TY - JOUR
T1 - Assessing Normalization Techniques for Simple Additive Weighting Method
AU - Vafaei, Nazanin
AU - Ribeiro, Rita Almeida
AU - Camarinha-Matos, Luis M.
N1 - Funding Information:
This work was funded in part by the Center of Technology and Systems (CTS) and the Portuguese Founda for Science and Technology (FCT) through the Strategic Program UIDB/00066/2020 .
Publisher Copyright:
© 2021 The Authors. Published by Elsevier B.V.
PY - 2022/1/15
Y1 - 2022/1/15
N2 - One of the current topics of attention in data analysis is the selection of the best normalization technique in the aggregation process when using Multi-Criteria Decision Making (MCDM) methods for solving decision problems. This is particularly critical in complex collaborative decision-making systems dealing with a large variety of heterogeneous data sources. Using different normalization techniques may result in different rankings of alternatives. So, enhancing the accuracy of the final ranking of alternatives could be achieved by selecting the most proper normalization techniques for each MCDM decision problem. In this direction, several attempts have been carried out, however, the lack of coherence and lack of a robust assessment framework persist. This situation encouraged the authors to propose an assessment framework that is enriched with several metrics for the evaluation of different normalization techniques in MCDM problems with the focus on partner/supplier selection in collaborative networks. As an illustration of the approach, in this work we assess different normalization techniques with the Simple Additive Weighting (SAW) method using metrics from the proposed assessment framework and select the most adequate technique for a small case study that is borrowed from literature. The suggested approach contributes to increasing the accuracy of final results for MCDM methods.
AB - One of the current topics of attention in data analysis is the selection of the best normalization technique in the aggregation process when using Multi-Criteria Decision Making (MCDM) methods for solving decision problems. This is particularly critical in complex collaborative decision-making systems dealing with a large variety of heterogeneous data sources. Using different normalization techniques may result in different rankings of alternatives. So, enhancing the accuracy of the final ranking of alternatives could be achieved by selecting the most proper normalization techniques for each MCDM decision problem. In this direction, several attempts have been carried out, however, the lack of coherence and lack of a robust assessment framework persist. This situation encouraged the authors to propose an assessment framework that is enriched with several metrics for the evaluation of different normalization techniques in MCDM problems with the focus on partner/supplier selection in collaborative networks. As an illustration of the approach, in this work we assess different normalization techniques with the Simple Additive Weighting (SAW) method using metrics from the proposed assessment framework and select the most adequate technique for a small case study that is borrowed from literature. The suggested approach contributes to increasing the accuracy of final results for MCDM methods.
KW - Aggregation
KW - Collaborative network
KW - Data fusion
KW - Decision making
KW - MCDM
KW - Normalization
KW - SAW
UR - http://www.scopus.com/inward/record.url?scp=85124946484&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2022.01.156
DO - 10.1016/j.procs.2022.01.156
M3 - Conference article
AN - SCOPUS:85124946484
VL - 199
SP - 1229
EP - 1236
JO - Procedia Computer Science
JF - Procedia Computer Science
SN - 1877-0509
T2 - 8th International Conference on Information Technology and Quantitative Management, ITQM 2020 and 2021
Y2 - 9 July 2021 through 11 July 2021
ER -