Sensitivity analysis for weak constraint generation

Authors:
Jamshaid G. Mohebzada;Michael M. Richter;Guenther Ruhe
Affiliations:
Department of Electrical & Computer Engineering, University of Calgary, Canada;Department of Computer Science, University of Calgary, Canada;Department of Electrical & Computer Engineering, University of Calgary, Canada and Department of Computer Science, University of Calgary, Canada
Venue:
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Year:
2011

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Abstract

In this paper we consider multi-constraint planning problems with limited and incomplete knowledge. We assume an optimization algorithm and a situation where not all existing knowledge can be formulated as constraints. As a result, one wants to change the plan in a way that weak constraints are relaxed. This can be done by changing some input constraints and obtaining a new input to the optimizer. We present a method for estimating the impact of such changes. The methods for sensitivity analysis are simulation and clustering. The main application domain and area for experiments is strategic release planning. A prototype simulator tool, RPSim, was developed to illustrate the applicability of sensitivity analysis. As a proof-of-concept, a sample release planning project with thirty features and three stakeholders is used to illustrate the approach.