Reference point based evolutionary multi-objective optimization with dynamic resampling for production systems improvement

被引:4
作者
Ng A.H.C. [1 ]
Siegmund F. [1 ]
Deb K. [2 ]
机构
[1] University of Skövde, Skövde
[2] Michigan State University, East Lansing, MI
关键词
Dynamic resampling; Multi-criteria decision making; Multi-objective optimization; Production systems improvement;
D O I
10.1108/JSIT-10-2017-0084
中图分类号
学科分类号
摘要
Purpose: Stochastic simulation is a popular tool among practitioners and researchers alike for quantitative analysis of systems. Recent advancement in research on formulating production systems improvement problems into multi-objective optimizations has provided the possibility to predict the optimal trade-offs between improvement costs and system performance, before making the final decision for implementation. However, the fact that stochastic simulations rely on running a large number of replications to cope with the randomness and obtain some accurate statistical estimates of the system outputs, has posed a serious issue for using this kind of multi-objective optimization in practice, especially with complex models. Therefore, the purpose of this study is to investigate the performance enhancements of a reference point based evolutionary multi-objective optimization algorithm in practical production systems improvement problems, when combined with various dynamic re-sampling mechanisms. Design/methodology/approach: Many algorithms consider the preferences of decision makers to converge to optimal trade-off solutions faster. There also exist advanced dynamic resampling procedures to avoid wasting a multitude of simulation replications to non-optimal solutions. However, very few attempts have been made to study the advantages of combining these two approaches to further enhance the performance of computationally expensive optimizations for complex production systems. Therefore, this paper proposes some combinations of preference-based guided search with dynamic resampling mechanisms into an evolutionary multi-objective optimization algorithm to lower both the computational cost in re-sampling and the total number of simulation evaluations. Findings: This paper shows the performance enhancements of the reference-point based algorithm, R-NSGA-II, when augmented with three different dynamic resampling mechanisms with increasing degrees of statistical sophistication, namely, time-based, distance-rank and optimal computing buffer allocation, when applied to two real-world production system improvement studies. The results have shown that the more stochasticity that the simulation models exert, the more the statistically advanced dynamic resampling mechanisms could significantly enhance the performance of the optimization process. Originality/value: Contributions of this paper include combining decision makers’ preferences and dynamic resampling procedures; performance evaluations on two real-world production system improvement studies and illustrating statistically advanced dynamic resampling mechanism is needed for noisy models. © 2018, Emerald Publishing Limited.
引用
收藏
页码:489 / 512
页数:23
相关论文
共 50 条
[41]   RDS-NSGA-II: a memetic algorithm for reference point based multi-objective optimization [J].
Hernandez Mejia, Jesus Alejandro ;
Schutze, Oliver ;
Cuate, Oliver ;
Lara, Adriana ;
Deb, Kalyanmoy .
ENGINEERING OPTIMIZATION, 2017, 49 (05) :828-845
[42]   Evolutionary multi-objective optimization in power systems:: State-of-the-art [J].
Rivas-Davalos, F. ;
Moreno-Goytia, E. ;
Gutierrez-Alacaraz, G. ;
Tovar-Hernandez, J. .
2007 IEEE LAUSANNE POWERTECH, VOLS 1-5, 2007, :2093-2098
[43]   Multi-objective dynamic optimization of hybrid renewable energy systems [J].
Sharma, Reena ;
Kodamana, Hariprasad ;
Ramteke, Manojkumar .
CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION, 2022, 180
[44]   Multi-Objective Framework for Dynamic Optimization of OFDMA Cellular Systems [J].
Chandhar, Prabhu ;
Das, Suvra Sekhar .
IEEE ACCESS, 2016, 4 :1889-1914
[45]   Multi-objective dynamic optimization of hybrid renewable energy systems [J].
Sharma, Reena ;
Kodamana, Hariprasad ;
Ramteke, Manojkumar .
CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION, 2022, 170
[46]   Evolutionary methods for multi-objective portfolio optimization [J].
Radiukyniene, I. ;
Zilinskas, A. .
WORLD CONGRESS ON ENGINEERING 2008, VOLS I-II, 2008, :1155-+
[47]   An evolutionary multi-objective optimization system for earthworks [J].
Parente, M. ;
Cortez, P. ;
Gomes Correia, A. .
EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (19) :6674-6685
[48]   Illustration of fairness in evolutionary multi-objective optimization [J].
Friedrich, Tobias ;
Horoba, Christian ;
Neumann, Frank .
THEORETICAL COMPUTER SCIENCE, 2011, 412 (17) :1546-1556
[49]   Research on evolutionary multi-objective optimization algorithms [J].
Gong, Mao-Guo ;
Jiao, Li-Cheng ;
Yang, Dong-Dong ;
Ma, Wen-Ping .
Ruan Jian Xue Bao/Journal of Software, 2009, 20 (02) :271-289
[50]   A study on multiform multi-objective evolutionary optimization [J].
Liangjie Zhang ;
Yuling Xie ;
Jianjun Chen ;
Liang Feng ;
Chao Chen ;
Kai Liu .
Memetic Computing, 2021, 13 :307-318