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

被引:13
|
作者
Diaz, Carlos Alberto Barrera [1 ]
Aslam, Tehseen [1 ]
Ng, Amos H. C. [1 ,2 ]
机构
[1] Univ Skovde, Sch Engn Sci, Div Intelligent Prod Syst, S-54128 Skovde, Sweden
[2] Uppsala Univ, Dept Civil & Ind Engn, Div Ind Engn & Management, S-75121 Uppsala, Sweden
来源
IEEE ACCESS | 2021年 / 9卷 / 09期
关键词
Production; Costs; Task analysis; Manufacturing systems; Optimization; Resource management; Manufacturing industries; Multi-objective optimization; reconfigurable manufacturing systems; simulation-based optimization; genetic algorithm; OPTIMAL CONFIGURATION SELECTION; FLOW-LINE CONFIGURATIONS; PROCESS PLAN GENERATION; GENETIC ALGORITHM; OPTIMIZATION APPROACH; DESIGN; PART; RMS; MODEL;
D O I
10.1109/ACCESS.2021.3122239
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:144195 / 144210
页数:16
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