Projects (QM/MuS)
SFB 559: Modelling of large logistics nets -
Project M1: Structured GNL-Models and efficient simulation (1998 - 2008)
The special research area (Sonderforschungsbereich) SFB 559, as funded by the DFG, aims at providing adequate support for the planning, realisation and operations phases of large logistic networks (GNLs). In particular, model-based analysis is exploited for this purpose, with so called process chains forming the basic modelling paradigm. Sub-project M1 pursues the task of extending and formally defining this modelling paradigm such that performance measures (e. g. throughput, population) as well as economical and ecological measures become computable. In addition, aggregation and decomposition approaches are to be incorporated, mainly in order to improve the efficiency of simulative analyses.
SFB 559: Modelling of large logistics nets -
Project M2: Efficient analysis methods (1998 - 2008)
The special research area (Sonderforschungsbereich) SFB 559, as funded by the DFG, aims at providing adequate support for the planning, realisation and operations phases of large logistic networks (GNLs). In particular, model-based analysis is exploited for this purpose. In the Computer Science field, very efficient analysis techniques have been developed for specific modelling paradigms. These methods appear well applicable during the design phases of GNLs. The objective of this sub-project, M2, is to adopt and extend these analysis approaches for the particular application area. Techniques from queueing theory (e. g. product form algorithms and approximate derivatives), numerical techniques for the analysis of stochastic processes and recently developed forms of structured Markov chain analysis of hierarchical models will, amongst others, be evaluated, adapted and included.
DFG-Project: Markovian Arrival and Service Processes for Performance and Reliability Analysis (since 2008)
Stochastic models are widely used for the analysis of performance, security
and reliability of systems. In these models time consumption has to be modelled by
probability distributions or stochastic processes. In most approaches used so far time
consumption is described by probability distributions being independent and identically
distributed. However, experience shows that many parameters of a model are highly
correlated and that this correlation is observable over a long period of time.
Ignoring correlations can lead to significant errors in performance measures as
illustrated by various examples. Therefore stochastic models need to be found
that are capable of capturing correlations. Furthermore the models have to be
parameterizable such that they reflect the characteristics of a measured trace
accurately and still result in an analysable model. An attractive model
class is Markovian Arrival Processes (MAPs).
Existing approaches for fitting the parameters of a MAP to real traces still
have some shortcomings impeding their use in practical applications. The key activities
of this project aim at eliminating or at least reducing these shortcomings with the objective
to make parameter fitting of MAPs similarly efficient and robust as it is possible
with methods developed in the recent years for the fitting of phase type distributions.