Field-synchronized Digital Twin framework for production scheduling with uncertainty
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Field-synchronized Digital Twin framework for production scheduling with uncertainty Elisa Negri1
· Vibhor Pandhare2
· Laura Cattaneo1
· Jaskaran Singh3
· Marco Macchi1
· Jay Lee2
Received: 21 April 2020 / Accepted: 29 September 2020 © The Author(s) 2020
Abstract Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possibility to grasp the current operating conditions of the industrial equipment in real-time and take them into account when elaborating the best production schedules. To this end, the article proposes a proof-of-concept of a simheuristics framework for robust scheduling applied to a Flow Shop Scheduling Problem. The framework is composed of genetic algorithms for schedule optimization and discrete event simulation and is synchronized with the field through a Digital Twin (DT) that employs an Equipment Prognostics and Health Management (EPHM) module. The contribution of the EPHM module inside the DT-based framework is the real time computation of the failure probability of the equipment, with data-driven statistical models that take sensor data from the field as input. The viability of the framework is demonstrated in a flow shop application in a laboratory environment. Keywords Digital Twin · Equipment health · Fault detection · Simheuristics · Robust scheduling · PHM · FSSP
Introduction Modern day industries need to compete for profitability and customer satisfaction in a challenging environment with ris-
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Elisa Negri [email protected] Vibhor Pandhare [email protected] Laura Cattaneo [email protected] Jaskaran Singh [email protected] Marco Macchi [email protected] Jay Lee [email protected]
1
Department of Management, Politecnico di Milano, Economics and Industrial Engineering, Milan, Italy
2
Mechanical and Materials Engineering, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, USA
3
Department of Mechanical Engineering, Thapar Institute of Engineering and Technology, Patiala, India
ing costs, attention to performance and reliability, operations safety, and others. Thus, industries are rapidly integrating emerging digital technologies that have ushered companies to a new era of industrial revolution called Industry 4.0 (Lee et al. 2013, 2015a; Oztemel and Gursev 2020; Shi et al. 2011; Wu et al. 2013; Xu et al. 2014; Yang et al. 2015; Zhang et al. 2014a, b). Industry 4.0 encompasses multiple evolving technology umbrellas, one of which is Cyber-Physical Systems (CPS). Pervasive sensor technologies, open and standardized communication protocols, and computational convenience have led to its development (Baheti and Gill 2011; Lee et al. 2015b; Leitão et al. 2016). CPS can be defined as a synergetic integration of the physical assets and their D
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