Real-time Event Processing for Smart Logistics Networks

In times of aggravating competition in the logistics industry, organizations need to distinguish themselves and enhance their logistics performance. Responsiveness to critical situations and deviations from plan serves exactly this goal as it may lead to measurable improvements in business performance. However, meaningful data needed to exploit and enhance this capability may reside in several widespread sources. Identifying and utilizing such sources can be pivotal on an organization’s path to survival and success on the market. Using smarter approaches to logistics is such a path. Cyber-physical systems and complex event processing may be adequate technological means to aspire for the transition towards smart logistics systems and processes.

[1]  Francesca Saglietti,et al.  Model-Based Design and Testing of Decisional Autonomy and Cooperation in Cyber-Physical Systems , 2015, 2015 41st Euromicro Conference on Software Engineering and Advanced Applications.

[2]  Erik Hofmann,et al.  Industry 4.0 and the current status as well as future prospects on logistics , 2017, Comput. Ind..

[3]  Vikrambhai S. Sorathia,et al.  The process-oriented event model (PoEM): a conceptual model for industrial events , 2014, DEBS '14.

[4]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[5]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[6]  Michael Schreckenberg,et al.  A cellular automaton model for freeway traffic , 1992 .

[7]  Bernd Noche,et al.  Analyzing the potential of Future-Internet-based logistics control tower solutions in warehouses , 2014, Proceedings of 2014 IEEE International Conference on Service Operations and Logistics, and Informatics.

[8]  Cyril Alias,et al.  Investigating into the Prevalence of Complex Event Processing and Predictive Analytics in the Transportation and Logistics Sector: Initial Findings From Scientific Literature , 2016, MCIS.

[9]  Carolyn L. Talcott,et al.  Cyber-Physical Systems and Events , 2008, Software-Intensive Systems and New Computing Paradigms.

[10]  Bernd Noche,et al.  Enabling Improved Process Control Opportunities by Means of Logistics Control Towers and Vision-Based Monitoring , 2014 .

[11]  Bernd Noche,et al.  Applying Novel Future-Internet-Based Supply Chain Control Towers to the Transport and Logistics Domain , 2014 .

[12]  Bernd Noche,et al.  A System of Multi-Sensor Fusion for Activity Monitoring of Industrial Trucks in Logistics Warehouses , 2015 .

[13]  Edward A. Lee.  CPS foundations , 2010, Design Automation Conference.

[14]  Bernd Scholz-Reiter,et al.  Autonomous Logistic Processes - New Demands and First Approaches - , 2004 .

[15]  Bernd Noche,et al.  Comparing sensor-based and camera-based approaches to recognizing the occupancy status of the load handling device of forklift trucks , 2016 .

[16]  Elgar Fleisch,et al.  Die betriebswirtschaftliche Vision des Internets der Dinge , 2005 .

[17]  Friedemann Mattern,et al.  Das Internet der Dinge: Ubiquitous Computing und RFID in der Praxis:Visionen, Technologien, Anwendungen, Handlungsanleitungen , 2005 .

[18]  Martin Treiber,et al.  Verkehrsdynamik und -simulation - Daten, Modelle und Anwendungen der Verkehrsflussdynamik , 2010 .

[19]  Paulo Leitão,et al.  Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges , 2016, Comput. Ind..

[20]  Gordon F. Newell,et al.  A simplified car-following theory: a lower order model , 2002 .

[21]  Dieter Uckelmann,et al.  A Definition Approach to Smart Logistics , 2008, NEW2AN.

[22]  Farhad Arbab,et al.  Soft Agents: Exploring Soft Constraints to Model Robust Adaptive Distributed Cyber-Physical Agent Systems , 2015, Software, Services, and Systems.