and control activities is intended to produce the highest possible qualitative results in the foreseeable future. While more time is available for the calculation in the long-term planning for more complex tasks, short-term planning and control often leads to manageable problems in a shorter time.
Common methods for automatic optimization are mathematical LP solvers, metaheuristics or metaheuristic solvers, all of which are often referred to as optimizers. Mathematical optimizers find optimal solutions for many problems, but sometimes require unacceptably long computation times. Meta heuristics, on the other hand, produce acceptable to good solutions in a short time, but these can often not be adapted to varying problems. Metaheuristic solvers quickly generate good solutions and can be easily adapted to changing problems.
We have developed optimization solutions for a wide range of practical problems. Detailed planning of production implementation in production, creation of personnel plans or the circulation and cycle planning in logistics are examples of this, as well as the planning of transport processes in aircraft terminals and freight transports.