Improving the remote scheduling of distributed production with process statistics and AI techniques》.pdfVIP

Simulation Modelling Practice and Theory 15 (2007) 175–184 /locate/simpat Improving the remote scheduling of distributed production with process statistics and AI techniques Alessandra Orsoni a,*, Romeo Bandinelli b a Senior Lecturer, Kingston University, School of Business Information Management, Kingston Hill, Kingston upon Thames, Surrey KT2 7LB, UK b Research Associate, Universita’ di Firenze, Dipartimento di Energetica ‘‘Sergio Stecco’’, Sezione Impianti e Tecnologie Industriali, Via Cesare Lombroso 6/17 quinto piano, 50134 Florence, Italy Available online 2 November 2006 Abstract Stochastic events, such as rush orders, stock-out events, and local failures have an important impact on the performance of distributed production, but they are difficult to anticipate and account for when scheduling production activities. Process statistics and artificial intelligence techniques can provide this knowledge to effectively time synchronization events among the simulation and scheduling federates of a same distributed architecture. Measurable benefits include reduced communi- cation delays and, thus, improved responsiveness of the system to changes in production and new scheduling needs, as they arise. Comparative results on the productivity of actual industrial systems are proposed and discussed in the paper. 2006 Elsevier B.V. All rights reserved. Keywords: On-line production scheduling; Coordinated production processes; AI techniques; Modeling of failure events; XLA-RTI architecture 1. Introduction Distributed simulation effectively supports the management of coordinated production processes. Industrial