The Institute of Applied Optimization (IAO) [应用优化研究所] provides applied research services in the fields of mathematical and combinatorial optimization, operations research, machine learning, metaheuristics, and computational intelligence. We help industry partners to discover and solve the optimization problems in their respective fields, in order to increase their efficiency and speed, to reduce their costs, resource consumption, and work efforts, and to improve their products and services by making them cheaper, better, and more reliable, while additionally making them more environmentally friendly. Five groups of business and operational aspects can be supported by optimization and machine learning techniques:

Five aspects of an enterprises operation which can be improved by optimization.

Optimization can, for instance, make logistics (B2C delivery, supply-chain-internal-, down to factory-floor logistics) more efficient. It can help to make optimal management decisions, such as finding the best assignment of staff to tasks or ideal locations to open branch offices. It can also improve production processes by automatically splitting incoming orders in production tasks and assigning these tasks to time slots on suitable machines in order to minimize the time-to-delivery and costs. Optimization can be combined with machine learning, big data, and data mining technologies to optimize business processes based on predicted future market developments. It is a key ingredient for optimal automated decisions in Industry 4.0. The IAO helps industry partners to discover which parts of their operation may benefit the most from optimization and machine learning and then develops tailor-made software solutions to reap these profits.

The Institute of Applied Optimization offers two types of collaboration to industry partners, applied research projects and endowed researcher positions.

Applied Research Projects

Applied research projects aim to improve one specific business or operational aspect of an enterprise. Such an aspect can either be named directly by the enterprise or be identified via free consultations. Together with the stakeholders in the enterprise, a project is then defined in terms of goals, volume, working plan, and deliverables. Research projects involving optimization and machine learning technologies differ from traditional software projects in several ways. Stakeholders often discover additional constraints and objectives for the software during an ongoing project. We therefore follow an agile methodology, meaning that we will quickly develop simple prototypes which can be tested by the stakeholders whose feedback can help to iteratively improve and extend the software. This also allows us to very early discover additional project goals, allowing for an early discussion and adaptation of the project plan and volume and ensuring that the final software product will provide the maximum utility for the enterprise. We offer a wide variety of project sizes.

Endowed Researcher Positions

An enterprise may endow a researcher position to our institute, which means funding one PhD for at least one year. This researcher will then carry the title “X Endowed Researcher” (where X is the name of the enterprise) in all official listings, staff directories, communications, and publications. The endowed researcher will fully focus on working with the enterprise to discover where it may benefit from optimization and machine learning and implement corresponding software solutions. The endowed researcher is more flexible than a dedicated project and also can spend significant time on premise of the enterprise. As a member of our institute, the endowed researcher benefits from its resources and the vast experience and guidance of its other members. The endowed researcher may also publish scientific articles. The enterprise may choose to extend the endowed position on a yearly basis or even decide to hire the researcher directly after her/his contract with us ends. The enterprise may also choose to support the researcher with assistants to increase the work speed and volume.

If you are interested in collaborating with us to utilize optimization, operation’s research, and machine learning for your enterprise, please directly contact the director Prof. Dr. Thomas Weise at This email address is being protected from spambots. You need JavaScript enabled to view it..