ORIGINAL_ARTICLE
A Two-Phase Approach for Supply Chain Network Design: A Real-World Case Study from Automotive Industry
Effective design and management of Supply Chain Networks (SCN) support the production and delivery of products at low cost, high quality, high variety, and short lead times. In this study, a SCN is designed for an automotive company by integrating various approaches. The study has been carried out in two phases: The first phase involves selecting suppliers and distributors by using Data Envelopment Analysis (DEA) and integer-programming model. In the second phase, first the priority ranking of selected suppliers and distributors is determined using the Analytical Hierarchy Process (AHP) and then these priority rankings are integrated into the transportation models developed to identify the optimal routing decisions for all members of the supply chain.
https://www.riejournal.com/article_57620_36ce5d0cecf5bc5125306941acd9dd01.pdf
2018-04-01
1
18
10.22105/riej.2018.103354.1029
Supply chain management
Data Envelopment Analysis
Integer programming
Analytical Hierarchy Process
Transportation problem
S.
Tunali
semra.tunali@ieu.edu.tr
1
Department of Business Administration, İzmir University of Economics, İzmir, Turkey
LEAD_AUTHOR
G.
Oztuzcu
gamze.cakir@opetfuchs.com.tr
2
Opet Fuchs Oil Company,35600 Cigli-Izmir, Turkey
AUTHOR
[1] Abdallah, T., Diabat, A., & Simchi-Levi, D. (2012). Sustainable supply chain design: a closed-loop formulation and sensitivity analysis. Production planning & control, 23(2-3), 120-133.
1
[2] Amin, S. H., & Zhang, G. (2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert systems with applications, 39(8), 6782-6791.
2
[3] Babazadeh, R., Razmi, J., Pishvaee, M. S., & Rabbani, M. (2017). A sustainable second-generation biodiesel supply chain network design problem under risk. Omega, 66, 258-277.
3
[4] Bai, X., & Liu, Y. (2016). Robust optimization of supply chain network design in fuzzy decision system. Journal of intelligent manufacturing, 27(6), 1131-1149.
4
[5] Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European journal of operational research, 233(2), 299-312.
5
[6] Chaabane, A., Ramudhin, A., & Paquet, M. (2012). Design of sustainable supply chains under the emission trading scheme. International journal of production economics, 135(1), 37-49.
6
[7] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
7
[8] Cohen, M. A., & Lee, H. L. (1985). Manufacturing strategy. In The management of productivity and technology in manufacturing (pp. 153-188). Springer, Boston, MA.
8
[9] Cohen, M. A., & Lee, H. L. (1988). Strategic analysis of integrated production-distribution systems: Models and methods. Operations research, 36(2), 216-228.
9
[10] Cohen, M. A., & Lee, H. L. (1989). Resource deployment analysis of global manufacturing and distribution networks. Journal of manufacturing and operations management, 2(2), 81-104.
10
[11] Desport, P., Lardeux, F., Lesaint, D., Cairano-Gilfedder, C. D., Liret, A., & Owusu, G. (2017). A combinatorial optimisation approach for closed-loop supply chain inventory planning with deterministic demand. European journal of industrial engineering, 11(3), 303-327.
11
[12] Easwaran, G., & Üster, H. (2010). A closed-loop supply chain network design problem with integrated forward and reverse channel decisions. Iie transactions, 42(11), 779-792.
12
[13] Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega, 45, 92-118.
13
[14] Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2017). Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transportation research part E: Logistics and transportation review, 101, 176-200.
14
[15] Geoffrion, A. M., & Graves, G. W. (1974). Multicommodity distribution system design by Benders decomposition. Management science, 20(5), 822-844.
15
[16] Govindan, K., & Fattahi, M. (2017). Investigating risk and robustness measures for supply chain network design under demand uncertainty: A case study of glass supply chain. International journal of production economics, 183, 680-699.
16
[17] Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European journal of operational research, 263(1), 108-141.
17
[18] Gupta, S., & Palsule-Desai, O. D. (2011). Sustainable supply chain management: review and research opportunities. IIMB management review, 23(4), 234-245.
18
[19] Hasani, A., & Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study. Transportation research part E: Logistics and transportation review, 87, 20-52.
19
[20] Jiang, D., Li, H., Yang, T., & Li, D. (2016). Genetic algorithm for inventory positioning problem with general acyclic supply chain networks. European journal of industrial engineering, 10(3), 367-384.
20
[21] Keyvanshokooh, E., Ryan, S. M., & Kabir, E. (2016). Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition. European journal of operational research, 249(1), 76-92.
21
[22] [Luthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of cleaner production, 140, 1686-1698.
22
[23] Talluri, S. (2000). A benchmarking method for business-process reengineering and improvement. International journal of flexible manufacturing systems, 12(4), 291-304.
23
[24] Talluri, S., & Baker, R. C. (2002). A multi-phase mathematical programming approach for effective supply chain design. European journal of operational research, 141(3), 544-558.
24
[25] Torabi, S. A., Namdar, J., Hatefi, S. M., & Jolai, F. (2016). An enhanced possibilistic programming approach for reliable closed-loop supply chain network design. International journal of production research, 54(5), 1358-1387.
25
[26] Zhou, G., & Min, H. (2011). Designing a closed-loop supply chain with stochastic product returns: a Genetic Algorithm approach. International journal of logistics systems and management, 9(4), 397-418.
26
ORIGINAL_ARTICLE
A Computer Simulation Model for Reliability Estimation of a Complex System
In today's competitive world, preventing from probable breakdowns can be act as a powerful leverage for managers. They are faced with large complex systems. Hence, the realistic estimation of the reliability of such systems has become increasingly important and it is a vital complicated task especially in the cases where the system configuration is too complicated to present it via a Reliability Block Diagram (RBD). The focus of this research is on the reliability estimation of the complex multi-component systems; each failure mechanism is deployed from a given failure density function. Hence, due to complexity arises from unknown RBD, current research methodology is set based on computer simulation modeling. After investigating the simulation model validity, an example is examined to reveal simulation method advantages. To assess the proposed method, a typical example has also been discussed.
https://www.riejournal.com/article_57406_d2d04e43dd38ac7d566b36a084cfcc22.pdf
2018-04-01
19
31
10.22105/riej.2018.109017.1032
System reliability estimation
Complex system
Fault Tree Analysis (FTA)
Reliability Block Diagram (RBD)
Simulation modeling
S.
Raissi
raissi@azad.ac.ir
1
Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
LEAD_AUTHOR
Sh.
Ebadi
ebaditehran@yahoo.com
2
Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
AUTHOR
[1] Bentley, J. P. (1999). Introduction to reliability and quality engineering. Prentice Hall.
1
[2] Weber, P., & Jouffe, L. (2006). Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN). Reliability engineering & system safety, 91(2), 149-162.
2
[3] Wilson, A. G., & Huzurbazar, A. V. (2007). Bayesian networks for multilevel system reliability. Reliability engineering & system safety, 92(10), 1413-1420.
3
[4] Li, W., & Zuo, M. J. (2008). Reliability evaluation of multi-state weighted k-out-of-n systems. Reliability engineering & system safety, 93(1), 160-167.
4
[5] Ge, H., & Asgarpoor, S. (2011). Parallel Monte Carlo simulation for reliability and cost evaluation of equipment and systems. Electric power systems research, 81(2), 347-356.
5
[6] Wang, Y., Li, L., Huang, S., & Chang, Q. (2012). Reliability and covariance estimation of weighted k-out-of-n multi-state systems. European journal of operational research, 221(1), 138-147.
6
[7] Jirgl, M., Bradac, Z., Stibor, K., & Havlikova, M. (2013). Reliability analysis of systems with a complex structure using Monte Carlo approach. IFAC proceedings volumes, 46(28), 461-466.
7
[8] Segovia, M. C., & Labeau, P. E. (2013). Reliability of a multi-state system subject to shocks using phase-type distributions. Applied mathematical modelling, 37(7), 4883-4904.
8
[9] Kim, Y., & Kang, W. H. (2013). Network reliability analysis of complex systems using a non-simulation-based method. Reliability engineering & system safety, 110, 80-88.
9
[10] Nguyen, K. A., Do, P., & Grall, A. (2015). Multi-level predictive maintenance for multi-component systems. Reliability engineering & system safety, 144, 83-94.
10
[11] Choi, I. H., & Chang, D. (2016). Reliability and availability assessment of seabed storage tanks using fault tree analysis. Ocean engineering, 120, 1-14.
11
[12] Wu, X., Zhu, Z., Fan, S., & Su, X. (2016). Failure and reliability prediction of engine systems using iterated nonlinear filters based state-space least square support vector machine method. Optik-International journal for light and electron optics, 127(3), 1491-1496.
12
[13] Fan, W., Yang, P., Ang, A. H., & Li, Z. (2016). Analysis of complex system reliability with correlated random vectors. Probabilistic engineering mechanics, 45, 61-69.
13
[14] Proppe, C. (2017). Markov Chain Monte Carlo Simulation Methods for Structural Reliability Analysis. Procedia engineering, 199, 1122-1127.
14
[15] Kumar, U. D., Crocker, J., Chitra, T., & Saranga, H. (2006). System Reliability. Reliability and six sigma, 105-141.
15
ORIGINAL_ARTICLE
A Fuzzy-AHP Method for Selection Best Apparel Item to Start-Up with New Garment Factory: A Case Study in Bangladesh
The readymade garments industry is rapidly growing and now it is the single highest export earner for Bangladesh. This business sector becomes an attractive investment destination to the country’s new young entrepreneurs upcoming due to its cheaper labor cost, lower investment cost, availability of resources, governments support, etc. than the other sectors. However, many other factors are often needed to consider in investing in this garments sectors. Again, in garments sectors, there is a wide range of apparel items like shirts, trousers, jackets, sweaters, etc. options that are available to invest. Different types of apparel items demand different types of resource requirements, diverse level of capital investment, operator’s skills, and it is also related to the many other factors. Again, all the investors are not in same stand points according to their business handling capabilities, capitals in hand, business locations and so many other aspects. This paper proposes a methodology for selection best apparel item among different alternatives that will provide a decision support to the investors in opening a new garment factory. The proposed methodology is based on Analytical Hierarchy Process (AHP) under fuzzy environment. The approaches allow the decision maker to use expert’s judgment in the form of linguistic expression in the evaluation process. In the application of proposed methodology, the best apparel item is selected for opening a garment factory in Bangladesh at present conditions.
https://www.riejournal.com/article_58089_ca611b1095cefb0f3c785e3c73665dcf.pdf
2018-04-01
32
50
10.22105/riej.2018.111802.1034
Fuzzy-AHP
Multi-criteria decision making
Garments
Product prioritization
T. K.
Biswas
tbiswasipe@gmail.com
1
Department of Industrial and amp, Production Engineering, Jessore University of Science and amp, Technology, Jessore- 7408, Bangladesh
LEAD_AUTHOR
S. M.
Akash
makash103@gmail.com
2
Department of Industrial and amp, Production Engineering, Jessore University of Science and amp, Technology, Jessore- 7408, Bangladesh
AUTHOR
S.
Saha
sajibsaha@yahoo.com
3
Department of Industrial and amp, Production Engineering, Jessore University of Science and amp, Technology, Jessore- 7408, Bangladesh
AUTHOR
[1] Hadad, Y., & Hanani, M. Z. (2011). Combining the AHP and DEA methodologies for selecting the best alternative. International journal of logistics systems and management, 9(3), 251-267.
1
[2] Oguztimur, S. (2011). Why Fuzzy Analytic Hierarchy Process Approach For Transport Problems? In Proceedings of 51st Congress of European Regional Science Association - ERSA, 1-10. Barcelona, SPAIN.
2
[3] Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of mathematical psychology, 15(3), 234-281.
3
[4] Saaty, T. L. (1994). Highlights and critical points in the theory and application of the analytic hierarchy process. European journal of operational research, 74(3), 426-447.
4
[5] Chang, C. W., Horng, D. J., & Lin, H. L. (2011). A measurement model for experts knowledge-based systems algorithm using fuzzy analytic network process. Expert systems with applications, 38(10), 12009-12017.
5
[6] Chan, F. T., & Kumar, N. (2007). Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega, 35(4), 417-431.
6
[7] Kilincci, O., & Onal, S. A. (2011). Fuzzy AHP approach for supplier selection in a washing machine company. Expert systems with Applications, 38(8), 9656-9664.
7
[8] Hefny, H. A., Elsayed, H. M., & Aly, H. F. (2013). Fuzzy multi-criteria decision making model for different scenarios of electrical power generation in Egypt. Egyptian informatics journal, 14(2), 125-133.
8
[9] Petkovic, J., Sevarac, Z., Jaksic, M. L., & Marinkovic, S. (2012). Application of fuzzy AHP method for choosing a technology within service company. Technics technologies education management-ttem, 7(1), 332-341.
9
[10] Saaty, T. L. (1980). Analytic Heirarchy Process. Wiley StatsRef: Statistics Reference Online.
10
[11] Sun, C. C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert systems with applications, 37(12), 7745-7754.
11
[12] Yaghoobi, T. (2018). Prioritizing key success factors of software projects using fuzzy AHP. Journal of software: Evolution and process, 30(1).
12
[13] Kahraman, C., Cebeci, U., & Ruan, D. (2004). Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey. International journal of production economics, 87(2), 171-184.
13
[14] Prascevic, N., & Prascevic, Z. (2017). Application of fuzzy AHP for ranking and selection of alternatives in construction project management. Journal of civil engineering and management, 23(8), 1123-1135.
14
[15] Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3), 649-655.
15
[16] Wang, Y. M., & Chin, K. S. (2011). Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology. International journal of approximate reasoning, 52(4), 541-553.
16
[17] Kusumawardani, R. P., & Agintiara, M. (2015). Application of fuzzy AHP-TOPSIS method for decision making in human resource manager selection process. Procedia computer science, 72, 638-646.
17
[18] Torfi, F., Farahani, R. Z., & Rezapour, S. (2010). Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives. Applied soft computing, 10(2), 520-528.
18
[19] Esmaili Dooki, A., Bolhasani, P., & Fallah, M. (2017). An Integrated Fuzzy AHP and Fuzzy TOPSIS Approach for Ranking and Selecting the Chief Inspectors Of Bank: A Case Study. Journal of applied research on industrial engineering, 4(1), 8-23.
19
[20] Mahendran, P., Moorthy, M. B. K., & Saravanan, S. (2014). A fuzzy AHP approach for selection of measuring instrument for engineering college selection. Applied mathematical sciences, 8(44), 2149-2161.
20
[21] Zadeh, L. A. (1996). Fuzzy sets. In Fuzzy sets, fuzzy logic, and fuzzy systems (pp. 394-432). doi.org/10.1142/9789814261302_0021
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[22] Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management science, 17(4), B-141.
22
[23] Bouyssou, D. (2000). Evaluation and decision models: a critical perspective (Vol. 32). Springer Science & Business Media.
23
[24] Chang, D. Y. (1992). Extent analysis and synthetic decision. Optimization techniques and applications, 1(1), 352-355.
24
[25] Cheng, C. H. (1997). Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European journal of operational research, 96(2), 343-350.
25
[26] Cheng, C. H., Yang, K. L., & Hwang, C. L. (1999). Evaluating attack helicopters by AHP based on linguistic variable weight. European journal of operational research, 116(2), 423-435.
26
[27] Ruoning, X., & Xiaoyan, Z. (1992). Extensions of the analytic hierarchy process in fuzzy environment. Fuzzy sets and Systems, 52(3), 251-257.
27
[28] Arikan, F. (2015). An interactive solution approach for multiple objective supplier selection problem with fuzzy parameters. Journal of intelligent manufacturing, 26(5), 989-998.
28
[29] Güngör, Z., Serhadlıoğlu, G., & Kesen, S. E. (2009). A fuzzy AHP approach to personnel selection problem. Applied soft computing, 9(2), 641-646.
29
[30] Chamodrakas, I., Batis, D., & Martakos, D. (2010). Supplier selection in electronic marketplaces using satisficing and fuzzy AHP. Expert systems with applications, 37(1), 490-498.
30
[31] Ayhan, M. B. (2013). A fuzzy AHP approach for supplier selection problem: A case study in a Gear motor company. International journal of managing value and supply chains (IJMVSC), 4(3).
31
[32] Kılıç, H. S., & Çevikcan, E. (2011). Job selection based on fuzzy AHP: an investigation including the students of Istanbul Technical University Management Faculty. International journal of business and management studies, 3(1), 173-182.
32
[33] Saad, S. M., Kunhu, N., & Mohamed, A. M. (2016). A fuzzy-AHP multi-criteria decision-making model for procurement process. International journal of logistics systems and management, 23(1), 1-24.
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[34] Enea, M., & Piazza, T. (2004). Project selection by constrained fuzzy AHP. Fuzzy optimization and decision making, 3(1), 39-62.
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[35] Shaygan, A., & Testik, Ö. M. (2017). A fuzzy AHP-based methodology for project prioritization and selection. Soft computing, 1-11.
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36
[37] Bozbura, F. T., & Beskese, A. (2007). Prioritization of organizational capital measurement indicators using fuzzy AHP. International journal of approximate reasoning, 44(2), 124-147.
37
[38] Mendoza, A., & Ventura, J. A. (2008). An effective method to supplier selection and order quantity allocation. International journal of business and systems research, 2(1), 1-15.
38
[39] Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98.
39
[40] Kahraman, C., Cebeci, U., & Ulukan, Z. (2003). Multi-criteria supplier selection using fuzzy AHP. Logistics information management, 16(6), 382-394.
40
[41] Lee, S. (2014). Determination of priority weights under multiattribute decision-making situations: AHP versus fuzzy AHP. Journal of construction engineering and management, 141(2), 05014015.
41
[42] Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3), 649-655.
42
[43] Kwong, C. K., & Bai, H. (2003). Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach. Iie transactions, 35(7), 619-626.
43
[44] Gumus, A. T. (2009). Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology. Expert systems with applications, 36(2), 4067-4074.
44
[45] Hadad, Y., & Hanani, M. Z. (2011). Combining the AHP and DEA methodologies for selecting the best alternative. International journal of logistics systems and management, 9(3), 251-267.
45
ORIGINAL_ARTICLE
Evaluation of Factors Affecting the Productivity of RMG in Bangladesh: A Fuzzy AHP Approach
Recently, the competitiveness and awareness of productivity have increased rapidly among different industries. Hence, the performance evaluation of the criteria affecting the productivity is needed to improve productivity and strengthen the management of the organization. In Bangladesh, Ready Made Garments (RMGs) is one of the most probable and profitable sectors which is considered as the main economic strength of the country. In this study, a two-phased research method has been projected to find out some governing factors affecting industry’s output. In the first phase, six criteria associated with the productivity have been identified based on literature, inputs from experts, opinions from the officials and managers of six garments industries in Bangladesh. In the second phase, among different MCDM tools, Fuzzy Analytic Hierarchy Process (FAHP) has been used for evaluating criteria weights and ranking the criteria. Among several criteria, line-balancing criterion has been found as the most important factor to improve the RMG’s productivity.
https://www.riejournal.com/article_51116_b057f51e5a5f9ced90c0881c1be1339d.pdf
2018-04-01
51
60
10.22105/riej.2017.94987.1011
Productivity
FAHP
RMG
Bangladesh
P. K.
Halder
pobitra.halder@gmail.com
1
Department of Chemical and Environmental Engineering, School of Engineering, RMIT University, Melbourne, Victoria 3001, Australia.
LEAD_AUTHOR
C. L.
Karmarker
k.chitroleka@gmail.com
2
Department of Industrial and Production Engineering, Jessore University of Science and Technology, Jessore-7408, Bangladesh.
AUTHOR
B.
Kundu
bishwa100703@gmail.com
3
Department of Industrial and Production Engineering, Jessore University of Science and Technology, Jessore-7408, Bangladesh.
AUTHOR
T.
Daniel
tapandaniel@yahoo.com
4
Department of Industrial and Production Engineering, Jessore University of Science and Technology, Jessore-7408, Bangladesh.
AUTHOR
[1] Shayan, E., & Sobhanallahi, A. (2002). Productivity gains by cellular manufacturing. Production planning & control, 13(6), 507-516.
1
[2] San, G., Huang, T. C., & Huang, L. H. (2008). Does labour quality matter on productivity growth? The case of the Taiwanese manufacturing industry. Total quality management & business excellence, 19(10), 1043-1053.
2
[3] Chaudhuri, A., Koudal, P., & Seshadri, S. (2010). Productivity and capital investments: An empirical study of three manufacturing industries in India. IIMB management review, 22(3), 65-79.
3
[4] Liu, T., & Li, K. W. (2012). Analyzing China's productivity growth: Evidence from manufacturing industries. Economic systems, 36(4), 531-551.
4
[5] Kottawatta, K. H. H. (2011). Impact of attitudinal factors on job performance of executives and non-executive employees in apparel industry in Sri Lanka. Sri lankan journal of human resource management, 1(1).
5
[6] Tangen, S. (2003). An overview of frequently used performance measures. Work study, 52(7), 347-354.
6
[7] Tang, Y. C., & Beynon, M. J. (2005). Application and development of a fuzzy analytic hierarchy process within a capital investment study. Journal of economics and management, 1(2), 207-230.
7
[8] Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making. Lecture notes in economics and mathematical systems.
8
[9] Aissaoui, N., Haouari, M., & Hassini, E. (2007). Supplier selection and order lot sizing modeling: A review. Computers & operations research, 34(12), 3516-3540.
9
[10] De Boer, L., Labro, E., & Morlacchi, P. (2001). A review of methods supporting supplier selection. European journal of purchasing & supply management, 7(2), 75-89.
10
[11] Ho, W. (2008). Integrated analytic hierarchy process and its applications–A literature review. European journal of operational research, 186(1), 211-228.
11
[12] El-Sawalhi, N., Eaton, D., & Rustom, R. (2007). Contractor pre-qualification model: State-of-the-art. International journal of project management, 25(5), 465-474.
12
[13] Bruno, G., Esposito, E., Genovese, A., & Passaro, R. (2012). AHP-based approaches for supplier evaluation: Problems and perspectives. Journal of purchasing and supply management, 18(3), 159-172.
13
[14] Chai, J., Liu, J. N., & Ngai, E. W. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert systems with applications, 40(10), 3872-3885.
14
[15] Ordoobadi, S. M. (2009). Development of a supplier selection model using fuzzy logic. Supply chain management: An international journal, 14(4), 314-327.
15
[16] Labib, A. W. (2011). A supplier selection model: a comparison of fuzzy logic and the analytic hierarchy process. International journal of production research, 49(21), 6287-6299.
16
[17] Van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty's priority theory. Fuzzy sets and systems, 11(1-3), 229-241.
17
[18] Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy sets and systems, 17(3), 233-247.
18
[19] Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3), 649-655.
19
[20] Kutlu, A. C., & Ekmekçioğlu, M. (2012). Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert systems with applications, 39(1), 61-67.
20
[21] Chang, B., Chang, C. W., & Wu, C. H. (2011). Fuzzy DEMATEL method for developing supplier selection criteria. Expert systems with Applications, 38(3), 1850-1858.
21
[22] Nakandala, D., Samaranayake, P., & Lau, H. C. (2013). A fuzzy-based decision support model for monitoring on-time delivery performance: A textile industry case study. European journal of operational research, 225(3), 507-517.
22
[23] Ganga, G. M. D., Carpinetti, L. C. R., & Politano, P. R. (2011). A fuzzy logic approach to supply chain performance management. Gestão & produção, 18(4), 755-774.
23
[24] Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied soft computing, 12(6), 1668-1677.
24
[25] Altinoz, C. (2008). Supplier selection for industry: a fuzzy rule-based scoring approach with a focus on usability. International journal of integrated supply management, 4(3-4), 303-321.
25
[26] Heo, E., Kim, J., & Boo, K. J. (2010). Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renewable and sustainable energy reviews, 14(8), 2214-2220.
26
[27] Tasri, A., & Susilawati, A. (2014). Selection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia. Sustainable energy technologies and assessments, 7, 34-44.
27
[28] Thengane, S. K., Hoadley, A., Bhattacharya, S., Mitra, S., & Bandyopadhyay, S. (2014). Cost-benefit analysis of different hydrogen production technologies using AHP and Fuzzy AHP. International journal of hydrogen energy, 39(28), 15293-15306.
28
[29] Dong, Q., & Cooper, O. (2016). An orders-of-magnitude AHP supply chain risk assessment framework. International journal of production economics, 182, 144-156.
29
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30
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31
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35
ORIGINAL_ARTICLE
Analyzing the Drivers of Green Manufacturing Using an Analytic Network Process Method: A Case Study
Todays, some challenges such as soil, water and air pollution, and severe health hazards to humanity are the results of growing manufacturer in the real world. These challenges are so-called posing risk for sustainable development of the planet. In this matter, the need of achieving higher economic prosperity, along with the least environmental impact has led to a new manufacturing paradigm so-called Green Manufacturing (GM). In this way, Small and Medium-sized Enterprises (SMEs) in Iran with ISO 9001 and ISO 14001 certification, and Just-In-Time inventory control, which make up 99.8 percent of all businesses, are responsible for 70 percent of all industrial pollution. In this paper, we explore the drivers of the GM of SMEs’ environmental processes in field of the industrial chemistry and their impacts on GM. Unfortunately, there is no proper work considering a wide range of drivers of GM in an industrial chemistry. We evaluate 500 Iran SMEs in the field on industrial chemistry, and select the Tage Company as a case study for this purpose considering its Greenhouse Gas Emission (GHG). This paper aims to gather the common drivers of GM from several sources on a case study in Tage Iran Company, as well as to analyze them by utilizing Analytical Network Process (ANP) in an uncertain environment. To tackle this uncertainty, the fuzzy ANP is utilized to prioritize the barriers to GM based on environmental, social, and economic perspectives. Finally, based on the obtained drivers, the fuzzy ANP identifies essential critical driver and the priority of drivers by utilizing a pair-wise comparison
https://www.riejournal.com/article_58366_7c7028306568587a6b586ccaaa7c5385.pdf
2018-04-01
61
83
10.22105/riej.2018.108563.1031
Greenhouse gas emission
Water and air pollution
Green manufacturing
Analytical network process
Industrial chemistry
M.
Barzegar
mb5229@yahoo.com
1
Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
AUTHOR
R.
Ehtesham Rasi
rezaehteshamrasi@gmail.com
2
Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
AUTHOR
A. H.
Niknamfar
niknamfar@yahoo.com
3
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
LEAD_AUTHOR
[1] Johansson, G., & Winroth, M. (2009). Lean vs. Green manufacturing: Similarities and differences. Proceeding of the 16th international annual euroma conference on implementation realizing operations management knowledge, (pp. 14-17).
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3
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4
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5
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[7] Hui, I. K., Chan, A. H., & Pun, K. F. (2001). A study of the environmental management system implementation practices. Journal of cleaner production, 9(3), 269-276.
7
[8] Kapur, A., Baldwin, C., Swanson, M., Wilberforce, N., McClenachan, G., & Rentschler, M. (2012). Comparative life cycle assessment of conventional and Green Seal-compliant industrial and institutional cleaning products. The international journal of life cycle assessment, 17(4), 377-387.
8
[9] Vachon, S., & Klassen, R. D. (2007). Supply chain management and environmental technologies: the role of integration. International journal of production research, 45(2), 401-423.
9
[10] Hillary, R. (2004). Environmental management systems and the smaller enterprise. Journal of cleaner production, 12(6), 561-569.
10
[11] Govindan, K., Diabat, A., & Shankar, K. M. (2015). Analyzing the drivers of green manufacturing with fuzzy approach. Journal of cleaner production, 96, 182-193.
11
[12] Corbett, C. J., & DeCroix, G. A. (2001). Shared-savings contracts for indirect materials in supply chains: Channel profits and environmental impacts. Management science, 47(7), 881-893.
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[14] Preuss, L. (2005). The green multiplier: A study of environmental protection and the supply chain. Springer.
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[15] Diabat, A., & Govindan, K. (2011). An analysis of the drivers affecting the implementation of green supply chain management. Resources, conservation and recycling, 55(6), 659-667.
15
[16] Agan, Y., Acar, M. F., & Borodin, A. (2013). Drivers of environmental processes and their impact on performance: a study of Turkish SMEs. Journal of cleaner production, 51, 23-33.
16
[17] Sawyer, J., & Evans, N. (2010). An investigation into the social and environmental responsibility behaviours of regional small businesses in relation to their impact on the local community and immediate environment. Australasian journal of regional studies, 16(2), 253.
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[18] Green, K., Morton, B., & New, S. (1996). Purchasing and environmental management: interactions, policies and opportunities. Business strategy and the environment, 5(3), 188-197.
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[19] Perez‐Sanchez, D., Barton, J. R., & Bower, D. (2003). Implementing environmental management in SMEs. Corporate social responsibility and environmental management, 10(2), 67-77.
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[20] Cramer, J., Dral, P., & Roes, B. (1991). Product information exchange about environmental aspects between producers. Ministry of housing, physical planning, and environment. The Netherlands.
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[21] Theyel, G. (2006). Customer and Supplier Relations for Environmental Performance. In J. Sarkis (Ed.), Greening the Supply Chain (pp. 139-149). Springer, London.
21
[22] Smith, N. C. (2003). Corporate social responsibility: whether or how? California management review, 45(4), 52-76.
22
[23] Saaty, T. L. (2005). Theory and applications of the analytic network process. Pittsburgh. PA, RWS Publication.
23
[24] Saaty, T. L., & Vargas, L. G. (2002). Decision Making With the Analytic Network Process. Springer, Boston, MA.
24
[25] Lee, Y., & Kozar, K. A. (2006). Investigating the effect of website quality on e-business success: An analytic hierarchy process (AHP) approach. Decision support systems, 42(3), 1383-1401.
25
[26] Ulutaş, B. H. (2005). Determination of the appropriate energy policy for Turkey. Energy, 30(7), 1146-1161.
26
[27] Kumar Sharma, S., & Bhat, A. (2014). Modelling supply chain agility enablers using ISM. Journal of modelling in management, 9(2), 200-214.
27
[28] Niemira, M. P., & Saaty, T. L. (2004). An analytic network process model for financial-crisis forecasting. International journal of forecasting, 20(4), 573-587.
28
[29] Neaupane, K. M., & Piantanakulchai, M. (2006). Analytic network process model for landslide hazard zonation. Engineering geology, 85(3-4), 281-294.
29
[30] Piantanakulchai, M. (2005). Analytic network process model for highway corridor planning. Proceedings of the ISAHP, 8-10.
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[31] Das, S., & Chakraborty, S. (2011). Selection of non-traditional machining processes using analytic network process. Journal of manufacturing systems, 30(1), 41-53.
31
[32] Milani, A. S., Shanian, A., Lynam, C., & Scarinci, T. (2013). An application of the analytic network process in multiple criteria material selection. Materials & design, 44, 622-632.
32
[33] A. Rahimi Ghazikalayeh; M. Amirafshari; H.M. Mkrchyan; M. Taji (2013). Application of Fuzzy Hybrid Analytic Network Process in Equipment Selection of Open-Pit Metal Mines. International journal of research in industrial engineering, 2(3), 35-46.
33
[34] Montazeri, A., & Jouzdani, J. (2018). Prioritization of the Advertising Activities of Tehran Stock Exchange Investment Companies based on Investors' Financial Literacy using Step-by-Step ANP Approach. Journal of applied research on industrial engineering.
34
[35] Shariatmadari Serkani, E. (2015). Using DEMATEL–ANP hybrid algorithm approach to select the most effective dimensions of CRM on innovation capabilities. Journal of applied research on industrial engineering, 2(2), 120-138.
35
[36] Pourkhandani, M., Iranban, S., Seyedi, S. (2014). QFD Application Using Combined ANP-DEMATEL Approach for Improving Service Quality: A Case Study of Dental Clinic. Journal of applied research on industrial engineering, 1(2), 112-129.
36
[37] Saaty, T. L., & Ozdemir, M. (2003). Negative priorities in the analytic hierarchy process. Mathematical and computer modelling, 37(9-10), 1063-1075.
37
[38] Saaty, T. L., & Özdemir, M. S. (2005). The encyclicon: A dictionary of decisions with dependence and feedback based on the analytic network process. RWS Publications, Pittsburgh.
38
[39] Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: Methods and applications. CRC press.
39
[40] Sevkli, M., Oztekin, A., Uysal, O., Torlak, G., Turkyilmaz, A., & Delen, D. (2012). Development of a fuzzy ANP based SWOT analysis for the airline industry in Turkey. Expert systems with applications, 39(1), 14-24.
40
[41] Yazgan, H. R., Boran, S., & Goztepe, K. (2009). An ERP software selection process with using artificial neural network based on analytic network process approach. Expert systems with applications, 36(5), 9214-9222.
41
[42] Bottero, M., Lami, I. M., & Lombardi, P. (2008). Analytic network process: la valutazione di scenari di trasformazione urbana e territoriale. Alinea Editrice.
42
[43] Malhotra, M. K., & Grover, V. (1998). An assessment of survey research in POM: from constructs to theory. Journal of operations management, 16(4), 407-425.
43
ORIGINAL_ARTICLE
Unsteady Magnetohydrodynamic Stagnation Point Flow of a Nanofluid over a Slendering Stretching Sheet Using Buongiorno’s Model
This paper aimed to model and analyze the unsteady hydromagnetic boundary layer stagnation point nanofluid flow over a non-linear stretching surface through porous medium with variable wall thickness. The effects of radiation, dissipation, and slip velocity are taken into account. The formulation of the problem is made through Buongiorno’s model, which involves the aspects of thermophoresis and Brownian motion. The set of governing non-linear Ordinary Differential Equations (ODE’s) are solved numerically by using boundary value problem default solver in MATLAB bvp4c package. The impact of different flow quantities on fluid velocity, temperature, and nanoparticle concentration are analyzed and examined through graphs. The physical parameters like friction factor coefficient , rates of heat transfer , and nanoparticle friction are derived and presented through tables. It is found that the wall thickness parameter depreciates the nanofluid velocity for and accelerates when . Also, the unsteadiness parameter shows a significant effect on the stagnation point flow.
https://www.riejournal.com/article_57841_579706952add4373aabf9f51b971dc0b.pdf
2018-04-01
84
105
10.22105/riej.2018.102367.1028
Magneticfield
Stagnation point
Unsteady nanofluid flow
Wall thickness
Partial slip
R.
V. M. S. S. Kiran Kumar
rsai.maths@gmail.com
1
Department of Mathematics, S.V. University, Tirupati-517502, A.P, India.
LEAD_AUTHOR
G.
Vindo Kumar
gvkphd@gmail.com
2
Department of Mathematics, S.V. University, Tirupati-517502, A.P, India.
AUTHOR
S. V. K.
Verma
svijayakumarvarma@yahoo.co.in
3
Department of Mathematics, S.V. University, Tirupati-517502, A.P, India.
AUTHOR
[1] Hiemenz, K. (1911). Die Grenzschicht an einem in den gleichformigen Flussigkeitsstrom eingetauchten geraden kreiszylinder. Gottingen dissertation. Dingler's polytech. J, 326, 311.
1
[2] Crane, L. J. (1970). Flow past a stretching plate. Zeitschrift für angewandte mathematik und physik zamp, 21(4), 645-647.
2
[3] Wang, C. Y. (1984). The three‐dimensional flow due to a stretching flat surface. The physics of fluids, 27(8), 1915-1917.
3
[4] Suali, M., Nik Long, N. M. A., & Ariffin, N. M. (2012). Unsteady stagnation point flow and heat transfer over a stretching/shrinking sheet with suction or injection. Journal of applied mathematics, 2012.
4
[5] Zhong, Y., & Fang, T. (2011). Unsteady stagnation-point flow over a plate moving along the direction of flow impingement. International journal of heat and mass transfer, 54(15-16), 3103-3108.
5
[6] Ishak, A., Jafar, K., Nazar, R., & Pop, I. (2009). MHD stagnation point flow towards a stretching sheet. Physica A: Statistical mechanics and its applications, 388(17), 3377-3383.
6
[7] Hayat, T., Qayyum, S., Alsaedi, A., & Waqas, M. (2016). Simultaneous influences of mixed convection and nonlinear thermal radiation in stagnation point flow of Oldroyd-B fluid towards an unsteady convectively heated stretched surface. Journal of molecular liquids, 224, 811-817.
7
[8] Hayat, T., Khan, M. I., Tamoor, M., Waqas, M., & Alsaedi, A. (2017). Numerical simulation of heat transfer in MHD stagnation point flow of Cross fluid model towards a stretched surface. Results in physics, 7, 1824-1827.
8
[9] Choi, S. U., & Eastman, J. A. (1995). Enhancing thermal conductivity of fluids with nanoparticles. Proceedings of the ASME international mechanical engineering congress and Exposition, 66. Argonne National Lab., IL (United States).
9
[10] Khanafer, K., Vafai, K., & Lightstone, M. (2003). Buoyancy-driven heat transfer enhancement in a two-dimensional enclosure utilizing nanofluids. International journal of heat and mass transfer, 46(19), 3639-3653.
10
[11] Buongiorno, J. (2006). Convective transport in nanofluids. Journal of heat transfer, 128(3), 240-250.
11
[12] Khalili, S., Dinarvand, S., Hosseini, R., Tamim, H., & Pop, I. (2014). Unsteady MHD flow and heat transfer near stagnation point over a stretching/shrinking sheet in porous medium filled with a nanofluid. Chinese physics B, 23(4), 048203.
12
[13] Hayat, T., Khan, M. I., Waqas, M., Alsaedi, A., & Farooq, M. (2017). Numerical simulation for melting heat transfer and radiation effects in stagnation point flow of carbon–water nanofluid. Computer methods in applied mechanics and engineering, 315, 1011-1024.
13
[14] Hady, F. M., Eid, M. R., & Ahmed, M. A. (2014). Slip effects on unsteady MHD stagnation point flow of a nanofluid over stretching sheet in a porous medium with thermal radiation. Journal of pure and applied mathematics: Advances and applications, 12(2), 181-206.
14
[15] Salem, A. M., Ismail, G., & Fathy, R. (2015). Unsteady MHD boundary layer stagnation point flow with heat and mass transfer in nanofluid in the presence of mass fluid suction and thermal radiation. The European physical journal plus, 130(6), 113.
15
[16] Das, K., Duari, P. R., & Kundu, P. K. (2014). Nanofluid flow over an unsteady stretching surface in presence of thermal radiation. Alexandria engineering journal, 53(3), 737-745.
16
[17] Haq, R. U., Nadeem, S., Khan, Z. H., & Akbar, N. S. (2015). Thermal radiation and slip effects on MHD stagnation point flow of nanofluid over a stretching sheet. Physica E: Low-dimensional systems and nanostructures, 65, 17-23.
17
[18] Akbar, N. S., Nadeem, S., Haq, R. U., & Khan, Z. H. (2013). Radiation effects on MHD stagnation point flow of nano fluid towards a stretching surface with convective boundary condition. Chinese journal of aeronautics, 26(6), 1389-1397.
18
[19] Nagendramma, V., Kumar, R. K., Prasad, P. D., Leelaratnam, A., & Varma, S. V. K. (2016). Multiple slips and radiation effects on Maxwell nanofluid flow over a permeable stretching surface with dissipation. Journal of nanofluids, 5(6), 817-825.
19
[20] Hayat, T., Qayyum, S., Waqas, M., & Alsaedi, A. (2016). Thermally radiative stagnation point flow of Maxwell nanofluid due to unsteady convectively heated stretched surface. Journal of molecular liquids, 224, 801-810.
20
[21] Farooq, M., Khan, M. I., Waqas, M., Hayat, T., Alsaedi, A., & Khan, M. I. (2016). MHD stagnation point flow of viscoelastic nanofluid with non-linear radiation effects. Journal of molecular liquids, 221, 1097-1103.
21
[22] Kumar, R. K., & Varma, S. V. K. (2017). Multiple Slips and Thermal Radiation Effects on MHD Boundary Layer Flow of a Nanofluid Through Porous Medium Over a Nonlinear Permeable Sheet with Heat Source and Chemical Reaction. Journal of nanofluids, 6(1), 48-58.
22
[23] Das, K. (2015). Nanofluid flow over a non-linear permeable stretching sheet with partial slip. Journal of the Egyptian mathematical society, 23(2), 451-456.
23
[24] Lee, L. L. (1967). Boundary layer over a thin needle. The physics of fluids, 10(4), 820-822.
24
[25] Fang, T., Zhang, J., & Zhong, Y. (2012). Boundary layer flow over a stretching sheet with variable thickness. Applied mathematics and computation, 218(13), 7241-7252.
25
[26] Acharya, N., Das, K., & Kundu, P. K. (2016). Ramification of variable thickness on MHD TiO2 and Ag nanofluid flow over a slendering stretching sheet using NDM. The European physical journal plus, 131(9), 303.
26
[27] Prasad, K. V., Vajravelu, K., Vaidya, H., & Van Gorder, R. A. (2017). MHD flow and heat transfer in a nanofluid over a slender elastic sheet with variable thickness. Results in physics, 7, 1462-1474.
27
[28] KiranKumar, R. V. M. S. S., & Varma, S. V. K. (2017). Hydromagnetic Boundary Layer Slip Flow of Nanofluid Through Porous Medium Over a Slendering Stretching Sheet. Journal of nanofluids, 6, 1-10.
28
[29] Hayat, T., Waqas, M., Alsaedi, A., Bashir, G., & Alzahrani, F. (2017). Magnetohydrodynamic (MHD) stretched flow of tangent hyperbolic nanoliquid with variable thickness. Journal of molecular liquids, 229, 178-184.
29
[30] Hayat, T., Zubair, M., Waqas, M., Alsaedi, A., & Ayub, M. (2017). Application of non-Fourier heat flux theory in thermally stratified flow of second grade liquid with variable properties. Chinese journal of physics, 55(2), 230-241.
30
[31] Khader, M. M., & Megahed, A. M. (2013). Numerical solution for boundary layer flow due to a nonlinearly stretching sheet with variable thickness and slip velocity. The European physical journal plus, 128(9), 100.
31
[32] Devi, S. A., & Prakash, M. (2016). Thermal radiation effects on hydromagnetic flow over a slendering stretching sheet. Journal of the brazilian society of mechanical sciences and engineering, 38(2), 423-431.
32
[33] Ascher, U. M., Mattheij, R. M., & Russell, R. D. (1994). Numerical solution of boundary value problems for ordinary differential equations (Vol. 13). Siam.
33
[34] Ibrahim, W., Shankar, B., & Nandeppanavar, M. M. (2013). MHD stagnation point flow and heat transfer due to nanofluid towards a stretching sheet. International journal of heat and mass transfer, 56(1-2), 1-9.
34
[35] Mahapatra, T. R., & Gupta, A. S. (2002). Heat transfer in stagnation-point flow towards a stretching sheet. Heat and mass transfer, 38(6), 517-521.
35
[36] Bhattacharyya, K. (2013). MHD stagnation-point flow of Casson fluid and heat transfer over a stretching sheet with thermal radiation. Journal of thermodynamics. http://dx.doi.org/10.1155/2013/169674
36
ORIGINAL_ARTICLE
[0,1] Truncated Fréchet-Weibull and Fréchet Distributions
In this paper, we introduce a new family of continuous distributions based on [0, 1] Truncated Fréchet distribution. [0, 1] Truncated Fréchet Weibull ([0, 1] ) and [0, 1] Truncated Fréchet ([0, 1] ) distributions are discussed as special cases. The cumulative distribution function, the rth moment, the mean, the variance, the skewness, the kurtosis, the mode, the median, the characteristic function, the reliability function and the hazard rate function are obtained for the distributions under consideration. It is well known that an item fails when a stress to which it is subjected exceeds the corresponding strength. In this sense, strength can be viewed as “resistance to failure.” Good design practice is such that the strength is always greater than the expected stress. The safety factor can be defined in terms of strength and stress as strength/stress. So, the [0, 1] strength-stress and the [0, 1] strength-stress models with different parameters will be derived here. The Shannon entropy and Relative entropy will be derived also.
https://www.riejournal.com/article_55714_0459bc5cae3ec76f7da643445319dbe2.pdf
2018-04-01
106
135
10.22105/riej.2018.100865.1020
1] TFW
[0
1] TFF
Stress-strength model
Shannon Entropy
Relative entropy
S.
Abid
abidsalah@gmail.com
1
Department of mathematics, Education College, University of Mustansiriyah, Baghdad, Iraq.
LEAD_AUTHOR
R.
Abdulrazak
dr.abid@rocketmail.com
2
Department of mathematics, Education College, University of Mustansiriyah, Baghdad, Iraq.
AUTHOR
[1] Abid, S. H., & Hassan, H. A. (2015). The Beta Marshall-Olkin Extended Uniform Distribution. Journal of safety engineering, 4(1), 1-7.
1
[2] Eugene, N., Lee, C., & Famoye, F. (2002). Beta-normal distribution and its applications. Communications in statistics-theory and methods, 31(4), 497-512.
2
[3] Gradshteyn, I. S., & Ryzhik, I. M. (2014). Table of integrals, series, and products. Academic press.
3
[4] Gupta, A. K., & Nadarajah, S. (2005). On the moments of the beta normal distribution. Communications in statistics-theory and methods, 33(1), 1-13.
4
[5] Jones, M. C. (2004). Families of distributions arising from distributions of order statistics. Test, 13(1), 1-43.
5
[6] Jamjoom, A. A., & Al-Saiary, Z. A. (2012). Computing the moments of order Statistics from independent nonidentically distributed exponentiated Frechet variables. Journal of probability and statistics, 2012.
6
[7] Maria do Carmo, S. L., Cordeiro, G. M., & Ortega, E. M. (2015). A new extension of the normal distribution. Journal of data science, 13(2), 385-408.
7