Optimization of Group Multiattribute Decision-Making Model in Commercial Space Investment

2021 ◽  
Author(s):  
Yiming Zhang ◽  
Junjie Hou ◽  
Shaowen Zhong
Keyword(s):  
Decision Making ◽  
Commercial Space ◽  
Decision Making Model

Applying the Multi-Criteria Decision Making Model for Ranking Commercial Banks: The Case of Vietnam

2019 ◽  
pp. 125-133
Author(s):  
Duong Truong Thi Thuy ◽  
Anh Pham Thi Hoang
Keyword(s):  
Decision Making ◽  
Commercial Banks ◽  
Decision Model ◽  
Business Models ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Topsis Method ◽  
Decision Making Model

Banking has always played an important role in the economy because of its effects on individuals as well as on the economy. In the process of renovation and modernization of the country, the system of commercial banks has changed dramatically. Business models and services have become more diversified. Therefore, the performance of commercial banks is always attracting the attention of managers, supervisors, banks and customers. Bank ranking can be viewed as a multi-criteria decision model. This article uses the technique for order of preference by similarity to ideal solution (TOPSIS) method to rank some commercial banks in Vietnam.

Multi-Attribute Decision-Making Model by Applying Grey Numbers

Informatica ◽  
2009 ◽  
Vol 20 (2) ◽  
pp. 305-320 ◽  
Author(s):  
Edmundas Kazimieras Zavadskas ◽  
Arturas Kaklauskas ◽  
Zenonas Turskis ◽  
Jolanta Tamošaitienė
Keyword(s):  
Decision Making ◽  
Multi Attribute Decision Making ◽  
Decision Making Model

scholarly journals A Methodology for Redesigning Networks by Using Markov Random Fields

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1389
Author(s):  
Julia García Cabello ◽  
Pedro A. Castillo ◽  
Maria-del-Carmen Aguilar-Luzon ◽  
Francisco Chiclana ◽  
Enrique Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Random Fields ◽  
Markov Random Fields ◽  
Application Performance ◽  
Cross Border ◽  
Markov Random ◽  
User Groups ◽  
Universal Definition ◽  
Definition Of ◽  
Decision Making Model

Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model (DMM) for redesigning networks that works without geographical constraints. There are multiple advantages of this approach: on one hand, it can be used in any country of the world; on the other hand, the absence of geographical constraints widens the application scope of our approach, meaning that it can be successfully implemented either in physical (ATM networks) or non-physical networks such as in group decision making, social networks, e-commerce, e-governance and all fields in which user groups make decisions collectively. Case studies involving both types of situations are conducted in order to illustrate the methodology. The model has been designed under a data reduction strategy in order to improve application performance.

A partner selection problem for complex product of manufacturing enterprises in supply chain

2021 ◽  
pp. 1-13
Author(s):  
Congdong Li ◽  
Yinyun Yu ◽  
Wei Xu ◽  
Jianzhu Sun
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Fuzzy Theory ◽  
Partner Selection ◽  
Manufacturing Companies ◽  
Complex Product ◽  
Manufacturing Enterprises ◽  
Collaborative Manufacturing ◽  
Matching Degree ◽  
Decision Making Model

In order to better meet customer needs and respond to market demands more quickly, mounting number of manufacturing companies have begun to bid farewell to the traditional unitary manufacturing model. The collaborative manufacturing model has become a widely adopted manufacturing model for manufacturing companies. Aiming at the problem of partner selection for collaborative manufacturing of complex products in a collaborative supply chain environment, this paper proposes a multi-objective decision-making model that comprehensively considers the maximization of the matching degree of manufacturing capacity and the profits of supply chain, and gives the modeling process and application steps in detail. The method first uses fuzzy theory to evaluate the manufacturing capabilities of candidate collaborative manufacturing partners. Secondly, Vector Space Model (VSM) is used to calculate the matching degree of manufacturing capacity and manufacturing demand. Then, the paper studied the profit of the supply chain under the “non-cooperative” mechanism and the “revenue sharing” mechanism. Furthermore, the decision-making model is established. Finally, a simulation was carried out by taking complex product manufacturing of Gree enterprise as an example. The research results show the feasibility and effectiveness of the method.

Representing a probabilistic linguistic term set with an interval type-2 fuzzy set and the application in green supplier selection

2021 ◽  
pp. 1-18
Author(s):  
Le Jiang ◽  
Hongbin Liu
Keyword(s):  
Decision Making ◽  
Membership Function ◽  
Fuzzy Set ◽  
Supplier Selection ◽  
Symbolic Model ◽  
Green Supplier Selection ◽  
Linguistic Term ◽  
Interval Type ◽  
Decision Making Model

The use of probabilistic linguistic term sets (PLTSs) means the process of computing with words. The existing methods computing with PLTSs mainly use symbolic model. To provide a semantic model for computing with PLTSs, we propose to represent a PLTS by using an interval type-2 fuzzy set (IT2FS). The key step is to compute the footprint of uncertainty of the IT2FS. To this aim, the upper membership function is computed by aggregating the membership functions of the linguistic terms contained in the PLTS, and the lower membership function is obtained by moving the upper membership function downward with the step being total entropy of the PLTS. The comparison rules, some operations, and an aggregation operator for PLTSs are introduced. Based on the proposed method of computing with PLTSs, a multi-criteria group decision making model is introduced. The proposed decision making model is then applied in green supplier selection problem to show its feasibility.

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