Archives > Volume 18 | Number 1 | March 2023 > pp 116–129
Advances in Production Engineering & Management
Volume 18 | Number 1 | March 2023 | pp 116–129
A NSGA-II based approach for multi-objective optimization of a reconfigurable manufacturing transfer line supported by Digital Twin: A case study
Ali, M.A.; Alarjani, A.; Mumtaz, M.A.
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A B S T R A C T
In response to the wide range of customer demands, the concept of reconfigurable manufacturing systems (RMS) was introduced in the industrial sector. RMS enables producers to meet varying volumes of demand over varying time periods by swiftly adjusting its production capacity and functionality within a part family in response to abrupt market changes. In these circumstances, RMS are made to swiftly reconfigure their Reconfigurable Machine Tools (RMTs). RMTs are designed to have a variety of configurations that may be conditionally chosen and reconfigured in accordance with specific performance goals. However, the reconfiguration process is not an easy process, which entails optimization of several objectives and many of which are inherently conflictual. As a result, it necessitates real-time monitoring of the RMS's condition, which may be achieved by digital twinning, or the real-time capture of system data. The concept of using a digital replica of a physical system to provide real-time optimization is known as digital twin. This work considered a case study of discrete parts manufacturing on a reconfigurable single manufacturing transfer line (SMTL). Six manufacturing operations are required to be performed on the parts at six production stages. This work uses the Digital Twin (DT) based approach to assist a discrete multi-objective optimization problem for a reconfigurable manufacturing transfer line. This multi-objective optimization problem consists of four objective functions which is illustrated by using DT-based Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The innovative aspect of the current study is the use of a DT-based framework for RMS reconfiguration to produce the best optimum solutions. The produced real-time solutions will be of great assistance to the decision maker in selecting the appropriate real-time optimal solutions for reconfigurable manufacturing transfer lines.
A R T I C L E I N F O
Keywords • Reconfigurable manufacturing system; Digital twin; Multi-objective optimization; Evolutionary computation; Evolutionary algorithm; Non-dominated sorting genetic algorithm-II (NSGA-II); Reconfigurable machine tools; Smart manufacturing
Corresponding author • Ali, M.A.
Article history • Received 14 March 2023, Revised 10 April 2023, Accepted 17 April 2023
Published on-line • 29 April 2022
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