Abstract | ||
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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. |
Year | DOI | Venue |
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2021 | 10.1007/s10845-020-01685-9 | JOURNAL OF INTELLIGENT MANUFACTURING |
Keywords | DocType | Volume |
Digital Twin, Equipment health, Fault detection, Simheuristics, Robust scheduling, PHM, FSSP | Journal | 32 |
Issue | ISSN | Citations |
4 | 0956-5515 | 1 |
PageRank | References | Authors |
0.37 | 0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Elisa Negri | 1 | 44 | 4.81 |
Vibhor Pandhare | 2 | 1 | 0.37 |
Laura Cattaneo | 3 | 1 | 1.39 |
Jaskaran Singh | 4 | 1 | 0.37 |
Marco Macchi | 5 | 36 | 10.56 |
Jay Lee | 6 | 46 | 6.14 |