PMID- 36359711
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230212
IS  - 1099-4300 (Electronic)
IS  - 1099-4300 (Linking)
VI  - 24
IP  - 11
DP  - 2022 Nov 8
TI  - A Clustering Multi-Criteria Decision-Making Method for Large-Scale Discrete and 
      Continuous Uncertain Evaluation.
LID - 10.3390/e24111621 [doi]
LID - 1621
AB  - In recent years, Dempster-Shafer (D-S) theory has been widely used in 
      multi-criteria decision-making (MCDM) problems due to its excellent performance 
      in dealing with discrete ambiguous decision alternative (DA) evaluations. In the 
      general framework of D-S-theory-based MCDM problems, the preference of the DAs 
      for each criterion is regarded as a mass function over the set of DAs based on 
      subjective evaluations. Moreover, the multi-criteria preference aggregation is 
      based on Dempster's combination rule. Unfortunately, this an idea faces two 
      difficulties in real-world applications: (i) D-S theory can only deal with 
      discrete uncertain evaluations, but is powerless in the face of continuous 
      uncertain evaluations. (ii) The generation of the mass function for each 
      criterion relies on the empirical judgments of experts, making it time-consuming 
      and laborious in terms of the MCDM problem for large-scale DAs. To the best of 
      our knowledge, these two difficulties cannot be addressed with existing 
      D-S-theory-based MCDM methods. To this end, this paper proposes a clustering MCDM 
      method combining D-S theory with the analytic hierarchy process (AHP) and the 
      Silhouette coefficient. By employing the probability distribution and the D-S 
      theory to represent discrete and continuous ambiguous evaluations, respectively, 
      determining the focal element set for the mass function of each criterion through 
      the clustering method, assigning the mass values of each criterion through the 
      AHP method, and aggregating preferences according to Dempster's combination rule, 
      we show that our method can indeed address these two difficulties in MCDM 
      problems. Finally, an example is given and comparative analyses with related 
      methods are conducted to illustrate our method's rationality, effectiveness, and 
      efficiency.
FAU - Wang, Siyuan
AU  - Wang S
AUID- ORCID: 0000-0002-9829-5948
AD  - School of Computer Science, South China Normal University, Guangzhou 510631, 
      China.
FAU - Ma, Wenjun
AU  - Ma W
AD  - School of Computer Science, South China Normal University, Guangzhou 510631, 
      China.
FAU - Zhan, Jieyu
AU  - Zhan J
AD  - School of Computer Science, South China Normal University, Guangzhou 510631, 
      China.
LA  - eng
GR  - 202102020948/Project of Science and Technology in Guangzhou in China/
PT  - Journal Article
DEP - 20221108
PL  - Switzerland
TA  - Entropy (Basel)
JT  - Entropy (Basel, Switzerland)
JID - 101243874
PMC - PMC9912255
OTO - NOTNLM
OT  - D-S theory
OT  - decision making under uncertainty
OT  - multi-criteria decision making
OT  - uncertain information clustering
COIS- The authors declare no conflict of interest.
EDAT- 2022/11/12 06:00
MHDA- 2022/11/12 06:01
PMCR- 2022/11/08
CRDT- 2022/11/11 01:12
PHST- 2022/09/30 00:00 [received]
PHST- 2022/11/03 00:00 [revised]
PHST- 2022/11/04 00:00 [accepted]
PHST- 2022/11/11 01:12 [entrez]
PHST- 2022/11/12 06:00 [pubmed]
PHST- 2022/11/12 06:01 [medline]
PHST- 2022/11/08 00:00 [pmc-release]
AID - e24111621 [pii]
AID - entropy-24-01621 [pii]
AID - 10.3390/e24111621 [doi]
PST - epublish
SO  - Entropy (Basel). 2022 Nov 8;24(11):1621. doi: 10.3390/e24111621.