SupChain: Supply Chain Design
Project Topic
Adaptive, distributed optimization algorithms for dependable, strategic supply chain design based on mathematical programming.
Project Description
The present project focuses on the development of a dependable model and efficient,
adaptive optimization algorithms for supply chain design problems. Strategic supply chain design is concerned with longterm decisions regarding the configuration of a supply chain with multiple facilities (e.g. production plants, distribution centers) linked by distribution channels. Typical decisions include determining the number, location and size of new facilities (e.g. plants, warehouses), the allocation of procurement and production activities to manufacturing facilities, and the transportation modes to be used to satisfy customer demands. Market dynamics, such as mergers, acquisitions and third party agreements, as well as rapid growth and rising costs, force companies to re think the configuration of their supply chains regularly. In this project, special focus is given to building a mixed integer programming model of broad scope and applicability by including many essential aspects in supply chain redesign that have not received adequate attention in the literature.
Furthermore, due to the large data requirements of
supply chain design models and the data uncertainty concerning future conditions, data mining and other statistical methods are
required to ensure reliable supply chain decisions through data aggregation and generation of possible future scenarios. Especially application of spatial data mining techniques in the field of supply chan managment will be investigated. The algorithms for computation of supply chain member objects spatial characterization, spatial autocorrelation, spatial trend detection and the estimation of long range developments of this measures will be coupled with the optimization procedures.
Moreover, in the supply chain design process is to develop efficient distributed algorithms taking advantage of recent trends in selfadaptive metaheuristics and exploring local search and LPbased methods previously developed for related problems.
Project Members
Project Chair
Participating Research Groups
- Sect. Optimization (Fraunhofer Institute for Industrial Mathematics (ITWM))
- Sect. System Analysis, Prognosis and Control (formerly Sect. Adaptive Systems) (Fraunhofer Institute for Industrial Mathematics (ITWM))
- Distributed Algorithms Group (Department of Computer Science)
Scientific Personnel
- Dr. Teresa Melo (Sect. Optimization, Fraunhofer Institute for Industrial Mathematics (ITWM))
- Dr. Alex Sarishvili (Sect. System Analysis, Prognosis and Control (formerly Sect. Adaptive Systems), Fraunhofer Institute for Industrial Mathematics (ITWM))
- Jun.-Prof. Dr. Peter Merz (Distributed Algorithms Group, Department of Computer Science)
- Steffen Wolf (Distributed Algorithms Group, Department of Computer Science)
External Cooperation
- Prof. Francisco Saldanha da Gama, University of Lisbon (Portugal)
- Prof. Dr. Stefan Nickel, Fraunhofer Institute for Industrial Mathematics (ITWM) and Saarland University
Project Events and Achievements
- Project start: October 1st, 2005
- Project end: December 31st, 2007
Presentations
- M.T. Melo: New Optimization Methods for Strategic Supply Chain Design, Annual Meeting of GOR Working Group "Supply Chain Management", Wiesbaden, October 2005
- M.T. Melo: Heuristics for Dynamic Dial-a-Ride Problems for In-House Hospital Transportation, invited talk, University of Catania, Italy, October 2005
Project Publications
Steffen Wolf, Peter Merz. In: Carlos Cotta and Jano van Hemert ed.,
Proceedings of the 7th European Conference on Evolutionary Computation in Combinatorial Optimization. LNCS, Volume 4446, Springer, April, 2007
M. Teresa Melo, S. Nickel, F. Saldanha da Gama. In:
Computers & Operations Research. Volume 33, P. 181--208, 2006
R. Velásquez, M. Teresa Melo, S. Nickel. In: H.-D. Haasis, H. Kopfer, J. Schönberger ed.,
Operations Research Proceedings 2005, Selected Papers of the Annual International Conference of the German Operations Research Society (GOR). Springer, Berlin, 2006
A. Sarishvili, Ch. Andersson, Jürgen Franke, G. Kroisandt. In:
Neural Computation. Volume 18, P. 2568--2581, 2006
