Coupling order release methods with autonomous control methods – an assessment of potentials by literature review and discrete event simulation

[EN] Production planning and control faces increasing uncertainty, dynamics and complexity. Autonomous control methods proved themselves as a promising approach for coping with these challenges. However, there is a lack of knowledge regarding the interaction between autonomous control and precedent functions of production planning and control. In particular, up to now previous research has paid no attention to the influence of order release methods on the efficiency of autonomous control methods. Thereby, many researchers over the last decades provided evidence that the order release function has great influence on the logistic objective achievement in conventional production systems. Therefore, this paper examines the influence of order release methods on the efficiency of autonomous control methods by both theoretic evaluation and discrete event simulation. The simulation results indicate an overall high influence. Moreover, the logistic performance differs considerably depending on the implemented order release methods and the combinations of order release methods with autonomous control methods. The findings highlight demand for further research in this field.This research was funded by the German Research Foundation (DFG) under the reference number SCHO 540/26-1 “Methods for the interlinking of central planning and autonomous control in production”.Grundstein, S.; Schukraft, S.; Scholz-Reiter, B.; Freitag, M. (2015). Coupling order release methods with autonomous control methods – an assessment of potentials by literature review and discrete event simulation. International Journal of Production Management and Engineering. 3(1):43-56. https://doi.org/10.4995/ijpme.2015.3199SWORD435631Park, H.-S., & Tran, N.-H. (2012). An autonomous manufacturing system based on swarm of cognitive agents. Journal of Manufacturing Systems, 31(3), 337-348. doi:10.1016/j.jmsy.2012.05.002Pinedo, M. L. (2008). Scheduling. theory, algorithms and systems. New York, USA: Springer.Rekersbrink, H. (2012). Methoden zum selbststeuernden Routing autonomer logistischer Objekte. (doctoral disserta-tion). Universität Bremen, Bremen, Germany.Scholz-Reiter, B., Böse, F., Jagalski, T., & Windt, K. (2007a). Selbststeuerung in der betrieblichen Praxis. Ein Framework zur Auswahl der passenden Selbststeuerungsstrategie. Industrie Management, 23(3), 7-10.Scholz-Reiter, B., Freitag, M., de Beer, C., & Jagalski, T. (2006). The influence of production network's complexity on the performance of autonomous control methods. Proceedings of the 5th CIRP International Seminar on Computation in Manufacturing engineering, 317-320.Scholz-Reiter, B., Freitag, M., de Beer, C., & Jagalski, T. (2005b). Modelling and Analysis of Autonomous Shop Floor Control. Proceedings of 38th CIRP International Seminar on Manufacturing Systems, 16-18.Scholz-Reiter, B., & Scharke, H. (2000). Reaktive Planung. Industrie Management, 16(2), 21-26.Weng, M. X., Wu, Z., Qi, G., & Zheng, L. (2008). Multi-agent-based workload control for make-to-order manufacturing. International Journal of Production Research, 46(8), 2197-2213. doi:10.1080/00207540600969758Westphal, J. R. (2001). Komplexitätsmanagement in der Produktionslogistik - ein Ansatz zur flussorientierten Gestal-tung und Lenkung heterogener Produktionssysteme. Wiesbaden, Germany: Deutscher Universitäts Verlag.Wiendahl, H.-P. (Ed.). (1991). Anwendung der belastungsorientierten Auftragsfreigabe. Munich, Germany: Carl Hanser.Wiendahl, H.-P. (1997). Fertigungsregelung. Logistische Beherrschung von Fertigungsabläufen auf Basis des Trich-termodells. Munich, Germany: Carl Hanser.Wiendahl, H.-P. (Ed.). (2005). Betriebsorganisation für Ingenieure. Munich: Hanser.Wyssusek, B. (1999). Grundlagen der Systemanalyse. In Krallmann, H., Frank, H., & Gronau, N. (Eds.), Sytemanalyse im Unternehmen (pp. 19-43). Munich, Germany: Oldenbourg

Agent-based distributed manufacturing scheduling: an ontological approach

The purpose of this paper is the need for self-sequencing operation plans in autonomous agents. These allow resolution of combinatorial optimisation of a global schedule, which consists of the fixed process plan jobs and which requires operations offered by manufacturers. The proposed agent-based approach was adapted from the bio-inspired metaheuristic- particle swarm optimisation (PSO), where agents move towards the schedule with the best global makespan. The research has achieved a novel ontology-based optimisation algorithm to allow agents to schedule operations whilst cutting down on the duration of the computational analysis, as well as improving the performance extensibility amongst others. The novelty of the research is evidenced in the development of a synchronised data sharing system allowing better decision-making resources with intrinsic manufacturing intelligence. The multi-agent platform is built upon the Java Agent Development Environment (JADE) framework. The operation research case studies were used as benchmarks for the evaluation of the proposed model. The presented approach not only showed a practical use case of a decentralised manufacturing system, but also demonstrated near optimal makespans compared to the operational research benchmarks