Proceedings of the 2018 Winter Simulation Conference M. Rabe, A.A. Juan, N. Mustafee, A. Skoogh, S. Jain, and B. Johansson, eds. SYMBIOTIC SIMULATION SYSTEM: HYBRID SYSTEMS MODEL MEETS BIG DATA ANALYTICS Bhakti Stephan Onggo Navonil Mustafee Andi Smart Trinity Business School The Business School Trinity College Dublin University of Exeter College Green Streatham Court, Rennes Drive Dublin 2, IRELAND Exeter EX4 4ST, UK Angel A. Juan Owen Molloy IN3 – Computer Science Department College of Engineering and Informatics Universitat Oberta de Catalunya National University of Ireland Galway Rambla del Poblenou, 156 University Road Barcelona, 08018, SPAIN Galway City, IRELAND ABSTRACT Symbiotic simulation is one of Industry 4.0 technologies that enables interaction between a physical system and the simulation model that represents it as its digital twin. Symbiotic simulation is designed to support decision making at the operational levels by making use of real- or near real- time data that is generated by the physical system, which is used as an input to the simulation model. From the modeling perspective, a symbiotic simulation system comprises a hybrid systems model that combines simulation, optimization and machine learning models as well as a data acquisition module and an actuator. The actuator is needed when the symbiotic simulation system is designed to directly control the physical system without human intervention. This paper reviews the components of a symbiotic simulation system from the perspective of hybrid systems modeling and highlights research questions needed to advance symbiotic simulation study. 1 INTRODUCTION The use of a hybrid modeling approach to solving complex real-world problems has gained popularity among simulation researchers and practitioners (Mustafee et al. 2017; Eldabi et al. 2016). This stems from the realization that a single modeling approach is bound by its underlying methodological constraints, and with systems becoming more complex and multi-faceted, there is now a need to use a combination of analytical approaches to best represent the system of interest. In simulation literature, there are two types of hybrid modeling approaches. The first and most common one is hybrid simulation, which combines different simulation approaches (e.g. Discrete-Event Simulation, System Dynamics and Agent-Based Simulation) into one model (e.g. Viana et al. 2014; Djanatliev and German 2013). The second approach is a hybrid systems modeling and simulation approach (Powell and Mustafee 2016) which combines simulation with compatible modeling approaches including system thinking models (e.g. soft system methodology, cognitive mapping) and analytics models (e.g. mathematical model). Combining analytics models and simulation is not new (see Shanthikumar and Sargent 1983). Andreas Tolk in Mustafee et al. (2017) states that a useful hybrid model combines the characteristics of its components into something more useful (in comparison to using any of its components alone). 1358 978-1-5386-6572-5/18/$31.00 ©2018 IEEE
SYMBIOTIC SIMULATION SYSTEM: HYBRID SYSTEMS MODEL MEETS BIG DATA ANALYTICS
Apr 25, 2023
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