Robust Optimization of Best-worst Multi-criteria Decision-making Method

Authors

  • Deqiang Qu Business School, University of Shanghai for Science and Technology, Shanghai, P.R. China
  • Zhong Wu Business School, University of Shanghai for Science and Technology, Shanghai, P.R. China
  • Shaojian Qu Business School, University of Shanghai for Science and Technology, Shanghai, P.R. China
  • Fan Zhang Business School, University of Shanghai for Science and Technology, Shanghai, P.R. China
  • Ping Li Business School, University of Shanghai for Science and Technology, Shanghai, P.R. China

DOI:

https://doi.org/10.13052/jwe1540-9589.19787

Keywords:

parameter uncertainty, robust counterpart, weight interval, quantile

Abstract

The Best-worst multi-criteria decision-making method can determine optimal weight value of each criteria. It uses two vectors for pairwise comparisons in multi-criteria decision-making problem. This paper improves the original method from the perspective of robust optimization. Four robust counterpart constraints instead of two linear constraints in original optimization model are proposed. The decision-making problem can divide into full consistent and non-full consistent problems by classifying parameter value. We can achieve a unique set of interval solution in full consistent decision-making problem. Non-full consistent problem can result in multiple sets of optimal interval solution. The result which we get from this method is more effective than the original method. Each criterion can achieve optimal weight interval value. Then we take quantile value of each interval as optimal weight. This is effectively illustrated in the numerical test at the end of the paper.

Downloads

Download data is not yet available.

Author Biographies

Deqiang Qu, Business School, University of Shanghai for Science and Technology, Shanghai, P.R. China

Deqiang Qu is a Ph.D. Student of the Business School at University of Shanghai for Science and Technology. His current research interests include: Decision making under uncertain.

Zhong Wu, Business School, University of Shanghai for Science and Technology, Shanghai, P.R. China

Zhong Wu received the Ph.D. degree from Tongji University, China. He is now the Vice President of University of Shanghai for Science and Technology. His current research interests include: Decision making under uncertain, game theory, and public management.

Shaojian Qu, Business School, University of Shanghai for Science and Technology, Shanghai, P.R. China

Shaojian Qu received the Ph.D. degree from Xi’an Jiaotong University, China, and was a Research Fellow at National University of Shanghai for 4.5 years. He is now a Professor of the Business School at University of Shanghai for Science and Technology. His current research interests include: Decision making, game theory, supply chain management, and portfolio.

Fan Zhang, Business School, University of Shanghai for Science and Technology, Shanghai, P.R. China

Fan Zhang is a master student at the University of Shanghai for Science and Technology since autumn 2017. His main research directions in school are crowdfunding, multi-cirteira decision and robust optimization. He has published two papers during the academic year.

Ping Li, Business School, University of Shanghai for Science and Technology, Shanghai, P.R. China

Ping Li is a master’s degree candidate in management science and engineering at the University of Shanghai for Science and Technology. His current research interests include: multi-attribute decision making, intuitionistic fuzzy theory and emergency management. During master’s studies, he has published two academic papers.

References

M.V. Kumar, M. Ramkumar, S. Digjoy. A Multi-Criteria Decision-Making Ap-proach for the Urban Renewal in Southern India. Sustainable Cities and Society, 42(2018), 471-481.

C.J. Li, M. Negnevitsky, X. Wang, W. Yue, X. Zou. Multi-criteria analysis of policies for implementing clean energy vehicles in China. Energy Policy, 34(2019), 826-840.

G. Tré, R. De Mol, A. Bronselaer. Handling veracity in multi-criteria decision-making: A multi-dimensional approach. Information Sciences, 65(2018), 541-554.

X. Gou, Z. Xu, H. Liao et al. Multiple Criteria Decision Making based on Distance and Similarity Measures under Double Hierarchy Hesitant Fuzzy Linguistic environment. Computers & Industrial Engineering, 35(2018), 516-530.

R. Jafer Best-Worst Multi-Criteria Decision-Making Method. Omega, 53(2015), 49-57.

Q. Mou, Z. Xu, H. Liao, et al. An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences, 374(2016), 224-239.

S. Guo, H. Zhao. Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 32(2017), 23-31.

H. Martin, G. Marc, W. Michael. A largest empty hypersphere metaheuristic for robust optimization with implementation uncertainty. Computers & Operations Research, 65(2019), 64-80.

M. Kaedi, C. Ahn. Robust Optimization Using Bayesian Optimization Algorithm: Early Detection of Non-robust Solutions. Applied Soft Computing, 121(2017), 1125-1138.

L. Marla, V. Vaze, C. Barnhart. Robust Optimization: Lessons Learned from Aircraft Routing. Social Science Electronic Publishing, 55(2017), 165-184.

R. Jafar. Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 44(2016), 126-130.

Downloads

Published

2020-12-24

How to Cite

Qu, D. ., Wu, Z., Qu, S. ., Zhang, F. ., & Li, P. . (2020). Robust Optimization of Best-worst Multi-criteria Decision-making Method. Journal of Web Engineering, 19(7-8), 1067–1088. https://doi.org/10.13052/jwe1540-9589.19787

Issue

2020: Vol 19 Iss 7-8

Section

Advanced Practice in Web Engineering in Asia