A simulation-based scheduling system for real-time optimization and decision making support

Frantzén, Marcus, Ng, Amos H.C. and Moore, Philip (2011) A simulation-based scheduling system for real-time optimization and decision making support. Robotics and Computer-Integrated Manufacturing, 27 (4). pp. 696-705. ISSN 07365845

Abstract / Summary

This journal paper reports an international collaborative research study undertaken between the Volvo Corporation, the University of Skovde (Sweden) and De Montfort University (UK), prior to the author joining Falmouth University. The research programme was commissioned and partially funded by the Knowledge Foundation (KK Stiftelsen) of Sweden., The paper relates the development of a pioneering simulation-based optimisation system applicable to the scheduling of complex manufacturing lines to support real-time decision making. The complex nature and real-time response needs of large manufacturing lines dictate the need to not only generate robust and adaptive production schedules, but to be able to undertake real-time rescheduling to effectively cope with uncertainties in the production environment., The research delivered a novel approach which facilitates real time scheduling, OPTIMISE; the system has been successfully deployed at the Volvo Car power-train manufacturing plant in Sweden on one of the largest and most complex manufacturing lines involving highly-automated machining stations and manual stations. The results of the initial trials in Volvo Cars indicate that such a novel scheduling approach can be readily applied in complex production to improve line throughput capacity and simultaneously support real-time decision making. The trial demonstrated production time savings of some 10% in comparison to normal production runs., The research study resulted in Volvo investing c. €400,000 in related manpower and IT systems. Volvo Cars now intends to apply this approach to other manufacturing production lines and is undertaking further related research (e.g. BlixtSim a follow on €380,000 research project).

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