Usually, to solve optimization tasks, including scheduling tasks, we use specialized software called an optimization solver. It is capable of performing a smart search of the status space and finding good or even optimal solutions.
There are different types of solvers such as mip/lp solvers, constraint solvers, sat solvers and others. For all of them, it is necessary to find a suitable way of modeling the problem for a specific optimization problem and, if necessary, to add on to the solver. Some well-known solvers are ones such as Gurobi, Cplex or the OR-Tools solver library.
As we started developing our platform, it was clear that complex and more extensive tasks required more than the standard solution method, which is assigning a complete task to an optimization solver and waiting for a solution, because these tasks exceeded the possibilities of solvers and technology in relation to memory and time requirements.
Therefore, we used the natural decomposition of the optimization task into subtasks solved at the workstation level, which allows the solution to scale. In practice, this approach reflects the method of production management, where the manager is responsible for certain workstations. This solution enabled us to isolate the effects of sudden changes in production only to certain stations and to preserve the original solution at the other stations.
The beginning of the European research project ARUM from the EU Horizon 2020 program. We get access to representative data in the field of production lines from partner companies Airbus and Iacobucci.
Key application for production planning and customization of Airbus aircraft.
Deployment of a custom application to define different scenarios and optimize results.
Introduction of a set of REST APIs for connecting external application systems.
EPIQA is brought to market as a product for planning investment units or buildings.