scholarly journals A Target-Oriented Multiple-Attribute Decision-Making Approach Based on Probabilistic Linguistic Preference Relations

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Chun-Hao Li ◽  
Hui-Xin Ma ◽  
Yan-Hui Jia
Keyword(s):  
Decision Making ◽  
Value Function ◽  
Choquet Integral ◽  
Preference Relation ◽  
Multiple Attribute Decision Making ◽  
Frequency Theory ◽  
Estimation Model ◽  
Fuzzy Measures ◽  
Multiple Attribute ◽  
Attribute Value

While the approach to multiple-attribute decision-making (MADM) is widely used in a variety of fields, including models with fuzzy sets and corresponding extensions, it cannot solve target-oriented decision problems with both selective and targeted alternatives. Therefore, this study provides the first description of a target-oriented MADM problem and proposes a novel decision framework. An attribute value function for target orientation is defined by integrating range and frequency values derived under cumulative prospect theory and range-frequency theory. A Choquet integral with discrete fuzzy measures is then used to integrate attribute values and determine comprehensive values for selective alternatives. In this determination of comprehensive values, a parameter estimation model is also established, with its input assumed to be the pairwise comparison judgment matrix with probabilistic linguistic preference relation. This model as well as its transformation aims at determining the parameters of both the attribute value function and fuzzy measures. Finally, the process of target-oriented MADM is summarized, and an illustrative example is provided to demonstrate the applicability of the proposed techniques.

Set Pair Analysis Method of Containing Target Constraint Mixed Interval Multi-Attribute Decision-Making

2012 ◽  
Vol 226-228 ◽  
pp. 2222-2226 ◽  
Author(s):  
Wen Sheng Lü ◽  
Bin Zhang
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Set Pair Analysis ◽  
Analysis Theory ◽  
Multiple Attribute ◽  
Complex Decision Making ◽  
Multi Attribute Decision Making ◽  
Interval Type ◽  
Pair Analysis ◽  
Attribute Value

In view of target attribute value for different sector number, moreover, also attaches a target constraint condition kind of mix sector multi-attribute decision making question, this paper presents set pair analysis decision-making method. Firstly this paper puts forward three typical interval type attribute value representation; Then using set pair analysis theory, the interval type attribute value unified convert the correlate form, Finally has given complex decision-making criterion function, which collected Conformity degree criteria and Criteria for membership degree. Through the construction plan changes decision-making example analysis shows that this method is a simple and effective method for solving multiple attribute decision making.

An estimation model of construction project segmentation for optimum project pricing

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fang-Jye Shiue ◽  
Hsin-Yun Lee ◽  
Meng-Cong Zheng ◽  
Akhmad F.K. Khitam ◽  
Sintayehu Assefa
Keyword(s):  
Decision Making ◽  
Construction Projects ◽  
Critical Issue ◽  
Multiple Attribute Decision Making ◽  
Estimation Model ◽  
Content Type ◽  
Proposed Model ◽  
Multiple Attribute ◽  
Bid Price ◽  
Construction Bids

PurposeFor large projects, project segmentation and planning the size of contract packages in construction bids is a complex and critical issue. Due to the nature of construction projects, which frequently have large budgets, long durations and many activities with complex procedures, project segmentation involves complicated decision-making. To fill this gap, this study aims to develop an integrated model for planning project segmentation.Design/methodology/approachThe proposed model integrates a simulation and multiple attribute decision-making method. The simulation is used to evaluate the bidding outcome of various project segmentations. The owner can then determine the bid-price behavior of contractors in response to varying work package sizes. The multiple attribute decision-making method is used to select the optimal segmentation solution from the simulated scenarios.FindingsThe proposed model is applied to a large road preservation project in Indonesia and incorporates bid participants and market conditions. The model provides seven scenarios for segmentation. The range of scenarios captures increasing competitiveness in the construction with the average bid price becoming gradually more beneficial for the owner. The model also utilizes a multiple attribute decision-making method to select the optimum scenario for the owner.Originality/valueThis study presents an applicable model for project segmentation that is useful for both project owners and contractors. By utilizing the proposed model, a project owner can segment a large project into smaller contract packages to create improved project pricing.

Shapley interval-valued dual hesitant fuzzy Choquet integral aggregation operators in multiple attribute decision making

2018 ◽  
Vol 34 (3) ◽  
pp. 1827-1845 ◽  
Author(s):  
Guohua Qu ◽  
Haisheng Zhou ◽  
Weihua Qu ◽  
Chunhua Li
Keyword(s):  
Decision Making ◽  
Choquet Integral ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Multiple Attribute ◽  
Interval Valued

scholarly journals METHOD FOR AGGREGATING CORRELATED INTERVAL GREY LINGUISTIC VARIABLES AND ITS APPLICATION TO DECISION MAKING

2013 ◽  
Vol 19 (2) ◽  
pp. 189-202 ◽  
Author(s):  
Nian Zhang
Keyword(s):  
Decision Making ◽  
Choquet Integral ◽  
Multiple Attribute Decision Making ◽  
Linguistic Variables ◽  
Analysis Method ◽  
Multiple Attribute

With respect to multiple attribute decision making (MADM) problems in which attribute values take the form of interval grey linguistic variables, a new decision making analysis method is developed. In this paper, we propose the interval grey linguistic variables ordered weighted aggregation (IGLOWA) operator, and then use the Choquet integral to develop the interval grey linguistic correlated ordered arithmetic aggregation (IGLCOA) operator and the interval grey linguistic correlated ordered geometric aggregation (IGLCOGA) operator. Those operators not only consider the importance of the elements, but also can reflect the correlations among the elements. Then, we develop an approach to multiple attribute decision making problems with correlative weights which attribute values are given in terms of interval grey linguistic variables information based on those operators. Finally an illustrative example is given to use the method in the range of uncertain multiple attribute decision making. The results show that the method proposed in this paper is feasible.

Multiple Attribute Decision Making with Inexact Value-Function Assessment

1990 ◽  
Vol 21 (2) ◽  
pp. 318-336 ◽  
Author(s):  
Ralph D. Badinelli ◽  
Joanna R. Baker
Keyword(s):  
Decision Making ◽  
Value Function ◽  
Multiple Attribute Decision Making ◽  
Multiple Attribute ◽  
Function Assessment

scholarly journals Multiple-Attribute Decision-Making Problem Using TOPSIS and Choquet Integral with Hesitant Fuzzy Number Information

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Harish Garg ◽  
Abazar Keikha ◽  
Hassan Mishmast Nehi
Keyword(s):  
Decision Making ◽  
Fuzzy Number ◽  
Choquet Integral ◽  
Multiple Attribute Decision Making ◽  
Hesitant Fuzzy Set ◽  
Fuzzy Information ◽  
Multiple Attribute ◽  
Decision Making Problem ◽  
Collection Data ◽  
Topsis Technique

The paper aims are to present a method to solve the multiple-attribute decision-making (MADM) problems under the hesitant fuzzy set environment. In MADM problems, the information collection, aggregation, and the measure phases are crucial to direct the problem. However, to handle the uncertainties in the collection data, a hesitant fuzzy number is one of the most prominent ways to express uncertain and vague information in terms of different discrete numbers rather than a single crisp number. Additionally, to aggregate and to rank the collective numbers, a TOPSIS (“Technique for Order of Preference by Similarity to Ideal Solution”) and the Choquet integral (CI) are the useful tools. Keeping all these features, in the present paper, we combine the TOPSIS and CI methods for hesitant fuzzy information and hence present a method named as TOPSIS-CI to address the MADM problems. The presented method has been described with a numerical example. Finally, the validity of the stated method as well as a comparative analysis with the existing methods is addressed in detail.

scholarly journals INTUITIONISTIC FUZZY EINSTEIN CHOQUET INTEGRAL OPERATORS FOR MULTIPLE ATTRIBUTE DECISION MAKING

2014 ◽  
Vol 20 (2) ◽  
pp. 227-253 ◽  
Author(s):  
Yejun Xu ◽  
Huimin Wang ◽  
José M. Merigó
Keyword(s):  
Decision Making ◽  
Water Resource Management ◽  
Choquet Integral ◽  
Integral Operators ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute ◽  
Decision Making Problem ◽  
Einstein Operations

In this paper, we propose some new aggregation operators which are based on the Choquet integral and Einstein operations. The operators not only consider the importance of the elements or their ordered positions, but also consider the interactions phenomena among the decision making criteria or their ordered positions. It is shown that the proposed operators generalize several intuitionistic fuzzy Einstein aggregation operators. Moreover, some of their properties are investigated. We also study the relationship between the proposed operators and the existing intuitionistic fuzzy Choquet aggregation operators. Furthermore, an approach based on intuitionistic fuzzy Einstein Choquet integral operators is presented for multiple attribute decision-making problem. Finally, a practical decision making problem involving the water resource management is given to illustrate the multiple attribute decision making process.

Multiple Attribute Decision Making Based on Neutrosophic Preference Relation

2021 ◽  
Author(s):  
Wen Jiang ◽  
Meijuan Wang ◽  
Xinyang Deng
Keyword(s):  
Decision Making ◽  
Preference Relation ◽  
Multiple Attribute Decision Making ◽  
Multiple Attribute
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