scholarly journals MULTI-CRITERIA DECISION MAKING FOR IDENTIFICATION OF UNBALANCED BIDDING

2019 ◽  
Vol 26 (1) ◽  
pp. 43-52 ◽  
Author(s):  
Limin Su ◽  
Tianze Wang ◽  
Huimin Li ◽  
Yongchao Chao ◽  
Lunyan Wang
Keyword(s):  
Decision Making ◽  
Construction Projects ◽  
Unit Price ◽  
Multi Criteria Decision Making ◽  
Vikor Method ◽  
Decision Matrix ◽  
Entropy Weight ◽  
Proposed Model ◽  
Entropy Weight Method ◽  
Weight Method

Unbalanced bidding is a serious problem in the competitive bidding practices of construction projects. Identification and prevention of unbalanced bidding is an important and complexity task for owners. This paper aims to propose an identification model of unbalanced bidding from multi-criteria decision making (MCDM) perspective. The VIKOR method is employed to detect unbalanced bidding, in which the line items and bidders are considered as criteria and alternatives in MCDM, respectively. And the engineer’s estimated price is chosen as evaluation benchmarking. Then relative distances between engineer’s estimated price and each bidding unit price are calculated to build decision matrix. The weights of factors are determined using entropy weight method. To illustrate the effectiveness of the proposed model, an application example is tested in detecting unbalanced bidding. Finally, the sensitivity analysis about VIKOR method is given. It shows that the presented model would provide a robust decision making support for owner in identifying unbalanced bidding.

scholarly journals Restoration Methods Selection for Wood Components of Chinese Ancient Architectures Based on TODIM with Single-Valued Neutrosophic Sets

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiaolu. Long ◽  
Lizhi. Liu ◽  
Can. Xiao ◽  
Pengfei. Cheng ◽  
Chengxun. Fu
Keyword(s):  
Decision Making ◽  
Entropy Weight ◽  
Neutrosophic Sets ◽  
Wood Component ◽  
Proposed Model ◽  
Entropy Weight Method ◽  
Todim Method ◽  
Weight Method ◽  
Multicriteria Group Decision Making ◽  
Selection Of

The selection of restoration methods for ancient architectures is of great significance for the protection of human cultural heritage. This paper proposes a novel restoration methods selection approach for wood components of Chinese ancient architectures, in which a multicriteria group decision-making (MCGDM) method with decision-making information is in the form of single-valued neutrosophic sets (SNNSs). Firstly, it establishes an index system by comprehensively considering subjective and objective criteria. In addition, the best-worst method (BWM) and the entropy weight method are combined to produce index weights. Furthermore, the TODIM method is utilized by the single-valued neutrosophic sets to prioritize restoration methods. Finally, a specific case of wood component restoration is conducted to demonstrate the practicability of the proposed model. The robustness and effectiveness of the proposed method is verified by sensitivity analysis and comparison analysis.

scholarly journals A FUZZY MULTI-ATTRIBUTE DECISION-MAKING METHOD UNDER RISK WITH UNKNOWN ATTRIBUTE WEIGHTS / NERAIŠKUSIS MAŽESNĖS RIZIKOS DAUGIATIKSLIS SPRENDIMŲ PRIĖMIMO METODAS SU NEŽINOMAIS PRISKIRIAMAIS REIKŠMINGUMAIS

2011 ◽  
Vol 17 (2) ◽  
pp. 246-258 ◽  
Author(s):  
Zuosheng Han ◽  
Peide Liu
Keyword(s):  
Decision Making ◽  
Data Types ◽  
Decision Matrix ◽  
Entropy Weight ◽  
Attribute Weights ◽  
Relative Closeness ◽  
Entropy Weight Method ◽  
Multi Attribute Decision Making ◽  
Weight Method ◽  
Multiple Attributes Decision Making

This paper aims at solving hybrid multiple attributes decision-making problems under risk with attribute weight known and a new decision approach based on entropy weight and TOPSIS is proposed. First, the risk decision matrix is transformed into the certain decision matrix based on the expectation value. Then, the deviation entropy weight method is used to determine the attribute weights. And according to the definitions of the distance and the positive/negative ideal solutions for different data types, the relative closeness coefficients can be calculated by TOPSIS. Furthermore, the alternatives are ranked by the relative closeness coefficients. Finally, an application case is given to demonstrate the steps and effectiveness of the proposed approach. Santrauka Šiame straipsnyje siekiama išspręsti mišrias mažesnės rizikos daugiatiksles sprendimo priėmimo problemas su žinomu priskiriamu reikšmingumu bei yra siūlomas naujas sprendimų priėmimo metodas grindžiamas entropijos reikšmingumu ir TOPSIS. Pirmiausia, rizikos sprendimų matrica yra transformuojama į tam tikrą sprendimų matricą, grindžiamą galimybės verte. Tuomet yra naudojamas entropijos reikšmingumo nuokrypio metodas norint nustatyti priskiriamą reikšmingumą. Atsižvelgiant į atstumo apibrėžimus ir teigiamus / neigiamus idealius sprendimus skirtingiems duomenų tipams, santykinio artumo koeficientas gali būti apskaičiuojami remiantis TOPSIS. Be to, alternatyvos yra reitinguojamos pagal santykinio artumo koeficientus. Galiausiai, yra pateiktas pritaikymo atvejis, siekiant parodyti visus žingsnius ir siūlomo metodo veiksmingumą.

scholarly journals Decision-Making of Irrigation Scheme for Soybeans in the Huaibei Plain Based on Grey Entropy Weight and Grey Relation–Projection Pursuit

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 877 ◽  
Author(s):  
Yi Cui ◽  
Shangming Jiang ◽  
Juliang Jin ◽  
Ping Feng ◽  
Shaowei Ning
Keyword(s):  
Decision Making ◽  
Water Consumption ◽  
Projection Pursuit ◽  
Crop Water ◽  
Irrigation Scheme ◽  
Entropy Weight ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Projection Pursuit Model ◽  
Grey Relation

To provide a scientific reference for formulating an effective soybean irrigation schedule in the Huaibei Plain, potted water deficit experiments with nine alternative irrigation schemes during the 2015 and 2016 seasons were conducted. An irrigation scheme decision-making index system was established from the aspects of crop water consumption, crop growth process and crop water use efficiency. Moreover, a grey entropy weight method and a grey relation–projection pursuit model were proposed to calculate the weight of each decision-making index. Then, nine alternative schemes were sorted according to the comprehensive grey relation degree of each scheme in the two seasons. The results showed that, when using the entropy weight method or projection pursuit model to determine index weight, it was more direct and effective to obtain the corresponding entropy value or projection eigenvalue according to the sequence of the actual study object. The decision-making results from the perspective of actual soybean growth responses at each stage for various irrigation schemes were mostly consistent in 2015 and 2016. Specifically, for an integrated target of lower water consumption and stable biomass yields, the scheme with moderate-deficit irrigation at the soybean branching stage or seedling stage and adequate irrigation at the flowering-podding and seed filling stages is relatively optimal.

scholarly journals A multi-criteria decision-making model dealing with correlation among criteria for reservoir flood control operation

2015 ◽  
Vol 18 (3) ◽  
pp. 531-543 ◽  
Author(s):  
Feilin Zhu ◽  
Ping-an Zhong ◽  
Bin Xu ◽  
Ye-nan Wu ◽  
Yu Zhang
Keyword(s):  
Decision Making ◽  
Flood Control ◽  
Multi Criteria Decision Making ◽  
Decision Matrix ◽  
Control Operation ◽  
Criterion System ◽  
Proposed Model ◽  
Flood Control Operation ◽  
Reservoir Flood Control Operation ◽  
Ranking Order

Flood control operation in a multi-reservoir system is a multi-criteria decision-making (MCDM) problem, in which the considered criteria are often correlated with each other. In this paper, we propose an MCDM model for reservoir flood control operation to deal with correlation among criteria. Considering the flood control safety of reservoirs and downstream protected regions, we establish the hierarchical structure of the criterion system. We use the principal component analysis method to eliminate the correlation, and transform the original criterion system into an independent comprehensive criterion system. The comprehensive decision matrix coupled with the weight vector obtained by the improved entropy weight method serves as the input to TOPSIS method, fuzzy optimum method, and fuzzy matter-element method, by which we determine the ranking order of the alternatives. We apply the proposed model to a cascade system of reservoirs at the Daduhe River basin in China. The results show that the dimensionality of the criterion system is reduced and the correlation among criteria is eliminated simultaneously, and the ranking order of the alternatives is reasonable. The proposed model provides an effective way to deal with correlation among criteria, and can be extended to wider applications in many other MCDM problems.

scholarly journals A Method of Determining Multi-Attribute Weights Based on Single-Valued Neutrosophic Numbers and Its Application in TODIM

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 506 ◽  
Author(s):  
Dongsheng Xu ◽  
Yanran Hong ◽  
Kaili Xiang
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Entropy Weight ◽  
Attribute Weights ◽  
Attribute Weight ◽  
Decision Making Problem ◽  
Entropy Weight Method ◽  
Todim Method ◽  
Multi Attribute Decision Making ◽  
Weight Method

In this paper, the TODIM method is used to solve the multi-attribute decision-making problem with unknown attribute weight in venture capital, and the decision information is given in the form of single-valued neutrosophic numbers. In order to consider the objectivity and subjectivity of decision-making problems reasonably, the optimal weight is obtained by combining subjective weights and objective weights. Subjective weights are given directly by decision makers. Objective weights are obtained by establishing a weight optimization model with known decision information, then this method will compare with entropy weight method. These simulation results also validate the effectiveness and reasonableness of this proposed method.

scholarly journals Effectiveness of Entropy Weight Method in Decision-Making

2020 ◽  
Vol 2020 ◽  
pp. 1-5 ◽  
Author(s):  
Yuxin Zhu ◽  
Dazuo Tian ◽  
Feng Yan
Keyword(s):  
Decision Making ◽  
Water Source ◽  
Entropy Weight ◽  
Weighting Method ◽  
Numerical Discrimination ◽  
Differentiation Degree ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Degree Of Differentiation ◽  
Index Weight

Entropy weight method (EWM) is a commonly used weighting method that measures value dispersion in decision-making. The greater the degree of dispersion, the greater the degree of differentiation, and more information can be derived. Meanwhile, higher weight should be given to the index, and vice versa. This study shows that the rationality of the EWM in decision-making is questionable. One example is water source site selection, which is generated by Monte Carlo Simulation. First, too many zero values result in the standardization result of the EWM being prone to distortion. Subsequently, this outcome will lead to immense index weight with low actual differentiation degree. Second, in multi-index decision-making involving classification, the classification degree can accurately reflect the information amount of the index. However, the EWM only considers the numerical discrimination degree of the index and ignores rank discrimination. These two shortcomings indicate that the EWM cannot correctly reflect the importance of the index weight, thus resulting in distorted decision-making results.

scholarly journals New Uncertainty Measure of Rough Fuzzy Sets and Entropy Weight Method for Fuzzy-Target Decision-Making Tables

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Huani Qin ◽  
Darong Luo
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Fuzzy Set ◽  
Theoretical Studies ◽  
Uncertainty Measure ◽  
Entropy Weight ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Rough Fuzzy Set ◽  
Fuzzy Target

In the rough fuzzy set theory, the rough degree is used to characterize the uncertainty of a fuzzy set, and the rough entropy of a knowledge is used to depict the roughness of a rough classification. Both of them are effective, but they are not accurate enough. In this paper, we propose a new rough entropy of a rough fuzzy set combining the rough degree with the rough entropy of a knowledge. Theoretical studies and examples show that the new rough entropy of a rough fuzzy set is suitable. As an application, we introduce it into a fuzzy-target decision-making table and establish a new method for evaluating the entropy weight of attributes.

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