The Chair of Management Science is led by Dr. Sven Mallach who joined Universität Siegen as an interim professor in April 2024.

It covers the fundamental scientific questions, challenges, and methodologies that arise in terms of the solution of optimization and decision making problems in economic management and planning processes. This includes in particular the modeling and algorithmic solution of operations research problems with mathematical and combinatorial optimization methods, and the analytical investigation of their structure (e.g., in terms of data, graphs, and networks).

Typical combinatorial optimization problems that frequently arise in management science and operations research include assignment, matching, ordering, layout, scheduling, selection, and routing problems, often tightly related to logistics, production, allocation, and strategic decisions. Literally, whenever a profit, throughput, performance, or yield is to be maximized (likewise a cost, loss, or idleness is to be minimized) under a choice to be made from a finite (but often large) set of options, and in the presence of scarce resources or otherwise restricting conditions, managers and controllers are likely confronted with a combinatorial optimization problem. Very prominent examples are for instance the traveling salesman and vehicle routing problems as well as facility location and facility layout problems.

In its research and teaching, the chair focuses on the algorithmic solution of linear and quadratic optimization problems, in particular (but not only) via the development and enhancement of sophisticated exact approaches based on integer programming, like e.g. cutting plane methods and branch-and-cut algorithms, elaborate model reformulations and linearization techniques, and tailored polynomial-time combinatorial algorithms. Following the principles of the algorithm engineering paradigm, theoretical investigations (in terms of e.g. polyhedral, graph, or complexity theory), algorithmic implementations of high performance, and qualified experimental studies are common and integral parts of our work. Our methodologies are further accompanied and guided by quantitative problem structure analyses that include statistical as well as data science methods. Ultimately, we strive to push frontiers in terms of the size or share of practically relevant instances of important problems that can be solved routinely. Moreover, the chair covers also inexact (that is, approximate or heuristic) approaches to typical challenges arising in operations research and business analytics, including predictive data analysis and forecasting methods.