| Summary: | Extracting valuable knowledge from data to support decision making is a widely
practiced trend. Data-driven decision support (DDDS) provides insight for decision makers
by exploring and extracting underlying patterns within a dataset. This thesis covers the
process of DDDS in reducing driving under the influence of alcohol (DUI) offenses by
introducing proposed prison sentences. In this thesis, DDDS is applied to a DUI dataset by
analyzing patterns in the dataset and by introducing proposed prison sentences for offenders
to reduce the number of DUI cases. Background theories in data mining, machine
learning, optimization and decision science that are related to the thesis project are also
covered. Furthermore, the thesis presents the application of data analysis and
multiobjective optimization, in formulating and optimizing objective functions representing
DUI reduction. The results obtained from the analysis show that, by grouping individuals
with similar DUI patterns and by introducing different proposed prison sentences for
each group, it is possible to provide decision support that can reduce the number of DUIs
at certain time intervals.
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