Abstract
In this paper, a model-based routing approach for flexible manufacturing systems (FMS) with alternative routes for the work pieces is proposed. For each work piece, an individual task has to be accomplished, which consists of several processing steps. Each processing step can be executed on alternative working stations of the FMS. The proposed routing method employs a model of the conveying system to find energy efficient and fast routes for the respective work pieces. The conveying system model is based on a directed graph, where the individual conveyors are modeled as weighted edges. It can be straightforwardly applied to several types of FMS by adjusting the application-dependent parameters. Efficient computation of the fastest route through the conveying system is accomplished by means of dynamic programming, i. e., by integration of Dijkstra’s algorithm in a dynamic programming framework, which is based on the proposed conveying system model. Additional consideration of energy efficiency aspects leads to a Mixed Integer Quadratically Constraint Program (MIQCP), which is solved by substitution of Dijkstra’s algorithm by a branch and bound method. Experimental results for an application scenario, where the energy efficient routing method is applied to route work pieces between the working stations of an FMS, lead to 20 % reduction of energy consumption on average.
Zusammenfassung
In dem Artikel wird ein Modell-basierter Routing-Ansatz für flexible Fertigungssysteme (FMS) eingeführt. Die Herausforderung besteht darin, dass für jedes in dem FMS hergestellte Werkstück ein spezifischer Auftrag abgearbeitet werden soll, welcher aus mehreren Verarbeitungsschritten besteht. Ein entsprechender Verarbeitungsschritt kann alternativ auf einem von mehreren Modulen des FMS abgearbeitet werden. Die vorgeschlagene Routing-Methode verwendet ein Modell des Transportsystems, welches auf einem gerichteten Graph basiert, um für die einzelnen Werkstücke energieeffiziente und schnelle Wege durch das FMS zu ermitteln. Durch die Anpassung anwendungsspezifischer Parameter ist es möglich, das zugrunde liegende Modell für unterschiedliche FMS anzuwenden. Die effiziente Berechnung des kürzesten Weges erfolgt mittels dynamischer Programmierung. Die weitere Berücksichtigung des Energieverbrauchs bei der Ermittlung des optimalen Weges führt auf ein gemischt-ganzzahliges Optimierungsproblem, welches in den Ansatz der dynamischen Programmierung integriert werden kann. Für ein typisches Anwendungsszenario konnte mittels der vorgeschlagenen Routing-Methode eine durchschnittliche Reduktion des Energieverbrauchs um 20 % erzielt werden.
About the authors
Stefan Windmann has been employed as a postdoctoral researcher at Fraunhofer IOSB-INA in Lemgo since 2012. He studied electrical engineering and technical computer sciences at the University of Paderborn where he finished his PhD in 2008. From 2008 to 2012 he worked as a software developer. Currently he is working on hard- and software solutions, machine learning algorithms and optimization methods for automated production systems.
Kaja Balzereit received a Bachelor’s degree in Applied Mathematics and a Master’s degree in Optimization and Simulation, both from the Bielefeld University of Applied Sciences. Since 2017 she has been employed as a researcher at the Fraunhofer Industrial Automation branch (IOSB-INA) in Lemgo, Germany. Currently she is working on machine learning for anomaly detection in production systems, and on optimization of industrial automation systems.
Oliver Niggemann received his PhD in Computer Science from the University of Paderborn. For 7 years he worked in various positions in industry, both in development and in leading management positions. In 2008 he accepted the professorship for technical computer science at the University of Applied Sciences OWL in Lemgo, Germany. He is an executive board member of the Institute of Industrial IT (inIT). Since 2009 he has been also the deputy director of the Fraunhofer IOSB-INA in Lemgo. He is heading the graduate school Intelligent Systems for Automation (ISA) and he is the scientific director of the Graduate Center GZ.OWL in Lemgo. His research interests comprise methods and applications for Artificial Intelligence in production systems.
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