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.
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Mohebzada, J.G., Richter, M.M., Ruhe, G. (2011). Sensitivity Analysis for Weak Constraint Generation. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_18
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DOI: https://doi.org/10.1007/978-3-642-23199-5_18
Publisher Name: Springer, Berlin, Heidelberg
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