TY  - JOUR
A1  - Schlosser, Rainer
A1  - Walther, Carsten
A1  - Boissier, Martin
A1  - Uflacker, Matthias
T1  - Automated repricing and ordering strategies in competitive markets
JF  - AI communications : AICOM ; the European journal on artificial intelligence
N2  - Merchants on modern e-commerce platforms face a highly competitive environment. They compete against each other using automated dynamic pricing and ordering strategies. Successfully managing both inventory levels as well as offer prices is a challenging task as (i) demand is uncertain, (ii) competitors strategically interact, and (iii) optimized pricing and ordering decisions are mutually dependent. We show how to derive optimized data-driven pricing and ordering strategies which are based on demand learning techniques and efficient dynamic optimization models. We verify the superior performance of our self-adaptive strategies by comparing them to different rule-based as well as data-driven strategies in duopoly and oligopoly settings. Further, to study and to optimize joint dynamic ordering and pricing strategies on online marketplaces, we built an interactive simulation platform. To be both flexible and scalable, the platform has a microservice-based architecture and allows handling dozens of competing merchants and streams of consumers with configurable characteristics.
KW  - Dynamic pricing
KW  - inventory management
KW  - demand learning
KW  - oligopoly competition
KW  - e-commerce
Y1  - 2019
U6  - https://doi.org/10.3233/AIC-180603
SN  - 0921-7126
SN  - 1875-8452
VL  - 32
IS  - 1
SP  - 15
EP  - 29
PB  - IOS Press
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Perscheid, Cindy
A1  - Grasnick, Bastien
A1  - Uflacker, Matthias
T1  - Integrative Gene Selection on Gene Expression Data
BT  - Providing Biological Context to Traditional Approaches
JF  - Journal of Integrative Bioinformatics
N2  - The advance of high-throughput RNA-Sequencing techniques enables researchers to analyze the complete gene activity in particular cells. From the insights of such analyses, researchers can identify disease-specific expression profiles, thus understand complex diseases like cancer, and eventually develop effective measures for diagnosis and treatment. The high dimensionality of gene expression data poses challenges to its computational analysis, which is addressed with measures of gene selection. Traditional gene selection approaches base their findings on statistical analyses of the actual expression levels, which implies several drawbacks when it comes to accurately identifying the underlying biological processes. In turn, integrative approaches include curated information on biological processes from external knowledge bases during gene selection, which promises to lead to better interpretability and improved predictive performance. Our work compares the performance of traditional and integrative gene selection approaches. Moreover, we propose a straightforward approach to integrate external knowledge with traditional gene selection approaches. We introduce a framework enabling the automatic external knowledge integration, gene selection, and evaluation. Evaluation results prove our framework to be a useful tool for evaluation and show that integration of external knowledge improves overall analysis results.
KW  - Gene Expression Data Analysis
KW  - Integrative Gene Selection
KW  - Pattern Recognition
KW  - Prior Knowledge
KW  - Knowledge Bases
Y1  - 2019
U6  - https://doi.org/10.1515/jib-2018-0064
SN  - 1613-4516
VL  - 16
IS  - 1
PB  - De Gruyter
CY  - Berlin
ER  -