TY - JOUR
T1 - Refinement of weights using attribute support for multiple attribute decision making
AU - Zhang, Hengshan
AU - Zhou, Yimin
AU - Chen, Tianhua
AU - Hill, Richard
AU - Wang, Zhongmin
AU - Chen, Yanping
N1 - Funding Information:
This work is sponsored under the National Science Foundation of China under Grant Nos. 61973296 , 61702414 , 61373116 , and 61602369. This work is also supported under the Shenzhen Basic Research Program Ref.JCYJ20170818153635759 and Science and Technology Planning Project of Guangdong Province Ref.2017B010117009; Science and Technology Project in Shaanxi Province of China (Program No. 2019ZDLGY07-08); Science and Technology Co-ordination and Innovation Project (Program No. 2016KTZDGY04-01); Xi’an Project for College Talent Providing Services to Enterprise (No. GXYD17.15). This work is supported under the Special Fund Construction Project for Key Disciplines of the colleges and universities in Shaanxi Province.
Publisher Copyright:
© 2021
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/9/8
Y1 - 2021/9/8
N2 - A number of approaches have been proposed to determine the weights for multiple attribute decision making. However, the resultant weights are usually assumed to be fixed, making it lack of tolerance to accommodate variation if the patterns of the subsequent data are subject to change. This article proposes a method to facilitate the adjustment of attribute weights, which accommodates a number of relevant characteristics. A model is first constructed that is able to express the requirements of a particular application. The concept of attribute support and consensus are then proposed for subsequent weight modification. A full algorithm is finally presented for the attribute weight adjustment. The effectiveness of the proposed method is validated by way of a case study in the tax credit domain with a sensitivity analysis of the method further evaluated.
AB - A number of approaches have been proposed to determine the weights for multiple attribute decision making. However, the resultant weights are usually assumed to be fixed, making it lack of tolerance to accommodate variation if the patterns of the subsequent data are subject to change. This article proposes a method to facilitate the adjustment of attribute weights, which accommodates a number of relevant characteristics. A model is first constructed that is able to express the requirements of a particular application. The concept of attribute support and consensus are then proposed for subsequent weight modification. A full algorithm is finally presented for the attribute weight adjustment. The effectiveness of the proposed method is validated by way of a case study in the tax credit domain with a sensitivity analysis of the method further evaluated.
KW - Attibute Weight Ajustment
KW - Attribute Support
KW - Strategic Weight Manipulation
KW - Consensus
UR - http://www.scopus.com/inward/record.url?scp=85114852606&partnerID=8YFLogxK
U2 - 10.1016/j.jocs.2021.101440
DO - 10.1016/j.jocs.2021.101440
M3 - Article
VL - 54
SP - 26
EP - 39
JO - Journal of Computational Science
JF - Journal of Computational Science
SN - 1877-7503
M1 - 101440
ER -