Forschungscluster „Intelligent Systems for Decision Support“

Participating Researchers

Brief summary of the research area

Decision making and planning in modern enterprises takes place in highly dynamic and fiercely competitive environments. Furthermore, the diffusion of Internet technology into all areas of private and public life has given rise to the development of innovative forms of inter-company collaboration along the supply chain which further boost the complexity of real-world decision making.

At the same time, advances in computer technology like the availability of powerful yet affordable parallel computing resources of variable granularity, middleware for distributed computing, advanced software tools for the design and the development of distributed applications as well as recent achievements in the development of advanced methods for modelling and solving complex decision problems open up new vistas in the field of computer-assisted planning and decision making.

The main points of reference for the technology-driven decision support are the individual, i.e. the decision maker, and the organization. The individual defines the information to be gathered and the form of its presentation. The organization defines the restrictions by its structure (e.g. hierarchy), processes and by its planning, control and management mechanisms. Basing on these pre-requisites the decision support systems have to be designed and implemented calling for empirically founded insights into human decision making behaviour and motivation. However, taking into account the advances in computer technology, in fact a dual relationship evolves: the system design is defined by the individual and the organization, but at the same time, the potential of advancing technology is an enabler for an innovative decision support.

This leads to the conclusion that the establishment of a cluster topic “Intelligent Systems for decision support” will not only offer an attractive challenge from the academic point of view, but will also feature great opportunities with respect to practical application. Thus, especially students holding industrial positions will be attracted by a doctoral program in this area. By this, the cluster topic “Intelligent Systems for Decision Support” will significantly contribute to the dissemination of scientific excellence into practice. This in turn also strengthens the university’s position as a preferred partner for the industry and an incubator for excellent research in the area of decision support. Furthermore, we will attract students that are already working in companies.

In particular, the cluster topic “Intelligent systems for decision support” will comprise the following aspects:

  • With respect to the application area, the cluster topic will focus on logistics, supply chain management, production planning and control as well as risk analysis in project management, service and decision management. In these fields, the participating researchers have achieved proven expertise and international reputation which will ensure that sustained success will be achieved.
  • As to the models and scenarios, PhD supervision in the cluster topic will attach importance on projects dealing with rich models, taking into account all kinds of practical constraints and goals. Special stress is laid on deciding and planning under uncertainty, in dynamic environments and in collaborative settings. First and second order uncertainty are an intrinsic factor in project and business decision making processes. These uncertainties might even vary with different scenarios. High dimensional probability models can overcome the inherent complexity and uncertainty present in such processes. The researchers distinguish on the development of the respective computer based tools and on large scale applications.
  • From a methodological point of view, advanced methods will be applied like modern metaheuristics and neighbourhood search techniques, complexity and structural analysis, structural equation modelling, design of efficient algorithms for related problems, multi-agent systems, discrete-event simulation, distributed scheduling techniques and new probabilistic strategies of knowledge modelling and processing.
    • Baumöl, U. (2006): Methodenkonstruktion für das Business-IT-Alignment. To appear in WIRTSCHAFTSINFORMATIK, September 2006.
    • Fandel, G. (2001): Interdependencies Between Network and Activity-analytical Descriptions of Production Relationships in the Implementation of Large-scale Projects - Illustrated by Textbook Production. International Journal of Production Economics, 70, 227-235.
    • Fandel, G.; Hegener C. (2005): Ein Ansatz zum General Lot Sizing and Scheduling Problem (GLSP) für die mehrstufige Fertigung. Zeitschrift für Betriebswirtschaft, 9, 879-894.
    • Fandel, G; Stammen, M. (2004): A General Model for Extended Strategic Supply Chain Management with Emphasis on Product Life Cycles Including Development and Recycling. International Journal of Production Economics, 89 (3), 293-308.
    • Fließ, S., Becker, U. (2006): Supplier Integration - Controlling of Co-Development Processes. Industrial Marketing Management, 35, 28-44.
    • Fließ, S. (2004): Qualitätsmanagement bei Vertrauensgütern. Marketing – ZFP 25, Special Issue "Dienstleistungsmarketing", 37 - 48.
    • Fließ, S., Kleinaltenkamp, M. (2004): Blueprinting the Service Company: Managing Service Processes Efficiently. Journal of Business Research, 57, 392-404.
    • Gehring, H.; Bortfeldt, A. (2002): A Parallel Genetic Algorithm for Solving the Container Loading Problem. International Transactions of Operational Research, 9 (4), 497-511.
    • Gehring, H.; Homberger, J. (2002): Parallelization of a Two-Phase Metaheuristic for Routing Problems with Time Windows. Journal of Heuristics, 8 (3), 251-277.
    • Fischer, T.; Gehring, H. (2005): Planning Vehicle Transshipment in a Seaport Automobile Terminal using a Multi Agent System. European Journal of Operational Research, 166, 726-740.
    • Hamacher, A., Hochstättler, W., Moll, C. (2000): Tree Partitioning under Constraints -- Clustering for Vehicle Routing Problems. Discrete Applied Mathematics, 99, 55-69.
    • Epping, T., Hochstättler, W., Oertel, P. (2004): Complexity Results on a Paint Shop Problem. Discrete Applied Mathematics, 136, 217-26.
    • Blasum, U., Hochstättler, W., Oertel, P., Woeginger, G. (2006): Steiner-Diagrams and k-Star-Hubs. To appear in Journal of Discrete Algorithms.
    • Mönch, L., Unbehaun, R., Choung, Y. I. (2006): Minimizing Earliness and Tardiness on a Single Burn-in Oven with a Common Due Date and a Maximum Available Tardiness Constraint. OR Spectrum, 28(2), 177-198.
      Mönch; L., Schabacker, R., Pabst, D., Fowler, J. W. (2006): Genetic Algorithm-Based Subproblem Solution Procedures for a Modified Shifting Bottleneck Heuristic for Complex Job Shops. To appear in European Journal of Operational Research. Mönch, L., Stehli, M. (2006): ManufAG: a Multi-Agent-System Framework for Pro­duction Control of Complex Manufacturing Systems. Information Systems and e-Business Management, 4(2), 159-185.
    • Pankratz, G. (2005): A Grouping Genetic Algorithm for the Pickup and Delivery Problem with Time Windows. Operations Research Spectrum 27, 21-41.
    • Pankratz, G. (2005): Dynamic Vehicle Routing by Means of a Genetic Algorithm. International Journal of Physical Distribution and Logistics Management (IJPDLM) 35 (5), 362-383.
    • Rödder, W. (2003): On the Measurability of Knowledge Acquisition and Query Processing. International Journal of Approximate Reasoning, 33/2, 203-218.
    • Kern-Isberner, G.; Rödder, W. (2004): Belief Revision and Information Fusion on Optimum Entropy. International Journal of Intelligent Systems, Special Issue on Uncertain Reasoning (Part 2), 19, 837-857.
    • Rödder, W.; Kulmann, F. (2006): Recall and Reasoning - an Information Theoretical Model of Cognitive Processes. Information Sciences, 176-17 (2006) 2439-2466.
Redaktion | 13.08.2021