Value-Driven Multiple Criteria Sorting with Probabilistic Linguistic Information Considering Uncertain Assignment Examples

2021 ◽  
pp. 1-25
Author(s):  
Shuxian Sun ◽  
Huchang Liao
Keyword(s):  
Decision Maker ◽  
Value Function ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Ordered Categories ◽  
Linguistic Information ◽  
Additive Value Function ◽  
Preference Disaggregation ◽  
Multiple Criteria Sorting ◽  
Evaluation Information

Multiple criteria sorting (MCS) dedicates to assigning alternatives to one of the predefined ordered categories according to their evaluation information on multiple criteria. The utility (value) function-based sorting is a popular MCS procedure, which requires decision-makers to express their preferences through assignment examples. By taking the assignment examples as reference alternatives, the additive value function, as the preferred model of a decision maker, can be built using the preference disaggregation technique. However, the existing literature hardly considered people’s hesitancy when determining assignment examples, and ignored applying linguistic evaluation information on qualitative criteria. To fill these research gaps, this study proposes a value-driven MCS procedure with probabilistic linguistic information considering uncertain assignment examples. Specifically, the probability linguistic term set, as a flexible information representation tool, is introduced to express the hesitancy of decision-makers regarding assignment examples and the performance of alternatives on qualitative criteria. Besides, to comprehensively reflect the preference of a decision-maker, a weighted additive value function is proposed based on the preference disaggregation technique to calculate the comprehensive scores of alternatives in which the weights are determined by the best-worst method. Finally, a case study on the sorting of down coats for sale demonstrates the applicability and superiority of our proposed method.

scholarly journals The Extension of Quality Function Deployment Based on 2-Tuple Linguistic Representation Model for Product Design under Multigranularity Linguistic Environment

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Ming Li
Keyword(s):  
Product Design ◽  
Quality Function Deployment ◽  
Decision Makers ◽  
Linguistic Information ◽  
Quality Function ◽  
Discrimination Ability ◽  
Linguistic Representation ◽  
Analysis Process ◽  
Representation Model ◽  
Evaluation Information

Quality function deployment (QFD) is a customer-driven approach for product design and development. A QFD analysis process includes a series of subprocesses, such as determination of the importance of customer requirements (CRs), the correlation among engineering characteristics (ECs), and the relationship between CRs and ECs. Usually more than group of one decision makers are involved in the subprocesses to make the decision. In most decision making problems, they often provide their evaluation information in the linguistic form. Moreover, because of different knowledge, background, and discrimination ability, decision makers may express their linguistic preferences in multigranularity linguistic information. Therefore, an effective approach to deal with the multi-granularity linguistic information in QFD analysis process is highly needed. In this study, the QFD methodology is extended with 2-tuple linguistic representation model under multi-granularity linguistic environment. The extended QFD methodology can cope with multi-granularity linguistic evaluation information and avoid the loss of information. The applicability of the proposed approach is demonstrated with a numerical example.

Selection of a representative value function in robust multiple criteria sorting

2011 ◽  
Vol 38 (11) ◽  
pp. 1620-1637 ◽  
Author(s):  
Salvatore Greco ◽  
MiŁosz Kadziński ◽  
Roman SŁowiński
Keyword(s):  
Value Function ◽  
Multiple Criteria ◽  
Multiple Criteria Sorting ◽  
Selection Of

Analytical Study on Big Data

2018 ◽  
Vol 8 (5) ◽  
pp. 75
Author(s):  
Vivek Raich ◽  
Pankaj Maurya
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Decision Maker ◽  
Analytical Study ◽  
Large Data ◽  
Decision Makers ◽  
Continuous Increase ◽  
Analytic Methods ◽  
Data Store ◽  
Business Engineering

in the time of the Information Technology, the big data store is going on. Due to which, Huge amounts of data are available for decision makers, and this has resulted in the progress of information technology and its wide growth in many areas of business, engineering, medical, and scientific studies. Big data means that the size which is bigger in size, but there are several types, which are not easy to handle, technology is required to handle it. Due to continuous increase in the data in this way, it is important to study and manage these datasets by adjusting the requirements so that the necessary information can be obtained.The aim of this paper is to analyze some of the analytic methods and tools. Which can be applied to large data. In addition, the application of Big Data has been analyzed, using the Decision Maker working on big data and using enlightened information for different applications.

A multicriteria hierarchical approach for portfolio selection in a stock exchange

2021 ◽  
Vol 40 (2) ◽  
pp. 1945-1955
Author(s):  
Maria Bernal ◽  
Pavel Anselmo Alvarez ◽  
Manuel Muñoz ◽  
Ernesto Leon-Castro ◽  
Diego Alonso Gastelum-Chavira
Keyword(s):  
Decision Maker ◽  
Portfolio Selection ◽  
Stock Exchange ◽  
Multiple Criteria ◽  
Financial Ratios ◽  
Hierarchical Approach

The objective of the paper is to present a multiple criteria hierarchical process (MCHP) approach for portfolio selection in a stock exchange. One of the problems that investors usually face is which stock should be included in the portfolio. This paper helps investors answer that question, and the paper presents an MCHP approach using different criteria based on financial ratios that the decision maker (in this case, the investor) will give different weights to make a portfolio based on her preferences; different importance is given to each criterion. An example using the Mexican Stock Exchange is presented.

Metric for Disassembly and Reuse Decisions: Formulation and Validation

2008 ◽  
Author(s):  
Vijitashwa Pandey ◽  
Deborah Thurston
Keyword(s):  
Maximum Likelihood ◽  
Value Function ◽  
Choice Theory ◽  
Decision Makers ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Maximum Value ◽  
Long Range Planning ◽  
The Relationship ◽  
The Value Function

Design for disassembly and reuse focuses on developing methods to minimize difficulty in disassembly for maintenance or reuse. These methods can gain substantially if the relationship between component attributes (material mix, ease of disassembly etc.) and their likelihood of reuse or disposal is understood. For products already in the marketplace, a feedback approach that evaluates willingness of manufacturers or customers (decision makers) to reuse a component can reveal how attributes of a component affect reuse decisions. This paper introduces some metrics and combines them with ones proposed in literature into a measure that captures the overall value of a decision made by the decision makers. The premise is that the decision makers would choose a decision that has the maximum value. Four decisions are considered regarding a component’s fate after recovery ranging from direct reuse to disposal. A method on the lines of discrete choice theory is utilized that uses maximum likelihood estimates to determine the parameters that define the value function. The maximum likelihood method can take inputs from actual decisions made by the decision makers to assess the value function. This function can be used to determine the likelihood that the component takes a certain path (one of the four decisions), taking as input its attributes, which can facilitate long range planning and also help determine ways reuse decisions can be influenced.

Women's Role Involvement in Family Businesses, Business Tensions, and Business Success

2003 ◽  
Vol 16 (1) ◽  
pp. 53-68 ◽  
Author(s):  
Sharon M. Danes ◽  
Patricia D. Olson
Keyword(s):  
Decision Maker ◽  
Family Business ◽  
Decision Makers ◽  
Business Success ◽  
Business Owner ◽  
Role Involvement ◽  
Work Involvement ◽  
Women's Role ◽  
The Impact ◽  
Major Decision

This paper is based on a study of 391 family-business-owning couples where the husband is the business owner. The purpose of the study was to examine the work involvement of the wife in the business, the business tensions, and the impact of those tensions on family business success. Fifty-seven percent of wives worked in the business, 47% of whom were paid. Forty-two percent of wives were considered major decision makers. Having more than one decision maker in the business impacted certain types of inclusion tension. Business and family success outcomes varied by level of tensions. There was initial evidence of a threshold where business tensions begin to affect business success negatively.

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