Proactive and dynamic event-driven disruption management in the manufacturing domain

Within the last few years, event processing has been gaining a lot of attention as it represents a powerful technology to establish a real-time monitoring and situation detection. The real-time detection of situations allows a timely reaction and helps to reduce the damage made by harmful situations or increase the benefit of opportunities. Through the mind shift of companies from reactivity towards proactivity, also the used systems have to carry out a shift. Instead of just detecting situations, we search for possibilities to predict them and even go one step further and recommend a suitable reaction with the smallest possible impact on the production process as a whole. In this paper we introduce our concept of a proactive and dynamic event-driven disruption management system.

[1]  James O'Kane,et al.  A knowledge-based system for reactive scheduling decision-making in FMS , 2000, J. Intell. Manuf..

[2]  Allan Larsen,et al.  Disruption management - operations research between planning and execution , 2001 .

[3]  Bernhard Seeger,et al.  Anomaly management using complex event processing: extending data base technology paper , 2013, EDBT '13.

[4]  Andreas Metzger,et al.  Predictive Monitoring of Heterogeneous Service-Oriented Business Networks: The Transport and Logistics Case , 2012, 2012 Annual SRII Global Conference.

[5]  Xiangtong Qi,et al.  Disruption management in production planning , 2005 .

[6]  Opher Etzion,et al.  Towards proactive event-driven computing , 2011, DEBS '11.

[7]  Opher Etzion,et al.  A basic model for proactive event-driven computing , 2012, DEBS.

[8]  J. Christopher Beck,et al.  Slack-based Techniques for Robust Schedules , 2014 .

[9]  Matthias Weidlich,et al.  Event Recognition Challenges and Techniques , 2014, ACM Trans. Internet Techn..

[10]  James C. Bean,et al.  Matchup Scheduling with Multiple Resources, Release Dates and Disruptions , 1991, Oper. Res..

[11]  Panagiotis Kouvelis,et al.  Robust scheduling to hedge against processing time uncertainty in single-stage production , 1995 .

[12]  Piet Demeester,et al.  Network Recovery: Protection and Restoration of Optical, SONET-SDH, IP, and MPLS , 2004 .

[13]  Douglas C. Schmidt,et al.  A pattern language for distributed computing , 2007 .

[14]  John Lygeros,et al.  Scalable Proactive Event-Driven Decision Making , 2014, IEEE Technology and Society Magazine.

[15]  Willy Herroelen,et al.  Robust and reactive project scheduling: a review and classification of procedures , 2004 .

[16]  Frank Buschmann,et al.  Pattern-Oriented Software Architecture, a Pattern Language for Distributed Computing , 2007 .

[17]  Sanjay Mehta,et al.  Predictable scheduling of a single machine subject to breakdowns , 1999, Int. J. Comput. Integr. Manuf..

[18]  George L. Vairaktarakis,et al.  Robust scheduling of a two-machine flow shop with uncertain processing times , 2000 .

[19]  Damien Trentesaux,et al.  Scheduling under uncertainty: Survey and research directions , 2014, 2014 International Conference on Advanced Logistics and Transport (ICALT).

[20]  Piotr Cholda,et al.  Network Recovery, Protection and Restoration of Optical, SONET-SDH, IP, and MPLS [Book Review] , 2005, IEEE Communications Magazine.

[21]  F. Roubellat,et al.  A new method for workshop real time scheduling , 1996 .

[22]  Hermann Kopetz,et al.  Fault tolerance, principles and practice , 1990 .

[23]  Reha Uzsoy,et al.  Predictable scheduling of a single machine with breakdowns and sensitive jobs , 1999 .

[24]  Andreas Metzger,et al.  Proactive event processing in action: a case study on the proactive management of transport processes (industry article) , 2013, DEBS '13.

[25]  Peter Mertens,et al.  Combining knowledge-based systems and simulation to solve rescheduling problems , 1996, Decis. Support Syst..