The rise of vast computing power and data storage capacity together with the ubiquitous availability of the internet has brought us to the dawn of a new era, which leaves no aspect of industry, business, services, logistics, or even basic infrastructure unchanged. An enterprise must seek to take advantage of these new resources in order to stay ahead of the market, to become more efficient, to reduce its costs, and to increase its speed. Computational Intelligence conquers tasks which were previously pure human domains, ranging from driving vehicles, playing Go, to the automated planning of intermodal logistics tasks involving hundreds of trucks, trains, and ships and to the scheduling of the production of complete multi-production line factories.
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, Computational Intelligence machine learning, metaheuristics, and evolutionary computation. With more than twelve years of research experience, we help industry partners to discover where optimization technologies can increase their efficiency and speed, can reduce their costs, resource consumption, and work efforts; how it can improve their products and services by making them cheaper, better, and more reliable – all while additionally making them more environmentally friendly. Five groups of business and operational aspects can be supported by Computational Intelligence, optimization, and machine learning techniques:
Optimization can, for instance, make logistics more efficient, both on the large scale of intermodal cargo transport down to the logistics on the factory floor. 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 and data mining technologies to optimize business processes based on predicted future market developments. It is a key ingredient for production which is both highly automated and green, as prescribed by Industry 4.0 and Made in China 2025 [中国制造2025]. We help our 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 specific business or operational aspects of an enterprise. Such an aspect can either be named directly by the enterprise or be identified via consultations. Together with the stakeholders in the enterprise, a project is then defined in terms of goals, volume, a working plan with milestones, and deliverables. Research projects involving optimization and machine learning technologies differ from traditional software projects in several ways. Additional constraints and objectives for the software are often discovered 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 and then are iteratively improved in order to facilitate this dynamic situation.
Endowed Researcher Positions
An enterprise may endow a researcher position to our institute. This means to fund 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 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 our resources and the vast experience and guidance of our other team members. The enterprise may choose to extend the endowed position on a yearly basis. It may even decide to hire the researcher directly after her/his contract with us ends, at which time the researcher and the enterprise will be highly familiar with each other.
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