A matching mechanism for public cloud manufacturing platforms using intuitionistic Fuzzy VIKOR and deferred acceptance algorithm

2021 ◽  
pp. 1-16
Author(s):  
Jalal Delaram ◽  
Omid Fatahi Valilai ◽  
Mahmoud Houshamand ◽  
Farid Ashtiani
Keyword(s):  
Cloud Manufacturing ◽  
Public Cloud ◽  
Fuzzy Vikor ◽  
Intuitionistic Fuzzy ◽  
Deferred Acceptance Algorithm ◽  
Deferred Acceptance ◽  
Matching Mechanism

ERP Software Selection Based on Intuitionistic Fuzzy VIKOR Method

2019 ◽  
pp. 121-137
Author(s):  
Burak Efe
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Selection Problem ◽  
Final Decision ◽  
Software Selection ◽  
Intuitionistic Fuzzy Numbers ◽  
Fuzzy Vikor ◽  
Intuitionistic Fuzzy ◽  
Erp Software ◽  
Unique Decision

This chapter uses intuitionistic fuzzy VIKOR (IFVIKOR) for the application of ERP software selection. First, priority values of criteria in ERP software selection problem have been determined by using the judgments of the experts. IFWA operator is utilized to integrate the judgments of the experts about the weights of criteria. Then, the result of the IFVIKOR can be employed to define the most appropriate ERP alternative in uncertain environment. Intuitionistic fuzzy numbers are presented in all phases in order to overcome any vagueness in the decision-making process. The final decision depends on the degree of importance of each decision so that wrong degree of importance causes the mistaken result. The researchers generally determine the degrees of importance of each decision makers according to special characteristics of each decision maker as subjectivity. In order to overcome this subjectivity in this chapter, the judgments of decision makers are degraded to unique decision by using the importance degree of each expert. There is no study about ERP software selection using IFVIKOR.

scholarly journals An Extended FMEA Model Based on Cumulative Prospect Theory and Type-2 Intuitionistic Fuzzy VIKOR for the Railway Train Risk Prioritization

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1418
Author(s):  
Yong Fu ◽  
Yong Qin ◽  
Weizhong Wang ◽  
Xinwang Liu ◽  
Limin Jia
Keyword(s):  
Prospect Theory ◽  
Failure Modes ◽  
Fuzzy Numbers ◽  
Cumulative Prospect Theory ◽  
Entropy Weight ◽  
Intuitionistic Fuzzy Numbers ◽  
Risk Prioritization ◽  
Fuzzy Vikor ◽  
Intuitionistic Fuzzy

This paper aims toward the improvement of the limitations of traditional failure mode and effect analysis (FMEA) and examines the crucial failure modes and components for railway train operation. In order to overcome the drawbacks of current FMEA, this paper proposes a novel risk prioritization method based on cumulative prospect theory and type-2 intuitionistic fuzzy VIKOR approach. Type-2 intuitionistic VIKOR handles the combination of the risk factors with their entropy weight. Triangular fuzzy number intuitionistic fuzzy numbers (TFNIFNs) applied as type-2 intuitionistic fuzzy numbers (Type-2 IFNs) are adopted to depict the uncertainty in the risk analysis. Then, cumulative prospect theory is employed to deal with the FMEA team member’s risk sensitiveness and decision-making psychological behavior. Finally, a numerical example of the railway train bogie system is selected to illustrate the application and feasibility of the proposed extended FMEA model in this paper, and a comparison study is also performed to validate the practicability and effectiveness of the novel FMEA model. On this basis, this study can provide guidance for the risk prioritization of railway trains and indicate a direction for further research of risk management of rail traffic.

scholarly journals Strategyproof and Fair Matching Mechanism for Union of Symmetric M-convex Constraints

2018 ◽  
Author(s):  
Yuzhe Zhang ◽  
Kentaro Yahiro ◽  
Nathanaël Barrot ◽  
Makoto Yokoo
Keyword(s):  
Convex Sets ◽  
Real Life ◽  
Convex Constraints ◽  
New Class ◽  
Distributional Constraints ◽  
Deferred Acceptance ◽  
Matching Mechanism

In this paper, we identify a new class of distributional constraints defined as a union of symmetric M-convex sets, which can represent a variety of real-life constraints in two-sided matching settings. Since M-convexity is not closed under union, a union of symmetric M-convex sets does not belong to this well-behaved class of constraints in general. Thus, developing a fair and strategyproof mechanism that can handle this class is challenging. We present a novel mechanism called Quota Reduction Deferred Acceptance (QRDA), which repeatedly applies the standard DA mechanism by sequentially reducing artificially introduced maximum quotas. We show that QRDA is fair and strategyproof when handling a union of symmetric M-convex sets. Furthermore, in comparison to a baseline mechanism called Artificial Cap Deferred Acceptance (ACDA), QRDA always obtains a weakly better matching for students and, experimentally, performs better in terms of nonwastefulness.

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