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Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes : A Simulation-Based Multi-Objective Approach

Authors :
Barrera Diaz, Carlos Alberto
Aslam, Tehseen
Ng, Amos H. C.
Barrera Diaz, Carlos Alberto
Aslam, Tehseen
Ng, Amos H. C.
Publication Year :
2021

Abstract

In today’s global and volatile market, manufacturing enterprises are subjected to intense global competition, increasingly shortened product lifecycles and increased product customization and tailoring while being pressured to maintain a high degree of cost-efficiency. As a consequence, production organizations are required to introduce more new product models and variants into existing production setups, leading to more frequent ramp-up and ramp-down scenarios when transitioning from an outgoing product to a new one. In order to cope with such as challenge, the setup of the production systems needs to shift towards reconfigurable manufacturing systems (RMS), making production capable of changing its function and capacity according to the product and customer demand. Consequently, this study presents a simulation-based multi-objective optimization approach for system re-configuration of multi-part flow lines subjected to scalable capacities, which addresses the assignment of the tasks to workstations and buffer allocation for simultaneously maximizing throughput and minimizing total buffer capacity to cope with fluctuating production volumes. To this extent, the results from the study demonstrate the benefits that decision-makers could gain, particularly when they face trade-off decisions inherent in today’s manufacturing industry by adopting a Simulation-Based Multi-Objective Optimization (SMO) approach.<br />CC BY 4.0This work was partially supported by the Knowledge Foundation (KKS), Sweden, through the funding of the research profile VirtualFactories with Knowledge-Driven Optimization (VF-KDO) (2018-2026).<br />VF-KDO

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1312837342
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1109.ACCESS.2021.3122239