학술논문

Deep Learning Enabling Digital Twin Applications in Production Scheduling: Case of Flexible Job Shop Manufacturing Environment
Document Type
Conference
Source
2023 Winter Simulation Conference (WSC) Simulation Conference (WSC), 2023 Winter. :2148-2159 Dec, 2023
Subject
Engineering Profession
General Topics for Engineers
Transportation
Measurement
Deep learning
Job shop scheduling
Fluctuations
Processor scheduling
Computational modeling
Estimation
Production
Digital twins
Manufacturing systems
Language
ISSN
1558-4305
Abstract
Digital twin-based Production Scheduling (DTPS) is a process in which a digital model replicates a manufacturing system, known as a "Digital Twin (DT)". DT is essentially a virtual representation of physical equipment and processes that are connected to the physical environment using an online data-sharing infrastructure within the Manufacturing Execution System (MES). In the case of reactive scheduling, DT is used to detect fluctuations in the scheduling plan and execute rescheduling plans. In proactive scheduling, it is used to simulate different production scenarios and optimize future states of production operations. Replicating detailed simulation models in most PS cases is highly computationally intensive, which negates against the main goal of DT (online decision making). Thus, this research aims to examine the possibility of using data-driven models within the DT of a Flexible Job Shop (FJS) production environment aiming to provide online estimations of PS metrics enabling DT-based reactive/proactive scheduling.