Jafar Rezaei
Biographic Data
| ID | 5774895 |
|---|---|
| NAME | Jafar Rezaei |
| GIVEN NAMES | Jafar |
| FAMILY NAME | Rezaei |
| SIGNATURE | REZAEI J |
| AFFILIATIONS | Delft University of Technology |
| ORCID | 0000-0002-7407-9255 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 16 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Anchoring Bias in the Tradeoff Procedure Within Multi‐Attribute Value Theory
Eliciting the weights of attributes is a key step in multi‐attribute decision‐making methods. The weights usually represent the relative importance of the attributes or the tradeoffs among them in forming a decision. Various weight elicitation methods exist, each based on different assumptions and procedures. Still, many of these methods do not explicitly account for the potential influence of cognitive biases in their design. This study examines…
Unveiling and Unraveling Aggregation and Dispersion Fallacies in Group MCDM
Priorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in fallacious statistical analysis. This article studies three prevalent fallacies in group MCDM along with s…
Equalizing bias in eliciting attribute weights in multiattribute decision‐making: Experimental research
One of the most important steps in formulating and solving a multiattribute decision‐making (MADM) problem is weighting the attributes. Most existing weighting methods are based on judgments by experts/decision‐makers, which are prone to several cognitive biases, making it necessary to examine these biases in MADM weighting methods and develop debiasing strategies. This study uses experimental analysis to look at equalizing bias—one of the main c…
On the evolution of maritime ports towards the Physical Internet
The Physical Internet (PI) is a novel, comprehensive and long-term vision of the future global freight transport and logistics (FTL) system, which is aimed at radically improving its efficiency and sustainability. As research on the PI concept is still young, the functioning of maritime ports in the context of the PI is still underexplored. Our aim is to contribute to the scientific debate about radically different futures for maritime ports arou…
Towards a balanced E-Participation Index: Integrating government and society perspectives
Quality assessment of airline baggage handling systems using SERVQUAL and BWM
Best-worst multi-criteria decision-making method: Some properties and a linear model
Best-worst multi-criteria decision-making method
Towards a balanced E-Participation Index: Integrating government and society perspectives
Quality assessment of airline baggage handling systems using SERVQUAL and BWM
Equalizing bias in eliciting attribute weights in multiattribute decision‐making: Experimental research
One of the most important steps in formulating and solving a multiattribute decision‐making (MADM) problem is weighting the attributes. Most existing weighting methods are based on judgments by experts/decision‐makers, which are prone to several cognitive biases, making it necessary to examine these biases in MADM weighting methods and develop debiasing strategies. This study uses experimental analysis to look at equalizing bias—one of the main c…
Best-worst multi-criteria decision-making method
Best-worst multi-criteria decision-making method: Some properties and a linear model
Quality assessment of airline baggage handling systems using SERVQUAL and BWM
Towards a balanced E-Participation Index: Integrating government and society perspectives
Equalizing bias in eliciting attribute weights in multiattribute decision‐making: Experimental research
One of the most important steps in formulating and solving a multiattribute decision‐making (MADM) problem is weighting the attributes. Most existing weighting methods are based on judgments by experts/decision‐makers, which are prone to several cognitive biases, making it necessary to examine these biases in MADM weighting methods and develop debiasing strategies. This study uses experimental analysis to look at equalizing bias—one of the main c…
On the evolution of maritime ports towards the Physical Internet
The Physical Internet (PI) is a novel, comprehensive and long-term vision of the future global freight transport and logistics (FTL) system, which is aimed at radically improving its efficiency and sustainability. As research on the PI concept is still young, the functioning of maritime ports in the context of the PI is still underexplored. Our aim is to contribute to the scientific debate about radically different futures for maritime ports arou…
Unveiling and Unraveling Aggregation and Dispersion Fallacies in Group MCDM
Priorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in fallacious statistical analysis. This article studies three prevalent fallacies in group MCDM along with s…
Anchoring Bias in the Tradeoff Procedure Within Multi‐Attribute Value Theory
Eliciting the weights of attributes is a key step in multi‐attribute decision‐making methods. The weights usually represent the relative importance of the attributes or the tradeoffs among them in forming a decision. Various weight elicitation methods exist, each based on different assumptions and procedures. Still, many of these methods do not explicitly account for the potential influence of cognitive biases in their design. This study examines…
Computer Science (7 works) · Mathematics (5 works) · Multi-Criteria Decision Making (5 works) · Operations research (4 works) · Algorithm (3 works) · Data mining (3 works) · Engineering (3 works) · Mathematical optimization (3 works) · Multiple-criteria decision analysis (3 works) · Analytic hierarchy process (2 works)