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Agent-based monitoring service for management of disruptive events in supply chains
Affiliation:1. Cracow University of Technology, Poland;2. National College of Ireland, Dublin, Ireland;3. Aristotle University of Thessaloniki, Greece;1. Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Eng., Univ. of Tehran, Tehran, Iran;2. Medical Image Analysis Laboratory, Radiology Department, Henry Ford Hospital, Detroit, MI, USA;1. University of Liverpool, Laureate Online Education, UK;7. John Molson School of Business, Concordia University, Montreal;1. School of Property, Construction and Project Management, RMIT Univ., Melbourne, VIC, Australia;2. School of Business IT and Logistics, RMIT Univ., Melbourne, VIC, Australia;3. Department of Architecture and Civil Engineering, City Univ. of Kong Kong, Kowloon, Hong Kong;4. School of Business, State Univ. of New York at New Paltz, United States
Abstract:Schedules of supply chains are generated with buffers to absorb the effect of disruptive events that could occur during their execution. Schedules can be systematically repaired through specific modifications within buffers by using appropriate decision models that consider the distributed nature of a supply chain. To this aim, information of disruptive events at occurrence or in advance allows decision models to make better decisions. To detect and predict disruptive events along a schedule execution, a service-oriented monitoring subsystem that uses a reference model for defining monitoring models was proposed. This subsystem offers services for collecting execution data of a schedule and environment data, and assessing them to detect/anticipate disruptive events. Because of the distributed nature and the complexity of these services functionalities, this paper presents an agent-based approach for their implementation. This technology allows dealing with supply chain monitoring by structuring monitoring subsystem functionalities as a set of autonomous entities. These entities are able to perform tailored plans created at execution time to concurrently monitor different schedules. A case study is described to try out the implemented prototype system.
Keywords:Multiagent system  Disruptive event  Supply chain  Monitoring  Model-driven development
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