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ddop: A python package for data-driven operations management

Authors: Andreas Philippi; Simone Buttler; Nikolai Stein;

ddop: A python package for data-driven operations management

Abstract

ddop is a Python package for data-driven operations management (DDOM). Among other things, it includes well-established DDOM algorithms, as well as standard benchmark datasets. The application programming interface (API) of ddop is designed to be consistent, easy-to-use, and accessible even for non-experts. With only a few lines of code one can: Apply and compare well-established DDOM models. Test new developed models on benchmark datasets provided in the package.

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Keywords

machine learning, data-driven decision making, operations management, newsvendor

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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