Evaluation and selecting the contractor in bidding with incomplete information using MCGDM method

Birjandi, Alireza Komeili; Akhyani, Fatemeh; Sheikh, Reza; Sana, Shib Sankar

doi:10.1007/s00500-019-04050-y

Cited by 38 publications

(18 citation statements)

References 61 publications

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“…Particularly, in the construction industry, the risk assessment is significantly more complicated than the other sectors because of the high complexity of the construction projects. Hence, using the hesitant fuzzy sets is more consistent with this type of complicated projects [46].…”

Section: Literature Reviewmentioning

confidence: 88%

An Extension of the Failure Mode and Effect Analysis with Hesitant Fuzzy Sets to Assess the Occupational Hazards in the Construction Industry

Dahooie

Vanaki

Firoozfar

et al. 2020

IJERPH

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The construction industry is considered as one of the most dangerous industries in terms of occupational safety and has a high rate of occupational incidents and risks compared to other industries. Given the importance of identifying and assessing the occupational hazards in this industry, researchers have conducted numerous studies using statistical methods, multi-criteria decision-making methods, expert-based judgments, and so on. Although, these researchers have used linguistic variables, fuzzy sets and interval-valued intuitionistic fuzzy sets to overcome challenges such as uncertainty and ambiguity in the risk assessment conducted by experts; the previous models lack in efficiency if the experts are hesitant in their assessment. This leads to the inability to assign a specific membership degree to any risk. Therefore, in this research, it is tried to provide an improved approach to the Failure Mode and Effects Analysis (FMEA) method using an Multi-Criteria Decision-Making (MCDM) method based on the hesitant fuzzy set, which can effectively cope with the hesitance of the experts in the evaluation. Also, Stepwise Weight Assessment Ratio Analysis (SWARA) method is applied for risk factor weighing in the proposed approach. This model is applied to a construction industry case study to solve a realistic occupational risk assessment. Moreover, a comparison is made between the results of this model and those obtained by the conventional FMEA and some other aggregation operators. The results indicate that the newly developed approach is useful and flexible to address complex FMEA problems and can generate logical and reliable priority rankings for failure modes.

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Section: Literature Reviewmentioning

confidence: 88%

An Extension of the Failure Mode and Effect Analysis with Hesitant Fuzzy Sets to Assess the Occupational Hazards in the Construction Industry

Dahooie

Vanaki

Firoozfar

et al. 2020

IJERPH

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“…Firstly, the matrix table is set up for incomplete pairwise comparisons as follows, and then the incomplete information will be obtained using the reciprocal preference relation (RPR) [10,51]. The incomplete information matrix in which some values are unknown due to the lack of time or the uncertainty on pairwise recognition is defined as follows: Reciprocal preference relation is represented by Γ = (γ ij ) n×n .…”

Section: Calculating Unknown Valuesmentioning

confidence: 99%

“…In Multi Criteria Decision Making (MCDM) problems, there are several alternatives and criteria in order to classify those alternatives according to defined boundaries [10,41].…”

Section: A New Approach To Classificationmentioning

confidence: 99%

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Analysis and classification of companies on tehran stock exchange with incomplete information

et al. 2021

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Due to uncertainty and large number of companies in financial market, it has become difficult to choose the right stock to investments. Identifying and classifying stocks using fundamental criteria help investors to better understand the risks involved in selecting companies and better manage their own capital, thereby rapidly and accurately choose their preferred stock and make more secure profit. The main concern that capital market investors are facing difficulty to choosing the right stock despite the uncertainties in the market. Uncertainties in the market that lead to incomplete information are presented in this article to complete the reciprocal preference relation method. The purpose of this paper is to present a method for completing information to reduce the uncertainties in the market and finally classify companies in each industry based on fundamental criteria. The classification method used is acceptability / reject ability which is based on distance fuzzy analysis yields more accurate results. Finally, a case study on one of the most critical industries in Tehran Stock Exchange is presented to show the effectiveness of the proposed approach.

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“…research in future. A deep dive analysis for selected sectors (Birjandi et al, 2019) can bring out more factors like information asymmetry, company conditions, opinions etc., which were beyond scope of this study.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

A comprehensive study on bid-ask spread and its determinants in India

Pan

Misra

2021

Cogent Economics & Finance

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Evaluation and selecting the contractor in bidding with incomplete information using MCGDM method

Cited by 38 publications

References 61 publications

An Extension of the Failure Mode and Effect Analysis with Hesitant Fuzzy Sets to Assess the Occupational Hazards in the Construction Industry

An Extension of the Failure Mode and Effect Analysis with Hesitant Fuzzy Sets to Assess the Occupational Hazards in the Construction Industry

Analysis and classification of companies on tehran stock exchange with incomplete information

A comprehensive study on bid-ask spread and its determinants in India

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