2020
1
1
0
84
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Supplier selection using fuzzy AHP method and DNumbers
http://www.journalfea.com/article_114153.html
10.22105/jfea.2020.248437.1007
1
Success in supply begins with the right choice of suppliers and in the long run is directly related to how suppliers are managed, because suppliers have a significant impact on the success or failure of a company. Multicriteria decisions are approaches that deal with ranking and selecting one or more suppliers from a set of suppliers. Multicriteria decisions provide an effective framework for comparing suppliers based on the evaluation of different criteria. The present research is applied based on the purpose and descriptivesurvey based on the nature and method of the research. In the present study, two library and field methods have been used to collect information. According to the objectives of this study, suppliers will be evaluated using two methods of fuzzy hierarchical analysis with Dnumbers. In order to better understand these two methods, a case study is presented in which suppliers are ranked using two methods and then the results are compared with each other. For manufacturing companies, 4 categories of parts were considered and based on the classification, the suppliers of the manufacturing company were evaluated and analyzed. In the results of suppliers of type A and B components in hierarchical analysis, D and fuzzy methods have many differences in the evaluation and ranking of suppliers, and this shows the lack of expectations of experts in D and fuzzy analysis. On the other hand, in type C and D components, the classification and ranking of suppliers have been matched in two ways and shows that the opinions in the evaluation of these suppliers are the same.
0

1
14


Payam
Shafi Salimi
Ayandegan Institute of Higher Education, Tonekabon, Iran.
Iran
payam_shafi@yahoo.com
Supply chain
Management
Fuzzy AHP Method
DNumbers
[Mazaheri, A. Saljuqi, M. & Seljuk, T. (2017). Identifying the key factors affecting the optimal selection of green suppliers in the green supply chain in the manufacturing industry. Fifth international conference on economics, management, accounting with value creation approach. Shiraz, Narun Expert Managers Training Institute.##[2] Fallah, S. Qadir, A. H., & Qadir, H. (2017). Twoobjective mathematical planning model for the integrated problem of stacked size and sustainable supplier selection under fuzzy conditions. 2nd international conference on industrial management. Babolsar, Mazandaran University.##[3] Genovese, A., Acquaye, A., IbnMohammed, T., Afrifa, G. A., Yamoah, F. A., & Oppon, E. (2018). A quantitative model for environmentally sustainable supply chain performance measurement. European journal of operational research, 269(1), 188205.## [4] Tamošaitienė, J., Valipour, A., Yahaya, N., Md Noor, N., & Antuchevičienė, J. (2017). Hybrid SWARACOPRAS method for risk assessment in deep foundation excavation project: An Iranian case study. Journal of civil engineering and management, 23(4), 524532.##[5] Liu, R. & Hai, L. (2005). The voting analytic hierarchy process method for selecting supplier. International journal of production economics, 97(3), 308317.## [6] Chen, C. T., Lin, C. T. & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain managemen. International journal of production economics, 102(2), 289301.## [7] Lim, J. J., & Zhang, A. N. (2016). A DEA approach for supplier selection with AHP and risk consideration. In Big Data (Big Data). 2016 IEEE international conference on, 37493758.##[8] Su, C. M., Horng, D. J., Tseng, M. L., Chiu, A. S., Wu, K. J., & Chen, H. P. (2016). Improving sustainable supply chain management using a novel hierarchical greyDEMATEL approach. Journal of cleaner production, 134, 469481.##[9] Momeni, M. (2006). New topics in operations research. First Edition, Tehran: University of Tehran School of Management Publications.Management, Accounting with Value Creation Approach, Shiraz, Narun Expert Managers Training Institute.##[10] Shahgholian, K., Shahraki, A., Waezi, Z. (2011). Multicriteria group decision for supplier selection with fuzzy approach. 11th Iranian fuzzy systems conference.##[11] Ming, Z. H. A., Xing. L. I. U. (2008). Research on mobile supply chain management Based Ubiquitous Network. IEE, 3351.## [12] Maleki, M., Cruz Machadi, V., (2013). Development of supply chain integration model through application of analytic network process and Bayesian Network. International journal of integrated supply management##[13] Nosrati, F. & Jafari Eskandari, M.(2009), stable optimization method pessimistic possibility in designing a multilevel supply chain network under uncertainty.2nd International Conference on Management, Industrial Engineering, Economics and Accounting. TbilisiGeorgia, Permanent Secretariat in collaboration with Imam Sadegh University##[14] Tabakhi Qasbeh, E., &Sediq, M. (2017). Evaluation of Overseas Suppliers with Emphasis on Risk Indicators Using Hierarchical Analytical Process, 6th national conference on management, economics and accounting. Tabriz, East Azarbaijan Technical and Vocational UniversityOrganization Tabriz Industrial .##[15] Shafia, M. A., Mahdavi Mazdeh, M., Pournader, M., &Bagherpour, M. (2016). Presenting a twolevel data envelopment analysis model in supply chain risk management in order to select a supplier.##[16] Mardani, A., Kannan, D., Hooker, R. E., Ozkul, S., Alrasheedi, M., & Tirkolaee, E. B. (2020). Evaluation of green and sustainable supply chain management using structural equation modelling: A systematic review of the state of the art literature and recommendations for future research. Journal of cleaner production, 249, 119383.##[17] Ghadimi, P., Wang, C., & Lim, M. K. (2019). Sustainable supply chain modeling and analysis: Past debate, present problems and future challenges. Resources, conservation and recycling, 140, 7284.##[18] Reefke, H., & Sundaram, D. (2017). Key themes and research opportunities in sustainable supply chain management–identification and evaluation. Omega, 66, 195211.##[19]. Hamdi, F., Ghorbel, A., Masmoudi, F., & Dupont, L. (2018). Optimization of a supply portfolio in the context of supply chain risk management: literature review. Journal of intelligent manufacturing, 29(4), 763788.##[20] Rad, R. S., & Nahavandi, N. (2018). A novel multiobjective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. Journal of cleaner production, 196, 15491565.##[21] Kumar, A., Pal, A., Vohra, A., Gupta, S., Manchanda, S., & Dash, M. K. (2018). Construction of capital procurement decision making model to optimize supplier selection using Fuzzy Delphi and AHPDEMATEL. Benchmarking: an international journal, 25(5), 15281547.##[22] Wang, Z., Luo, C., & Luo, T. (2018). Selection optimization of bloom filterbased index services in ubiquitous embedded systems. International conference on web Services (pp. 231245).##]
1

A new approach for ranking of intuitionistic fuzzy numbers
http://www.journalfea.com/article_114137.html
10.22105/jfea.2020.247301.1003
1
The concept of an intuitionistic fuzzy number (I F N) is of importance for representing an illknown quantity. Ranking fuzzy numbers plays a very important role in the decision process, data analysis and applications. The concept of an intuitionistic fuzzy number (IFN) is of importance for quantifying an illknown quantity. Ranking of intuitionistic fuzzy numbers plays a vital role in decision making and linear programming problems. Also, ranking of intuitionistic fuzzy numbers is a very difficult problem. In this paper, a new method for ranking intuitionistic fuzzy number is developed by means of magnitude for different forms of intuitionistic fuzzy numbers. In Particular ranking is done for trapezoidal intuitionistic fuzzy numbers, triangular intuitionistic fuzzy numbers, symmetric trapezoidal intuitionistic fuzzy numbers, and symmetric triangular intuitionistic fuzzy numbers. Numerical examples are illustrated for all the defined different forms of intuitionistic fuzzy numbers. Finally some comparative numerical examples are illustrated to express the advantage of the proposed method.
0

15
26


Suresh
Mohan
Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.
India
mathssuresh84@gmail.com


Arun Prakash
Kannusamy
Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.
India
arunfuzzy@gmail.com


Vengataasalam
Samiappan
Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.
India
sv.maths@gmail.com
intuitionistic fuzzy sets
intuitionistic fuzzy numbers
Trapezoidal Intuitionistic
Fuzzy Numbers
triangular intuitionistic fuzzy numbers
magnitude of intuitionistic fuzzy number
[Atanassov K. (1986). Intuitionistic fuzzy sets. Fuzzy sets and systems, 20, 8796.##[2] Abbasbandy, S., & Hajjari, T. (2009). A new approach for ranking of trapezoidal fuzzy numbers. Computers & mathematics with applications, 57(3), 413419.##[3] Allahviranloo, T., Abbasbandy, S., & Saneifard, R. (2011). A method for ranking of fuzzy numbers using new weighted distance. Mathematical and computational applications, 16(2), 359369.##[4] Dubey, D., & Mehra, A. (2011). Linear programming with triangular intuitionistic fuzzy number. Proceedings of the 7th conference of the european society for fuzzy logic and technology (pp. 563569). Atlantis Press.##[5] Grzegrorzewski, P. (2003). The hamming distance between intuitionistic fuzzy sets. Proceedings of the 10th IFSA world congress, Istanbul, Turkey (Vol. 30, pp. 3538).##[6] Li, D. F. (2010). A ratio ranking method of triangular intuitionistic fuzzy numbers and its application to MADM problems. Computers & mathematics with applications, 60(6), 15571570.##[7] Li, D. F., Nan, J. X., & Zhang, M. J. (2010). A ranking method of triangular intuitionistic fuzzy numbers and application to decision making. International journal of computational intelligence Systems, 3(5), 522530.##[8] Mitchell, H. B. (2004). Rankingintuitionistic fuzzy numbers. International journal of uncertainty, fuzziness and knowledgebased systems, 12(03), 377386.##[9] Mahapatra, G. S., & Mahapatra, G. S. (2010). Intuitionistic fuzzy fault tree analysis using intuitionistic fuzzy numbers. International mathematical forum, 5(21), 10151024.##[10] Nehi, H. M. (2010). A new ranking method for intuitionistic fuzzy numbers. International journal of fuzzy systems, 12(1).##[11] Nayagam, V.L.G., Venkateshwari, G., Sivaraman, G., (2008). Ranking of intuitionistic fuzzy numbers, Proc. of international conference on fuzzy systems 2008, FuzzIEEE (pp. 19711974).##[12] Nagoorgani, A., & Ponnalagu, K. (2012). A new approach on solving intuitionistic fuzzy linear programming problem. Applied mathematical sciences, 6(70), 34673474.##[13] Parvathi, R., & Malathi, C. (2012). Arithmetic operations on symmetric trapezoidal intuitionistic fuzzy numbers. International Journal of Soft Computing and Engineering, 2, 268273.##[14] Parvathi, R., & Malathi, C. (2012). Intuitionistic fuzzy simplex method. International journal of computer applications, 48(6), 3948.##[15] Rezvani, S. (2013). Ranking method of trapezoidal intuitionistic fuzzy numbers. Annals of fuzzy mathematics and informatics, 5(3), 515523.##[16] Seikh, M. R., Nayak, P. K., & Pal, M. (2012). Generalized triangular fuzzy numbers in intuitionistic fuzzy environment. International journal of engineering research and development, 5(1), 0813.##[17] Jianqiang, W., & Zhong, Z. (2009). Aggregation operators on intuitionistic trapezoidal fuzzy number and its application to multicriteria decision making problems. Journal of systems engineering and electronics, 20(2), 321326.##[18] Wang, J. (2008). Overview on fuzzy multicriteria decisionmaking approach. Control and decision, 23(6), 601606.##]
1

Application of transportation problem under pentagonal Neutrosophic environment
http://www.journalfea.com/article_114140.html
10.22105/jfea.2020.246633.1001
1
The paper talks about the pentagonal Neutrosophic sets and its operational law. The paper presents the cut of single valued pentagonal Neutrosophic numbers and additionally introduced the arithmetic operation of singlevalued pentagonal Neutrosophic numbers. Here, we consider a transportation problem with pentagonal Neutrosophic numbers where the supply, demand and transportation cost is uncertain. Taking the benefits of the properties of ranking functions, our model can be changed into a relating deterministic form, which can be illuminated by any method. Our strategy is easy to assess the issue and can rank different sort of pentagonal Neutrosophic numbers. To legitimize the proposed technique, some numerical tests are given to show the adequacy of the new model.
0

27
41


Sapan
Kumar Das
Department of Revenue, Ministry of Finance, Govt. of India.
India
cool.sapankumar@gmail.com
Transportation problem
pentagonal neutrosophic numbers
Linear Programming
[Hitchcock, F. L. (1941). The distribution of a product from several sources to numerous localities. Journal of mathematics and physics, 20(14), 224230.##[2] Dantzig, G. B., & Thapa, M. N. (2006). Linear programming 2: theory and extensions. Springer Science & Business Media.##[3] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338353.##[4] Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy sets and systems, 1(1), 4555.##[5] Chanas, S., Kołodziejczyk, W., & Machaj, A. (1984). A fuzzy approach to the transportation problem. Fuzzy sets and systems, 13(3), 211221.##[6] Das, S. K., Mandal, T., & Edalatpanah, S. A. (2017). A mathematical model for solving fully fuzzy linear programming problem with trapezoidal fuzzy numbers. Applied intelligence, 46(3), 509519.##[7] Dinagar, D. S., & Palanivel, K. (2009). The transportation problem in fuzzy environment. International journal of algorithms, computing and mathematics, 2(3), 6571.##[8] Kaur, A., & Kumar, A. (2011). A new method for solving fuzzy transportation problems using ranking function. Applied mathematical modelling, 35(12), 56525661.## [9] Pandian, P., & Natarajan, G. (2010). A new algorithm for finding a fuzzy optimal solution for fuzzy transportation problems. Applied mathematical sciences, 4(2), 7990.##[10] Kundu, P., Kar, S., & Maiti, M. (2013). Some solid transportation models with crisp and rough costs. International journal of mathematical and computational sciences, 7(1), 1421.##[11] Kaur, A., & Kumar, A. (2012). A new approach for solving fuzzy transportation problems using generalized trapezoidal fuzzy numbers. Applied soft computing, 12(3), 12011213.## [12] Kundu, P., Kar, S., & Maiti, M. (2014). Multiobjective solid transportation problems with budget constraint in uncertain environment. International journal of systems science, 45(8), 16681682.##[13] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A pythagorean fuzzy approach to the transportation problem. Complex & intelligent systems, 5(2), 255263.##[14] Liu, P., Yang, L., Wang, L., & Li, S. (2014). A solid transportation problem with type2 fuzzy variables. Applied soft computing, 24, 543558.##[15] Tada, M., & Ishii, H. (1996). An integer fuzzy transportation problem. Computers & mathematics with applications, 31(9), 7187.## [16] Liu, S. T., & Kao, C. (2004). Solving fuzzy transportation problems based on extension principle. European journal of operational research, 153(3), 661674.## [17] Saad, O. M., & Abass, S. A. (2003). A parametric study on tranportation problem under fuzzy environment. Journal of fuzzy mathematics, 11(1), 115124.## [18] A. Charnes, Charnes, A., & Cooper, W. W. (1954). The stepping stone method of explaining linear programming calculations in transportation problems. Management science, 1(1), 4969.## [19] Chanas, S., & Kuchta, D. (1996). A concept of the optimal solution of the transportation problem with fuzzy cost coefficients. Fuzzy sets and systems, 82(3), 299305.##[20] Maheswari, P. U., & Ganesan, K. (2018, April). Solving fully fuzzy transportation problem using pentagonal fuzzy numbers. Journal of physics: conference series (Vol. 1000, No. 1, p. 012014). IOP Publishing.## [21] Das, S. K., & Edalatpanah, S. A. (2020). New insight on solving fuzzy linear fractional programming in material aspects. Fuzzy optimization and modelling, 1, 17.##[22] Das, S. K., Mandal, T., & Edalatpanah, S. A. (2017). A new approach for solving fully fuzzy linear fractional programming problems using the multiobjective linear programming. RAIROoperations research, 51(1), 285297.##[23] Das, S. K., Mandal, T., & Behera, D. (2019). A new approach for solving fully fuzzy linear programming problem. International journal of mathematics in operational research, 15(3), 296309.##[24] Atanassov K. (1986). Intuitionistic fuzzy sets. Fuzzy sets and systems, 20, 8796.##[25] Ebrahimnejad, A., & Verdegay, J. L. (2018). A new approach for solving fully intuitionistic fuzzy transportation problems. Fuzzy optimization and decision making, 17(4), 447474.## [26] Nagoorgani, A., & Abbas, S. (2013). A new method for solving intuitionistic fuzzy transportation problem. Applied mathematical science, 7(28), 1357–1365.##[27] Singh, S. K., & Yadav, S. P. (2016). A new approach for solving intuitionistic fuzzy transportation problem of type2. Annals of operations research, 243(12), 349363.## [28] Singh, S. K., & Yadav, S. P. (2015). Efficient approach for solving type1 intuitionistic fuzzy transportation problem. International journal of system assurance engineering and management, 6(3), 259267.##[29] Singh, S. K., & Yadav, S. P. (2016). Intuitionistic fuzzy transportation problem with various kinds of uncertainties in parameters and variables. International journal of system assurance engineering and management, 7(3), 262272.## [30] Hussain, R. J., & Kumar, P. S. (2012). Algorithmic approach for solving intuitionistic fuzzy transportation problem. Applied mathematical sciences, 6(80), 39813989.## [31] Aggarwal, S., & Gupta, C. (2017). Sensitivity analysis of intuitionistic fuzzy solid transportation problem. International journal of fuzzy systems, 19(6), 19041915.## [32] Singh, S. K., & Yadav, S. P. (2016). A novel approach for solving fully intuitionistic fuzzy transportation problem. International journal of operational research, 26(4), 460472.## [33] Mahmoodirad, A., Allahviranloo, T., & Niroomand, S. (2019). A new effective solution method for fully intuitionistic fuzzy transportation problem. Soft computing, 23(12), 45214530.##[34] Das, S. K., Edalatpanah, S. A., & Mandal, T. (2018). A proposed model for solving fuzzy linear fractional programming problem: numerical point of view. Journal of computational science, 25, 367375.##[35] Das, S. K. (2017). Modified method for solving fully fuzzy linear programming problem with triangular fuzzy numbers. International journal of research in industrial engineering, 6(4), 293311.##[36] Smarandache, F. (1998). A unifying field in logics: Neutrosophic logic, Neutrosophy, Neutrosophic set, Neutrosophic probability (fifth eition). AmericanResearchPress, Rchoboth.##[37] Wang, H., Smarandache, F., Zhang, Y. Q., & Sunderraman, R. (2010). Single valued Neutrosophic sets. Multispace and multistructure, 4, 410–413. ##[38] Ye, J. (2018). Neutrosophic number linear programming method and its application under Neutrosophic number environments. Soft computing, 22(14), 46394646.##[39] Roy, R., & Das, P. (2015). A Multiobjective production planning problem based on Neutrosophiclinear programming approach. Infinite Study.##[40]AbdelBasset, M., Gunasekaran, M., Mohamed, M., & Smarandache, F. (2019). A novel method for solving the fully Neutrosophic linear programming problems. Neural computing and applications, 31(5), 15951605.##[41] Edalatpanah, S. A. (2020). A direct model for triangular Neutrosophic linear programming. International journal of neutrosophic science, 1(1), 1928.##[42] Maiti, I., Mandal, T., & Pramanik, S. (2019). Neutrosophic goal programming strategy for multilevel multiobjective linear programming problem. Journal of ambient intelligence and humanized computing, 112.##[43] Edalatpanah, S. A. (2020). Data envelopment analysis based on triangular Neutrosophic numbers. CAAI transactions on intelligence technology. Retrieved from##https://www.researchgate.net/profile/Sa_Edalatpanah3/publication/339979254_Data_Envelopment_Analysis_Based_on_Triangular_Neutrosophic_Numbers/links/5e86e5fb92851c2f5277b0bc/DataEnvelopmentAnalysisBasedonTriangularNeutrosophicNumbers.pdf##[44] Mohamed, M., AbdelBasset, M., Zaied, A. N. H., & Smarandache, F. (2017). Neutrosophic integer programming problem. Infinite Study.##[45] Banerjee, D., & Pramanik, S. (2018). Singleobjective linear goal programming problem with Neutrosophic numbers. Infinite Study.##[46] Das, S. K., & Dash, J. K. (2020). Modified solution for Neutrosophic linear programming problems with mixed constraints. International journal of research in industrial engineering, 9(1), 1324.##[47] Das, S. K., & Chakraborty, A. (2020). A new approach to evaluate linear programming problem in pentagonal Neutrosophic environment. Complex & intelligent systems, 110.## [48] Chakraborty, A., Mondal, S. P., Alam, S., Ahmadian, A., Senu, N., De, D., & Salahshour, S. (2019). The pentagonal fuzzy number: its different representations, properties, ranking, defuzzification and application in game problems. Symmetry, 11(2), 248.##[49] Chakraborty, A., Broumi, S., & Singh, P. K. (2019). Some properties of pentagonal Neutrosophic numbers and its applications in transportation problem environment. Neutrosophic sets and systems, 28(1), 16.##[50] Korukoğlu, S., & Ballı, S. (2011). An improved vogel's approximatio method for the transportation problem. Mathematical and computational applications, 16(2), 370381.##[51] Bharati, S. K. (2019). Trapezoidal intuitionistic fuzzy fractional transportation problem. Soft computing for problem solving (pp. 833842). Springer, Singapore.##[52] Ahmad, F., & Adhami, A. Y. (2019). Neutrosophic programming approach to multiobjective nonlinear transportation problem with fuzzy parameters. International journal of management science and engineering management, 14(3), 218229.##[53] Srinivasan, R., Karthikeyan, N., Renganathan, K., & Vijayan, D. V. (In press). Method for solving fully fuzzy transportation problem to transform the materials. Materials today: proceedings.##[54] Maiti, I., Mandal, T., Pramanik, S., Das, S.K. (2020). Solving multiobjective linear fractional programming problem based on Stanojevic’s normalization technique under fuzzy environment. International journal of operation research. DOI: 10.1504/IJOR.2020.10028794. ##]
1

Precise services and supply chain prioritization in manufacturing companies using cost analysis provided in a fuzzy environment
http://www.journalfea.com/article_114139.html
10.22105/jfea.2020.248187.1006
1
In recent years, management and, consequently, supply chain performance measurement, has attracted the attention of a large number of managers and researchers in the field of production and operations management. In parallel with the evolution of organizations from a single approach to a network and supply chain approach, performance measurement systems have also changed and moved towards network and supply chain performance measurement. Therefore, in order to face the storm of great change and transformation and not give in to the wave of competitive aggression, organizations have long had one thing in common, and that is to focus approaches and focus efforts towards achieving results. Results that lead to a competitive advantage and are more effective and decisive in the performance indicators of the organization, including earning more. In this study, in order to identify and prioritize the factors affecting the supply chain in manufacturing companies, using indicators such as cost, timely delivery and procurement time to evaluate the supply chain efficiency is considered. And performance evaluation was performed at the manufacturer level. Therefore, in order to evaluate the performance of the supply chain using the AHP integration approach and the DEA method approach in the fuzzy environment, the suppliers and suppliers of the manufacturing company were evaluated and ranked in terms of performance.
0

42
59


Alireza
Marzband
Ayandegan Institute of Higher Education, Tonekabon, Iran.
Iran
marzband.omidico@gmail.com
Supply chain management
Data Envelopment Analysis
Supply Chain Efficiency
1

Using interval arithmetic for providing a MADM approach
http://www.journalfea.com/article_114142.html
10.22105/jfea.2020.247946.1004
1
The VIKOR method was developed for MultiCriteria Decision Making (MCDM). It determines the compromise ranking list and the compromise solution obtained with the initial weights. This method focuses on ranking and selecting from a set of alternatives in the presence of conflicting criteria. It introduces the multicriteria ranking index based on the particular measure of ‘‘closeness” to the “Ideal” solution. The aim of this paper is to extend the VIKOR method for decision making problems with interval number. The extended VIKOR method’s ranking is obtained through comparison of interval numbers and for doing the comparisons between intervals. In the end, a numerical example illustrates and clarifies the main results developed in this paper.
0

60
68


Hossein
Jafari
Young Researchers and Elite Club, Arak Branch, Islamic Azad University, Arak, Iran.
Iran
hossein_jafari_123@yahoo.com


Mohammad
Ehsanifar
Department of Industrial Engineering, Islamic Azad University of Arak, Arak, Iran.
Iran
mehsanitfar@iauarak.ac.ir
Decision Making
Multi Attribute Decision Making (MADM)
Interval arithmetic
[[1] Yu, P. L. (1973). A class of solutions for group decision problems. Management science, 19(8), 936946.##[2] Zeleny, M. (Ed.). (2012). Multiple criteria decision making Kyoto 1975 (Vol. 123). Springer Science & Business Media.##[3] Yu, P. L. (2013). Multiplecriteria decision making: concepts, techniques, and extensions (Vol. 30). Springer Science & Business Media.##[4] Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.##[5] triantaphyllou, E. (2000). multicriteria decision making methods: a comparative study. kluwer academic publishers, dordrecht.##[6] wang, h. f. (2000). Fuzzy multicriteria decision making—an overview. Journal of intelligent & Fuzzy Systems, 9(1, 2), 6183.##[7] Chen, S. J., & Hwang, C. L. (1991). Fuzzy multiple attribute decision making. Springer Verlag, Berlin.##[8] Ribeiro, R. A. (1996). Fuzzy multiple attribute decision making: a review and new preference elicitation techniques. Fuzzy sets and systems, 78(2), 155181.##[9] Prékopa, A. (2013). Stochastic programming (Vol. 324). Springer Science & Business Media.##[10] Sengupta, J. K. (1981). Optimal decision under uncertainty. Springer, New York.##[11] Vajda, S. (1972). Probabilistic programming. Academic Press, New York.##[12] Liu, X. (2004). On the methods of decision making under uncertainty with probability information. International journal of intelligent systems, 19(12), 12171238.##[13] Moore, R. E. (1979). Method and application of interval analysis. SIAM, Philadelphia.##[14] Jahanshahloo, G. R., Lotfi, F. H., & Izadikhah, M. (2006). An algorithmic method to extend TOPSIS for decisionmaking problems with interval data. Applied mathematics and computation, 175(2), 13751384.##[15] Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European journal of operational research, 156(2), 445455.##[16] Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European journal of operational research, 178(2), 514529.##[17] Choobineh, F., & Behrens, A. (1993). Use of intervals and possibility distributions in economic analysis. Journal of the operational research society, 43(9), 907918.##[18] Sengupta, A., & Pal, T. K. (2006). Solving the shortest path problem with interval arcs. Fuzzy optimization and decision making, 5(1), 7189.##[19] Alefeld, G., & Herzberger, J. (2012). Introduction to interval computation. Academic press.##[20] Kearfott, R. B., & Kreinovich, V. (Eds.). (2013). Applications of interval computations (Vol. 3). Springer Science & Business Media.##[21] Shaocheng, T. (1994). Interval number and fuzzy number linear programmings. Fuzzy sets and systems, 66(3), 301306.##[22] Moore, R., & Lodwick, W. (2003). Interval analysis and fuzzy set theory. Fuzzy sets and systems, 135(1), 59.##[23] Bhattacharyya, R. (2015). A grey theory based multiple attribute approach for R&D project portfolio selection. Fuzzy information and engineering, 7(2), 211225.##]
1

Fuzzy logic in accounting and auditing
http://www.journalfea.com/article_114141.html
10.22105/jfea.2020.246647.1002
1
Many areas of accounting have highly ambiguous due to undefined and inaccurate terms. Many ambiguities are generated by the human mind. In the field of accounting, these ambiguities lead to the creation of uncertain information. Many of the targets and concepts of accounting with binary classification are not consistent. Similarly, the discussion of the materiality or reliability of accounting is not a twopart concept. Because there are degrees of materiality or reliability. Therefore, these ambiguities lead to the presentation information that is not suitable for decision making. Lack of attention to the issue of ambiguity in management accounting techniques, auditing procedures, and financial reporting may lead to a reduced role of accounting information in decisionmaking processes. Because information plays an important role in economic decisionmaking, and no doubt, the quality of their, including accuracy in providing it to a wide range of users, can be useful for decisionmaking. One of the features of the fuzzy set is that it reduces the need for accurate data in decision making. Hence this information can be useful for users.
0

69
75


Mohsen
Imeni
Department of Accounting, Ayandegan Institute of Higher Education, Tonekabon, Iran.
Iran
mohsen.imeni86@yahoo.com
Fuzzy logic
Accounting
Auditing
ambiguity
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Efficiency study with undesirable inputs and outputs in DEA
http://www.journalfea.com/article_114143.html
10.22105/jfea.2020.248018.1005
1
Data Envelopment Analysis (DEA) is one of the wellknown methods for calculating efficiency, determining efficient boundaries and evaluating efficiency that is used in specific input and output conditions. Traditional models of DEA do not try to reduce undesirable outputs and increase undesirable inputs. Therefore, in this study, in addition to determining the efficiency of DecisionMaking Units (DMU) with the presence of some undesirable input and output components, its effect has also been investigated on the efficiency limit. To do this, we first defined the appropriate production possibility set according to the problem assumptions, and then we presented a new method to determine the unfavorable performance of some input and output components in decisionmaking units. And we determined the impact of unfavorable inputs and outputs on the efficient boundary. We also showed the model result by providing examples for both unfavorable input and output states and solving them and determining the efficiency score and driving them to the efficient boundary by plotting those boundaries.
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Abbasali
Monzeli
Department of Mathematics, Islamic Azad University of Central Tehran Branch, Iran.
Iran
abbas_monzeli@yahoo.com


Behrouz
Daneshian
Islamic Azad University of Central Tehran Branch, Iran.
Iran
be_daneshian@yahoo.com


Gasem
Tohidi
Islamic Azad University of Central Tehran Branch, Iran.
Iran
gas.tohidi@iauctb.ac.ir


Masud
Sanei
Islamic Azad University of Central Tehran Branch, Iran.
Iran
m_sanei@iauctb.ac.ir


Shabnam
Razavian
Islamic Azad University of Central Tehran Branch, Iran.
Iran
sh_razaveian@iauctb.ac.ir
Data Envelopment Analysis
Undesirable inputs and outputs
Efficiency
Efficient boundaries
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