Optimize processes automatically through optimization and machine learning
Optimize processes automatically through optimization and machine learning
Artificial intelligence (AI) allows software programs to solve tasks autonomously. Particularly interesting for us as a solution provider of B2B software for industrial customers are the possibilities of autonomous learning and automatic optimal solution creation. For example, systems using neural networks can interpret external data correctly, learn from such data and leverage the learning outcomes it achieves to achieve specific goals and tasks through flexible adaptation. We are currently starting projects with neural networks that learn the planner’s activities to assist the planners and automate the planning.
Optimizers (solvers) make it possible to solve complex problems automatically, sometimes optimally. These exist in large numbers in the planning of production and logistics processes, e.g. in the detailed planning of production execution, the creation of personnel plans or the circulation and cycle planning in logistics. For these tasks, we have ready-made solutions in our software offering, which significantly simplify your planning processes and improve their results.
Optimization and automatic plan creation
Optimization and automatic plan creation
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.
Neural Networks
Neural Networks
An artificial neural network (ANN) is a paradigm of information processing that is inspired by the way biological systems of the brain, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It consists of a large number of strongly connected processing elements (neurons) working in unison to solve specific problems. ANNs learn through examples like people. An ANN is configured by a learning process for a particular application, e.g. Pattern recognition or data classification. Learning in biological systems requires adaptations of the synaptic connections between the neurons. This also applies to ANNs.
Neural networks, with their remarkable ability to derive meanings from complicated or inaccurate data, can be used to detect and extract patterns, and to identify trends that are too complex for other computer techniques or humans to be perceived. A trained neural network can be considered as an “expert” in the category of information it had to analyze. This expert can provide forecasts for new situations of interest, on the basis of which ANN, like humans, can derive and execute actions.
Examples of use cases
Examples of use cases
Adaptive Learning: The ability to learn tasks based on the data given for training or initial experience.
Self-organization: An ANN can create its own organization or presentation of the information it receives during learning.
Real-time operation: ANN calculations can be performed in parallel, and special hardware devices are developed and manufactured to take advantage of this capability.