Coordination of Just-in-Time Deliveries with Multi-attribute Auctions

Just-in-time deliveries are crucial for many industries. They are particularly essential when the properties of the delivered resource or the demanding processes are sensitive in time. Rigid, centralized planning tends to fail, especially in dynamic environments with distributed decisions and control. Under the constraints of distributed decisions and control, auctions promise an efficient allocation of resources. However, a dedicated design of auctions for just-in-time deliveries, which can be incorporated into the design of an IT artifact, is still lacking. We contribute a linear and a quadratic multi-attribute scoring rule for an automated execution by software. We evaluate the artifact in a simulation experiment and reveal the effects of the scoring rules for just-in-time deliveries. Our results provide evidence that the artifact effectively coordinates just-in-time deliveries, which also holds when considering one additional side constraint

A Survey of the Application of Machine Learning in Decision Support Systems

Machine learning is a useful technology for decision support systems and assumes greater importance in research and practice. Whilst much of the work focuses technical implementations and the adaption of machine learning algorithms to application domains, the factors of machine learning design affecting the usefulness of decision support are still understudied. To enhance the understanding of machine learning and its use in decision support systems, we report the results of our content analysis of design-oriented research published between 1994 and 2013 in major Information Systems outlets. The findings suggest that the usefulness of machine learning for supporting decision-makers is dependent on the task, the phase of decision-making, and the applied technologies. We also report about the advantages and limitations of prior research, the applied evaluation methods and implications for future decision support research. Our findings suggest that future decision support research should shed more light on organizational and people-related evaluation criteria

Author Correction: Reprogramming triggers endogenous L1 and Alu retrotransposition in human induced pluripotent stem cells

This Article contains an error in the author affiliations. The correct affiliation for author Ruchi Shukla is ‘MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XU, UK’, and is not ‘Mater Research Institute – University of Queensland, TRI Building, Woolloongabba QLD 4102, Australia’