Optimal Academic Ranking of Students in a Fuzzy Environment: A Case Study

2018 ◽  
pp. 453-462 ◽  
Author(s):  
Satish S. Salunkhe ◽  
Yashwant Joshi ◽  
Ashok Deshpande
Keyword(s):  
Fuzzy Environment ◽  
Academic Ranking

scholarly journals An Integrated Fuzzy Model for Selecting Resilient Suppliers in Electronics Industry of Iran

Logistics ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 71
Author(s):  
Hamzeh Aghababayi ◽  
Mohsen Shafiei Shafiei Nikabadi
Keyword(s):  
Real Life ◽  
Fuzzy Model ◽  
Electronics Industry ◽  
Electronic Industry ◽  
Chain Management ◽  
Fuzzy Environment ◽  
Proposed Model ◽  
Delay Times ◽  
Effective Decision

Selecting appropriate and resilient suppliers is an important issue in supply chain management (SCM) literature. Making an effective decision on this issue can decrease external risks and disruptions, purchase costs, and delay times and also guarantees business continuity in the event of disruptions and, consequently, increases company competitiveness and customer satisfaction. This paper aims to provide a model based on identifying and investigating related criteria to evaluate suppliers’ resilience and select the most resilient suppliers in Iran’s electronic industry. To this purpose, the screening technique, the best–worst methodology (BWM), and goal programming (GP) have been applied in the fuzzy environment. The proposed model has been implemented and demonstrated by a case study of the electronic industry, as a real-life example. The results show that agility (0.227), compatibility (0.153), and vulnerability (0.102) are the most important factors for a resilient supplier.

scholarly journals Significance of multi-objective optimization in logistics problem for multi-product supply chain network under the intuitionistic fuzzy environment

2021 ◽  
Author(s):  
Srikant Gupta ◽  
Ahteshamul Haq ◽  
Irfan Ali ◽  
Biswajit Sarkar
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Real Life ◽  
Decision Makers ◽  
Supply Chain Network ◽  
Multi Objective ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Hierarchical Decision

AbstractDetermining the methods for fulfilling the continuously increasing customer expectations and maintaining competitiveness in the market while limiting controllable expenses is challenging. Our study thus identifies inefficiencies in the supply chain network (SCN). The initial goal is to obtain the best allocation order for products from various sources with different destinations in an optimal manner. This study considers two types of decision-makers (DMs) operating at two separate groups of SCN, that is, a bi-level decision-making process. The first-level DM moves first and determines the amounts of the quantity transported to distributors, and the second-level DM then rationally chooses their amounts. First-level decision-makers (FLDMs) aimed at minimizing the total costs of transportation, while second-level decision-makers (SLDM) attempt to simultaneously minimize the total delivery time of the SCN and balance the allocation order between various sources and destinations. This investigation implements fuzzy goal programming (FGP) to solve the multi-objective of SCN in an intuitionistic fuzzy environment. The FGP concept was used to define the fuzzy goals, build linear and nonlinear membership functions, and achieve the compromise solution. A real-life case study was used to illustrate the proposed work. The obtained result shows the optimal quantities transported from the various sources to the various destinations that could enable managers to detect the optimum quantity of the product when hierarchical decision-making involving two levels. A case study then illustrates the application of the proposed work.

scholarly journals A Stone Resource Assignment Model under the Fuzzy Environment

2012 ◽  
Vol 2012 ◽  
pp. 1-26 ◽  
Author(s):  
Liming Yao ◽  
Jiuping Xu ◽  
Feng Guo
Keyword(s):  
Simulated Annealing Algorithm ◽  
Optimization Method ◽  
Resource Assignment ◽  
Possibility Measure ◽  
Fuzzy Environment ◽  
Assignment Model ◽  
Simulation Based ◽  
Annealing Algorithm ◽  
Upper Level

This paper proposes a bilevel multiobjective optimization model with fuzzy coefficients to tackle a stone resource assignment problem with the aim of decreasing dust and waste water emissions. On the upper level, the local government wants to assign a reasonable exploitation amount to each stone plant so as to minimize total emissions and maximize employment and economic profit. On the lower level, stone plants must reasonably assign stone resources to produce different stone products under the exploitation constraint. To deal with inherent uncertainties, the object functions and constraints are defuzzified using a possibility measure. A fuzzy simulation-based improved simulated annealing algorithm (FS-ISA) is designed to search for the Pareto optimal solutions. Finally, a case study is presented to demonstrate the practicality and efficiency of the model. Results and a comparison analysis are presented to highlight the performance of the optimization method, which proves to be very efficient compared with other algorithms.

A novel probabilistic hesitant fuzzy portfolio selection model with value-at-risk and safety level of score

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xue Deng ◽  
Weimin Li
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Selection Model ◽  
Selection Models ◽  
Content Type ◽  
Fuzzy Portfolio Selection ◽  
Fuzzy Environment ◽  
Portfolio Selection Model

Purpose This paper aims to propose two portfolio selection models with hesitant value-at-risk (HVaR) – HVaR fuzzy portfolio selection model (HVaR-FPSM) and HVaR-score fuzzy portfolio selection model (HVaR-S-FPSM) – to help investors solve the problem that how bad a portfolio can be under probabilistic hesitant fuzzy environment. Design/methodology/approach It is strictly proved that the higher the probability threshold, the higher the HVaR in HVaR-S-FPSM. Numerical examples and a case study are used to illustrate the steps of building the proposed models and the importance of the HVaR and score constraint. In case study, the authors conduct a sensitivity analysis and compare the proposed models with decision-making models and hesitant fuzzy portfolio models. Findings The score constraint can make sure that the portfolio selected is profitable, but will not cause the HVaR to decrease dramatically. The investment proportions of stocks are mainly affected by their HVaRs, which is consistent with the fact that the stock having good performance is usually desirable in portfolio selection. The HVaR-S-FPSM can find portfolios with higher HVaR than each single stock and has little sacrifice of extreme returns. Originality/value This paper fulfills a need to construct portfolio selection models with HVaR under probabilistic hesitant fuzzy environment. As a downside risk, the HVaR is more consistent with investors’ intuitions about risks. Moreover, the score constraint makes sure that undesirable portfolios will not be selected.

Decision Making in a Fuzzy Environment and its Management to the Diminishing Manufacturing Sources and Material Shortage Problems

2014 ◽  
Vol 513-517 ◽  
pp. 2672-2675
Author(s):  
Yuan Cheng Tsai ◽  
Yi Lun Chi
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Fuzzy Theory ◽  
Aerospace Industry ◽  
Possibility Theory ◽  
Lessons Learned ◽  
Fuzzy Environment ◽  
The Subject ◽  
Electronic Support

The paper formulated a proposed methodology to manage diminishing manufacturing sources and material shortage (DMS) with conclusions and recommendations on the subject of component obsolescence management in a military electronic support environment. By assessing applicable literature as well as feedback and lessons learned from relevant support projects, a strategy for the management of component obsolescence is proposed. The aim of the research is to explore the problem of managing DMS strategies by the method of project management and describes the risk of running distinct strategies to solve problems of DMS by fuzzy theory and possibility theory. Based on the results, this paper can be applied to support businesses quickly to determine the Strategies Combination, Resource Allocation and Inventory by using the model and genetic algorithm. A case study of an aerospace industry is used to illustrate the concept developed, which would be meaningful to reduce applicable obsolescence risks and thereby reducing related inventory and manpower costs.

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