ORIGINAL_ARTICLE
Supplier selection using fuzzy AHP method and D-Numbers
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. Multi-criteria decisions are approaches that deal with ranking and selecting one or more suppliers from a set of suppliers. Multi-criteria 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 descriptive-survey 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 D-numbers. 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.
http://www.journal-fea.com/article_114153_3f82131eba51b9ec5c4e96603679a0ba.pdf
2020-03-01T11:23:20
2020-11-29T11:23:20
1
14
10.22105/jfea.2020.248437.1007
Supply chain
Management
Fuzzy AHP Method
D-Numbers
Payam
Shafi Salimi
payam_shafi@yahoo.com
true
1
Ayandegan Institute of Higher Education, Tonekabon, Iran.
Ayandegan Institute of Higher Education, Tonekabon, Iran.
Ayandegan Institute of Higher Education, Tonekabon, Iran.
AUTHOR
Seyyed Ahmad
Edalatpanah
saedalatpanah@gmail.com
true
2
Ayandegan Institute of Higher Education, Tonekabon, Iran.
Ayandegan Institute of Higher Education, Tonekabon, Iran.
Ayandegan Institute of Higher Education, Tonekabon, Iran.
LEAD_AUTHOR
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.
1
[2] Fallah, S. Qadir, A. H., & Qadir, H. (2017). Two-objective 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.
2
[3] Genovese, A., Acquaye, A., Ibn-Mohammed, 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), 188-205.
3
[4] Tamošaitienė, J., Valipour, A., Yahaya, N., Md Noor, N., & Antuchevičienė, J. (2017). Hybrid SWARA-COPRAS method for risk assessment in deep foundation excavation project: An Iranian case study. Journal of civil engineering and management, 23(4), 524-532.
4
[5] Liu, R. & Hai, L. (2005). The voting analytic hierarchy process method for selecting supplier. International journal of production economics, 97(3), 308-317.
5
[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), 289-301.
6
[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, 3749-3758.
7
[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 grey-DEMATEL approach. Journal of cleaner production, 134, 469-481.
8
[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.
9
[10] Shahgholian, K., Shahraki, A., Waezi, Z. (2011). Multi-criteria group decision for supplier selection with fuzzy approach. 11th Iranian fuzzy systems conference.
10
[11] Ming, Z. H. A., Xing. L. I. U. (2008). Research on mobile supply chain management Based Ubiquitous Network. IEE, 33-51.
11
[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
12
[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. Tbilisi-Georgia, Permanent Secretariat in collaboration with Imam Sadegh University
13
[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 University-Organization Tabriz Industrial .
14
[15] Shafia, M. A., Mahdavi Mazdeh, M., Pournader, M., &Bagherpour, M. (2016). Presenting a two-level data envelopment analysis model in supply chain risk management in order to select a supplier.
15
[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.
16
[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, 72-84.
17
[18] Reefke, H., & Sundaram, D. (2017). Key themes and research opportunities in sustainable supply chain management–identification and evaluation. Omega, 66, 195-211.
18
[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), 763-788.
19
[20] Rad, R. S., & Nahavandi, N. (2018). A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. Journal of cleaner production, 196, 1549-1565.
20
[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 AHP-DEMATEL. Benchmarking: an international journal, 25(5), 1528-1547.
21
[22] Wang, Z., Luo, C., & Luo, T. (2018). Selection optimization of bloom filter-based index services in ubiquitous embedded systems. International conference on web Services (pp. 231-245).
22
ORIGINAL_ARTICLE
A new approach for ranking of intuitionistic fuzzy numbers
The concept of an intuitionistic fuzzy number (I F N) is of importance for representing an ill-known 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 ill-known 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.
http://www.journal-fea.com/article_114137_62c69e2442d72bfa78cee799043333bf.pdf
2020-03-01T11:23:20
2020-11-29T11:23:20
15
26
10.22105/jfea.2020.247301.1003
intuitionistic fuzzy sets
intuitionistic fuzzy numbers
Trapezoidal Intuitionistic
Fuzzy Numbers
triangular intuitionistic fuzzy numbers
magnitude of intuitionistic fuzzy number
Suresh
Mohan
mathssuresh84@gmail.com
true
1
Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.
Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.
Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.
LEAD_AUTHOR
Arun Prakash
Kannusamy
arunfuzzy@gmail.com
true
2
Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.
Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.
Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.
AUTHOR
Vengataasalam
Samiappan
sv.maths@gmail.com
true
3
Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.
Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.
Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.
AUTHOR
Atanassov K. (1986). Intuitionistic fuzzy sets. Fuzzy sets and systems, 20, 87-96.
1
[2] Abbasbandy, S., & Hajjari, T. (2009). A new approach for ranking of trapezoidal fuzzy numbers. Computers & mathematics with applications, 57(3), 413-419.
2
[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), 359-369.
3
[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. 563-569). Atlantis Press.
4
[5] Grzegrorzewski, P. (2003). The hamming distance between intuitionistic fuzzy sets. Proceedings of the 10th IFSA world congress, Istanbul, Turkey (Vol. 30, pp. 35-38).
5
[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), 1557-1570.
6
[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), 522-530.
7
[8] Mitchell, H. B. (2004). Ranking-intuitionistic fuzzy numbers. International journal of uncertainty, fuzziness and knowledge-based systems, 12(03), 377-386.
8
[9] Mahapatra, G. S., & Mahapatra, G. S. (2010). Intuitionistic fuzzy fault tree analysis using intuitionistic fuzzy numbers. International mathematical forum, 5(21), 1015-1024.
9
[10] Nehi, H. M. (2010). A new ranking method for intuitionistic fuzzy numbers. International journal of fuzzy systems, 12(1).
10
[11] Nayagam, V.L.G., Venkateshwari, G., Sivaraman, G., (2008). Ranking of intuitionistic fuzzy numbers, Proc. of international conference on fuzzy systems 2008, Fuzz-IEEE (pp. 1971-1974).
11
[12] Nagoorgani, A., & Ponnalagu, K. (2012). A new approach on solving intuitionistic fuzzy linear programming problem. Applied mathematical sciences, 6(70), 3467-3474.
12
[13] Parvathi, R., & Malathi, C. (2012). Arithmetic operations on symmetric trapezoidal intuitionistic fuzzy numbers. International Journal of Soft Computing and Engineering, 2, 268-273.
13
[14] Parvathi, R., & Malathi, C. (2012). Intuitionistic fuzzy simplex method. International journal of computer applications, 48(6), 39-48.
14
[15] Rezvani, S. (2013). Ranking method of trapezoidal intuitionistic fuzzy numbers. Annals of fuzzy mathematics and informatics, 5(3), 515-523.
15
[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), 08-13.
16
[17] Jianqiang, W., & Zhong, Z. (2009). Aggregation operators on intuitionistic trapezoidal fuzzy number and its application to multi-criteria decision making problems. Journal of systems engineering and electronics, 20(2), 321-326.
17
[18] Wang, J. (2008). Overview on fuzzy multi-criteria decision-making approach. Control and decision, 23(6), 601-606.
18
ORIGINAL_ARTICLE
Application of transportation problem under pentagonal Neutrosophic environment
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 single-valued 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.
http://www.journal-fea.com/article_114140_88d3ae2d70d6f2087f11aca75c3a77c8.pdf
2020-03-01T11:23:20
2020-11-29T11:23:20
27
41
10.22105/jfea.2020.246633.1001
Transportation problem
pentagonal neutrosophic numbers
Linear Programming
Sapan
Kumar Das
cool.sapankumar@gmail.com
true
1
Department of Revenue, Ministry of Finance, Govt. of India.
Department of Revenue, Ministry of Finance, Govt. of India.
Department of Revenue, Ministry of Finance, Govt. of India.
LEAD_AUTHOR
Hitchcock, F. L. (1941). The distribution of a product from several sources to numerous localities. Journal of mathematics and physics, 20(1-4), 224-230.
1
[2] Dantzig, G. B., & Thapa, M. N. (2006). Linear programming 2: theory and extensions. Springer Science & Business Media.
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[3] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
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[4] Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy sets and systems, 1(1), 45-55.
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[5] Chanas, S., Kołodziejczyk, W., & Machaj, A. (1984). A fuzzy approach to the transportation problem. Fuzzy sets and systems, 13(3), 211-221.
5
[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), 509-519.
6
[7] Dinagar, D. S., & Palanivel, K. (2009). The transportation problem in fuzzy environment. International journal of algorithms, computing and mathematics, 2(3), 65-71.
7
[8] Kaur, A., & Kumar, A. (2011). A new method for solving fuzzy transportation problems using ranking function. Applied mathematical modelling, 35(12), 5652-5661.
8
[9] Pandian, P., & Natarajan, G. (2010). A new algorithm for finding a fuzzy optimal solution for fuzzy transportation problems. Applied mathematical sciences, 4(2), 79-90.
9
[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), 14-21.
10
[11] Kaur, A., & Kumar, A. (2012). A new approach for solving fuzzy transportation problems using generalized trapezoidal fuzzy numbers. Applied soft computing, 12(3), 1201-1213.
11
[12] Kundu, P., Kar, S., & Maiti, M. (2014). Multi-objective solid transportation problems with budget constraint in uncertain environment. International journal of systems science, 45(8), 1668-1682.
12
[13] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A pythagorean fuzzy approach to the transportation problem. Complex & intelligent systems, 5(2), 255-263.
13
[14] Liu, P., Yang, L., Wang, L., & Li, S. (2014). A solid transportation problem with type-2 fuzzy variables. Applied soft computing, 24, 543-558.
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[15] Tada, M., & Ishii, H. (1996). An integer fuzzy transportation problem. Computers & mathematics with applications, 31(9), 71-87.
15
[16] Liu, S. T., & Kao, C. (2004). Solving fuzzy transportation problems based on extension principle. European journal of operational research, 153(3), 661-674.
16
[17] Saad, O. M., & Abass, S. A. (2003). A parametric study on tranportation problem under fuzzy environment. Journal of fuzzy mathematics, 11(1), 115-124.
17
[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), 49-69.
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[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), 299-305.
19
[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.
20
[21] Das, S. K., & Edalatpanah, S. A. (2020). New insight on solving fuzzy linear fractional programming in material aspects. Fuzzy optimization and modelling, 1, 1-7.
21
[22] Das, S. K., Mandal, T., & Edalatpanah, S. A. (2017). A new approach for solving fully fuzzy linear fractional programming problems using the multi-objective linear programming. RAIRO-operations research, 51(1), 285-297.
22
[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), 296-309.
23
[24] Atanassov K. (1986). Intuitionistic fuzzy sets. Fuzzy sets and systems, 20, 87-96.
24
[25] Ebrahimnejad, A., & Verdegay, J. L. (2018). A new approach for solving fully intuitionistic fuzzy transportation problems. Fuzzy optimization and decision making, 17(4), 447-474.
25
[26] Nagoorgani, A., & Abbas, S. (2013). A new method for solving intuitionistic fuzzy transportation problem. Applied mathematical science, 7(28), 1357–1365.
26
[27] Singh, S. K., & Yadav, S. P. (2016). A new approach for solving intuitionistic fuzzy transportation problem of type-2. Annals of operations research, 243(1-2), 349-363.
27
[28] Singh, S. K., & Yadav, S. P. (2015). Efficient approach for solving type-1 intuitionistic fuzzy transportation problem. International journal of system assurance engineering and management, 6(3), 259-267.
28
[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), 262-272.
29
[30] Hussain, R. J., & Kumar, P. S. (2012). Algorithmic approach for solving intuitionistic fuzzy transportation problem. Applied mathematical sciences, 6(80), 3981-3989.
30
[31] Aggarwal, S., & Gupta, C. (2017). Sensitivity analysis of intuitionistic fuzzy solid transportation problem. International journal of fuzzy systems, 19(6), 1904-1915.
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[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), 460-472.
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[33] Mahmoodirad, A., Allahviranloo, T., & Niroomand, S. (2019). A new effective solution method for fully intuitionistic fuzzy transportation problem. Soft computing, 23(12), 4521-4530.
33
[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, 367-375.
34
[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), 293-311.
35
[36] Smarandache, F. (1998). A unifying field in logics: Neutrosophic logic, Neutrosophy, Neutrosophic set, Neutrosophic probability (fifth eition). AmericanResearchPress, Rchoboth.
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[37] Wang, H., Smarandache, F., Zhang, Y. Q., & Sunderraman, R. (2010). Single valued Neutrosophic sets. Multispace and multistructure, 4, 410–413.
37
[38] Ye, J. (2018). Neutrosophic number linear programming method and its application under Neutrosophic number environments. Soft computing, 22(14), 4639-4646.
38
[39] Roy, R., & Das, P. (2015). A Multi-objective production planning problem based on Neutrosophiclinear programming approach. Infinite Study.
39
[40]Abdel-Basset, 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), 1595-1605.
40
[41] Edalatpanah, S. A. (2020). A direct model for triangular Neutrosophic linear programming. International journal of neutrosophic science, 1(1), 19-28.
41
[42] Maiti, I., Mandal, T., & Pramanik, S. (2019). Neutrosophic goal programming strategy for multi-level multi-objective linear programming problem. Journal of ambient intelligence and humanized computing, 1-12.
42
[43] Edalatpanah, S. A. (2020). Data envelopment analysis based on triangular Neutrosophic numbers. CAAI transactions on intelligence technology. Retrieved from
43
https://www.researchgate.net/profile/Sa_Edalatpanah3/publication/339979254_Data_Envelopment_Analysis_Based_on_Triangular_Neutrosophic_Numbers/links/5e86e5fb92851c2f5277b0bc/Data-Envelopment-Analysis-Based-on-Triangular-Neutrosophic-Numbers.pdf
44
[44] Mohamed, M., Abdel-Basset, M., Zaied, A. N. H., & Smarandache, F. (2017). Neutrosophic integer programming problem. Infinite Study.
45
[45] Banerjee, D., & Pramanik, S. (2018). Single-objective linear goal programming problem with Neutrosophic numbers. Infinite Study.
46
[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), 13-24.
47
[47] Das, S. K., & Chakraborty, A. (2020). A new approach to evaluate linear programming problem in pentagonal Neutrosophic environment. Complex & intelligent systems, 1-10.
48
[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
[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
[50] Korukoğlu, S., & Ballı, S. (2011). An improved vogel's approximatio method for the transportation problem. Mathematical and computational applications, 16(2), 370-381.
51
[51] Bharati, S. K. (2019). Trapezoidal intuitionistic fuzzy fractional transportation problem. Soft computing for problem solving (pp. 833-842). Springer, Singapore.
52
[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), 218-229.
53
[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
[54] Maiti, I., Mandal, T., Pramanik, S., Das, S.K. (2020). Solving multi-objective linear fractional programming problem based on Stanojevic’s normalization technique under fuzzy environment. International journal of operation research. DOI: 10.1504/IJOR.2020.10028794.
55
ORIGINAL_ARTICLE
Precise services and supply chain prioritization in manufacturing companies using cost analysis provided in a fuzzy environment
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.
http://www.journal-fea.com/article_114139_9546626dcce3841d5605bac3bbec6994.pdf
2020-03-01T11:23:20
2020-11-29T11:23:20
42
59
10.22105/jfea.2020.248187.1006
Supply chain management
Data Envelopment Analysis
Supply Chain Efficiency
Alireza
Marzband
marzband.omidico@gmail.com
true
1
Ayandegan Institute of Higher Education, Tonekabon, Iran.
Ayandegan Institute of Higher Education, Tonekabon, Iran.
Ayandegan Institute of Higher Education, Tonekabon, Iran.
LEAD_AUTHOR
ORIGINAL_ARTICLE
Using interval arithmetic for providing a MADM approach
The VIKOR method was developed for Multi-Criteria 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 multi-criteria 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.
http://www.journal-fea.com/article_114142_dd9c7cc342363e56e0afad71740cd7e9.pdf
2020-03-01T11:23:20
2020-11-29T11:23:20
60
68
10.22105/jfea.2020.247946.1004
Decision Making
Multi Attribute Decision Making (MADM)
Interval arithmetic
Hossein
Jafari
hossein_jafari_123@yahoo.com
true
1
Young Researchers and Elite Club, Arak Branch, Islamic Azad University, Arak, Iran.
Young Researchers and Elite Club, Arak Branch, Islamic Azad University, Arak, Iran.
Young Researchers and Elite Club, Arak Branch, Islamic Azad University, Arak, Iran.
LEAD_AUTHOR
Mohammad
Ehsanifar
m-ehsanitfar@iau-arak.ac.ir
true
2
Department of Industrial Engineering, Islamic Azad University of Arak, Arak, Iran.
Department of Industrial Engineering, Islamic Azad University of Arak, Arak, Iran.
Department of Industrial Engineering, Islamic Azad University of Arak, Arak, Iran.
AUTHOR
[1] Yu, P. L. (1973). A class of solutions for group decision problems. Management science, 19(8), 936-946.
1
[2] Zeleny, M. (Ed.). (2012). Multiple criteria decision making Kyoto 1975 (Vol. 123). Springer Science & Business Media.
2
[3] Yu, P. L. (2013). Multiple-criteria decision making: concepts, techniques, and extensions (Vol. 30). Springer Science & Business Media.
3
[4] Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
4
[5] triantaphyllou, E. (2000). multi-criteria decision making methods: a comparative study. kluwer academic publishers, dordrecht.
5
[6] wang, h. f. (2000). Fuzzy multicriteria decision making—an overview. Journal of intelligent & Fuzzy Systems, 9(1, 2), 61-83.
6
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[8] Ribeiro, R. A. (1996). Fuzzy multiple attribute decision making: a review and new preference elicitation techniques. Fuzzy sets and systems, 78(2), 155-181.
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[9] Prékopa, A. (2013). Stochastic programming (Vol. 324). Springer Science & Business Media.
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[14] Jahanshahloo, G. R., Lotfi, F. H., & Izadikhah, M. (2006). An algorithmic method to extend TOPSIS for decision-making problems with interval data. Applied mathematics and computation, 175(2), 1375-1384.
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[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), 445-455.
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[16] Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European journal of operational research, 178(2), 514-529.
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[17] Choobineh, F., & Behrens, A. (1993). Use of intervals and possibility distributions in economic analysis. Journal of the operational research society, 43(9), 907-918.
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[18] Sengupta, A., & Pal, T. K. (2006). Solving the shortest path problem with interval arcs. Fuzzy optimization and decision making, 5(1), 71-89.
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[19] Alefeld, G., & Herzberger, J. (2012). Introduction to interval computation. Academic press.
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[20] Kearfott, R. B., & Kreinovich, V. (Eds.). (2013). Applications of interval computations (Vol. 3). Springer Science & Business Media.
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[21] Shaocheng, T. (1994). Interval number and fuzzy number linear programmings. Fuzzy sets and systems, 66(3), 301-306.
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[22] Moore, R., & Lodwick, W. (2003). Interval analysis and fuzzy set theory. Fuzzy sets and systems, 135(1), 5-9.
22
[23] Bhattacharyya, R. (2015). A grey theory based multiple attribute approach for R&D project portfolio selection. Fuzzy information and engineering, 7(2), 211-225.
23
ORIGINAL_ARTICLE
Fuzzy logic in accounting and auditing
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 two-part 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 decision-making processes. Because information plays an important role in economic decision-making, and no doubt, the quality of their, including accuracy in providing it to a wide range of users, can be useful for decision-making. 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.
http://www.journal-fea.com/article_114141_407394dcb76f15bdbc5d6db588b8fbd6.pdf
2020-03-01T11:23:20
2020-11-29T11:23:20
69
75
10.22105/jfea.2020.246647.1002
Fuzzy logic
Accounting
Auditing
ambiguity
Mohsen
Imeni
mohsen.imeni86@yahoo.com
true
1
Department of Accounting, Ayandegan Institute of Higher Education, Tonekabon, Iran.
Department of Accounting, Ayandegan Institute of Higher Education, Tonekabon, Iran.
Department of Accounting, Ayandegan Institute of Higher Education, Tonekabon, Iran.
LEAD_AUTHOR
[1] ZarifFard, A. (1999). The problem of ambiguity and lack of clarity in accounting. Accounting and auditing reviews, 8(27), 33-55. (In Persian)
1
[2] Zebda, A., & McEacham, M. (2008). Accounting expert systems and the treatment of uncertainty. The BUSINESS Review, 11(1), 1-13.
2
[3] Zebda, A. (1991). The problem of ambiguity and vagueness in accounting. Behavioral research in accounting, 3(1), 117-145.
3
[4] Ro, B. T. (1982). An analytical approach to accounting materiality. Journal of business finance & accounting, 9(3), 397-412.
4
[5] Kosko, B., & Toms, M. (1993). Fuzzy thinking: The new science of fuzzy logic (pp. 183-187). New York: Hyperion.
5
[6] Höglund, H. (2013). Fuzzy linear regression-based detection of earnings management. Expert systems with applications, 40(15), 6166-6172.
6
[7] [7] Azar, A., & Faraji, H. (2008). Fuzzy management science. Studies and Productivity Center Publications, Tehran, Iran. (In Persian)
7
[8] Namazi, M., & Karimi, M. (2013). Investigating the applications of fuzzy logic in accounting. Financial management perspective, 1(1), 9-36. (In Persian)
8
[9] Golmohammadi, D. (2011). Neural network application for fuzzy multi-criteria decision making problems. International journal of production economics, 131(2), 490-504.
9
[10] Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision making methods. In Fuzzy multiple attribute decision making (pp. 289-486). Springer, Berlin, Heidelberg.
10
[11] Friedlob, G. T., & Schleifer, L. L. (1999). Fuzzy logic: application for audit risk and uncertainty. Managerial auditing journal, 14(3), 127- 137.
11
[12] Pathak, J., Vidyarthi, N., & Summers, S. L. (2005). A fuzzy‐based algorithm for auditors to detect elements of fraud in settled insurance claims. Managerial auditing journal,20(6), 632-644.
12
[13] Comunale, C. L., & Sexton, T. R. (2005). A fuzzy logic approach to assessing materiality. Journal of emerging technologies in accounting, 2(1), 1-15.
13
[14] Dereli, T., Baykasoğlu, A., & Daş, G. S. (2007). Fuzzy quality-team formation for value added auditing: A case study. Journal of engineering and technology management, 24(4), 366-394.
14
[15] De Korvin, A., Shipley, M. F., & Omer, K. (2004). Assessing risks due to threats to internal control in a computer‐based accounting information system: a pragmatic approach based on fuzzy set theory. Intelligent systems in accounting, finance & management: international journal, 12(2), 139-152.
15
[16] Rahnamay Roodposhti, F., Kharadyar, S., & Imeni, M. (2016). The historical roots of stream researches in behavioral management accounting: Theories and research methods. Valued and behavioral accountings achievements, 1(1), 25-52. (In Persian)
16
[17] Rahnamay Roodposhti, F., Imeni, M., Sayadmanesh, S. (2019). BSC application and innovative methods of developed in the management accounting and strategic decisions of performance measurement. Journal of decisions and operations research, 4(3), 246-261.
17
[18] Oderanti, F. O., & De Wilde, P. (2010). Dynamics of business games with management of fuzzy rules for decision making. International journal of production economics, 128(1), 96-109.
18
[19] Cassia, L., Paleari, S., & Redondi, R. (2005). Management accounting systems and organizational structure. Small business economics, 25(4), 373-391.
19
[20] Rangone, A. (1997). Linking organizational effectiveness, key success factors and performance measures: an analytical framework. Management accounting research, 8(2), 207-219.
20
[21] Nagasawa, S. Y. (1997). Application of fuzzy theory to value engineering. Computers & industrial engineering, 33(3-4), 565-568.
21
[22] Nachtmann, H., & Needy, K. L. (2001). Fuzzy activity based costing: a methodology for handling uncertainty in activity based costing systems. The engineering economist, 46(4), 245-273.
22
[23] Nachtmann, H., & Needy, K. L. (2003). Methods for handling uncertainty in activity based costing systems. The engineering economist, 48(3), 259-282.
23
[24] Yuan, F. C. (2009). The use of a fuzzy logic-based system in cost-volume-profit analysis under uncertainty. Expert systems with applications, 36(2), 1155-1163.
24
[25] Pourali, M. R., Imeni, M., & Taherpour, G. R. (2013). The study of relationship between institutional shareholders and firm cash conversion cycle (CCC): evidence from Tehran stock exchange (TSE). International research journal of applied and basic sciences, 4 (9), 2735, 2741.
25
[26] Samadi Lorgani, M., Imeni, M. (2013). The relationship between working capital management and cash holding companies listed in Tehran stock exchange. Journal of management accounting and auditing knowledge, 2(5), 39-52. (In Persian)
26
ORIGINAL_ARTICLE
Efficiency study with undesirable inputs and outputs in DEA
Data Envelopment Analysis (DEA) is one of the well-known 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 Decision-Making 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 decision-making 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.
http://www.journal-fea.com/article_114143_0baa27d2278f215b8b3cf875978b8747.pdf
2020-03-01T11:23:20
2020-11-29T11:23:20
76
84
10.22105/jfea.2020.248018.1005
Data Envelopment Analysis
Undesirable inputs and outputs
Efficiency
Efficient boundaries
Abbasali
Monzeli
abbas_monzeli@yahoo.com
true
1
Department of Mathematics, Islamic Azad University of Central Tehran Branch, Iran.
Department of Mathematics, Islamic Azad University of Central Tehran Branch, Iran.
Department of Mathematics, Islamic Azad University of Central Tehran Branch, Iran.
LEAD_AUTHOR
Behrouz
Daneshian
be_daneshian@yahoo.com
true
2
Islamic Azad University of Central Tehran Branch, Iran.
Islamic Azad University of Central Tehran Branch, Iran.
Islamic Azad University of Central Tehran Branch, Iran.
AUTHOR
Gasem
Tohidi
gas.tohidi@iauctb.ac.ir
true
3
Islamic Azad University of Central Tehran Branch, Iran.
Islamic Azad University of Central Tehran Branch, Iran.
Islamic Azad University of Central Tehran Branch, Iran.
AUTHOR
Masud
Sanei
m_sanei@iauctb.ac.ir
true
4
Islamic Azad University of Central Tehran Branch, Iran.
Islamic Azad University of Central Tehran Branch, Iran.
Islamic Azad University of Central Tehran Branch, Iran.
AUTHOR
Shabnam
Razavian
sh_razaveian@iauctb.ac.ir
true
5
Islamic Azad University of Central Tehran Branch, Iran.
Islamic Azad University of Central Tehran Branch, Iran.
Islamic Azad University of Central Tehran Branch, Iran.
AUTHOR
[1] Walheer, B. (2020). Output, input, and undesirable output interconnections in data envelopment analysis: convexity and returns-to-scale. Annals of operations research, 284(1), 447-467.
1
[2] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
2
[3] Chen, Y., & Ali, A. I. (2002). Output–input ratio analysis and DEA frontier. European journal of operational research, 142(3), 476-479.
3
[4] Jahanshahloo, G. R., Lotfi, F. H., & Zohrehbandian, M. (2005). Finding the piecewise linear frontier production function in data envelopment analysis. Applied mathematics and computation, 163(1), 483-488.
4
[5] Wu, J., Xia, P., Zhu, Q., & Chu, J. (2019). Measuring environmental efficiency of thermoelectric power plants: a common equilibrium efficient frontier DEA approach with fixed-sum undesirable output. Annals of operations research, 275(2), 731-749.
5
[6] Kao, H. Y., Wu, D. J., & Huang, C. H. (2017). Evaluation of cloud service industry with dynamic and network DEA models. Applied mathematics and computation, 315, 188-202.
6
[7] Gómez-Calvet, R., Conesa, D., Gómez-Calvet, A. R., & Tortosa-Ausina, E. (2020). European energy efficiency evaluation based on the use of super-efficiency under undesirable outputs in SBM models. In Advances in efficiency and productivity II (pp. 193-208). Springer, Cham.
7
[8] Yu, S., Liu, J., & Li, L. (2020). Evaluating provincial eco-efficiency in China: an improved network data envelopment analysis model with undesirable output. Environmental science and pollution research, 27(7), 6886-6903.
8
[9] Song, M., Wang, J., Zhao, J., Baležentis, T., & Shen, Z. (2020). Production and safety efficiency evaluation in Chinese coal mines: accident deaths as undesirable output. Annals of operations research, 291, 827–845.
9