ORIGINAL_ARTICLE
Supplier selection among alternative scenarios by Data envelopment analysis
A considerable problem in competitive trade world is choosing the best supply chain. As a result in much more serious circumstances of competitions looking for the best supplier for manufacturing, for preparing raw material, is very significant. Meantime suppliers have different scenarios to be fulfilled, such as changing selection variables like lead-time, transportation cost and transportation path. In this paper a mathematical model using Data Envelopment Analysis (DEA) technique and binary algorithm for selecting suppliers with different scenarios is used which can evaluate suppliers with variable preferences and replacement for other suppliers.A considerable problem in competitive trade world is choosing the best supply chain. As a result in much more serious circumstances of competitions looking for the best supplier for manufacturing, for preparing raw material, is very significant. Meantime suppliers have different scenarios to be fulfilled, such as changing selection variables like lead-time, transportation cost and transportation path. In this paper a mathematical model using Data Envelopment Analysis (DEA) technique and binary algorithm for selecting suppliers with different scenarios is used which can evaluate suppliers with variable preferences and replacement for other suppliers.
http://ijo.iaurasht.ac.ir/article_523397_c0a462c308b137e727fa52576323558f.pdf
2015-12-01T11:23:20
2017-11-22T11:23:20
815
821
supply chain management
Data envelopment analysis
Mixed integer programming
Binary Variables
Mohsen
Vaez-Ghasemi
mohsen.vaez@iaurasht.ac.ir
true
1
Department of Mathematics, Islamic Azad University, Rasht Branch
Department of Mathematics, Islamic Azad University, Rasht Branch
Department of Mathematics, Islamic Azad University, Rasht Branch
LEAD_AUTHOR
[1]Charnes, A., W.W. Cooper, E. Rhodes, Measuring the efficiency of decision-making units, European Journal of Operational Research, 2, 1978, 429–444.
1
[2]FarzipoorSaen, R., Restricting weights in supplier selection decisions in the presence of dual-role factors, Applied Mathematical Modeling, 34, 2010, 2820–2830.
2
[3]FarzipoorSaen, R., Suppliers selection in the presence of both cardinal and ordinal data, European Journal of Operational Research, 183(2), 2007, 741–747.
3
[4]FarzipoorSaen, R., Supplier selection by the new AR-IDEA model, International Journal of Advance Manufacturing and Technology, 39(11–12), 2008, 1061–1070.
4
[5]FarzipoorSaen, R., M. Zohrehbandian, A data envelopment analysis approach for supplier selection in volume discount environments, International Journal of Procurement Management, 1(4), 2008, 472–488.
5
[6]FarzipoorSaen, R., Using data envelopment analysis for ranking suppliers in the presence of nondiscretionary factors, International Journal of Procurement Management, 2(3), 2009, 229–243.
6
[7]Gunasekarana, A., C. Patel, Ronald E. McGaughey, A framework for supply chain performance measurement, International Journal of Production Economics, 87, 2004, 333–347.
7
[8]Ho, W., X.Xu, P. K. Dey , Multi-criteria decision making approaches for supplier evaluation and selection: A literature review, European Journal of Operational Research, 202, 2010, 16–24.
8
[9]Kuosmanen, T., R. KazemiMatin, Theory of integer-valued data envelopment analysis, European Journal of Operational Research, 192, 2009, 658–667.
9
[10]Liu, J., F.Y. Ding, V. Lall, Using data envelopment analysis to compare suppliers for supplier selection and performance improvement, Supply Chain Management: an International Journal, 5(3), 2000, 143–150.
10
[11]Shin, H., D.A. Collier, D.D. Wilson, Supply management orientation and supplier/buyer performance, Journal of Operations Management, 18(3), 2000, 317–333.
11
[12]Talluri, S., R. Narasimhan, A. Nair, Vendor performance with supply risk: a chance-constrained DEA approach, International Journal of Production Economics, 100(2), 2006, 212–222.
12
[13]Thomas, D.J., Griffin, P.M., Coordinated supply chain management, European Journal of Operational Research, 94(3), 1996, 1–15.
13
[14]Toloo, M., S. Nalchigar, A new integrated DEA model for finding most BCC-efficient DMU, Applied Mathematical Modelling, 33, 2009, 597–604.
14
[15]Farhad Hosseinzadeh Lotfi, Golamreza Jahanshahloo, Mohsen Vaez-Ghasemi, and Zohreh Moghaddas, “Modified Malmquist Productivity Index Based on Present Time Value of Money,” Journal of Applied Mathematics, vol. 2013, Article ID 607190, 8 pages, 2013. doi:10.1155/2013/607190
15
[16]Weber, C.A., A data envelopment analysis approach to measuring vendor performance, Supply Chain Management, 1(1), 1996, 28–39.
16
[17]Zolghadri, M.,C.Eckert, S.Zouggar, P. Girard, Power-based supplier selection in product development projects, Computers in Industry, 62, 2011, 487–500.
17
ORIGINAL_ARTICLE
A numerical approach for optimal control model of the convex semi-infinite programming
In this paper, convex semi-infinite programming is converted to an optimal control model of neural networks and the optimal control model is solved by iterative dynamic programming method. In final, numerical examples are provided for illustration of the purposed method.
http://ijo.iaurasht.ac.ir/article_519141_4f0562b3637b4466757d2112b1645445.pdf
2015-12-01T11:23:20
2017-11-22T11:23:20
823
829
optimal control
Iterative dynamic programming
Neural networks
Convex semi-infinite programming
Hamid
Rouhparvar
rouhparvar59@gmail.com
true
1
Department of Mathematics, Saveh Branch, Islamic Azad University, Saveh, Iran.
Department of Mathematics, Saveh Branch, Islamic Azad University, Saveh, Iran.
Department of Mathematics, Saveh Branch, Islamic Azad University, Saveh, Iran.
LEAD_AUTHOR
[1]Ben Tal A. and Kerzner L. and Zlobec S., Optimality conditions for convex semi-infinite programming problems, Naval Research Logistics, Vol. 27, N. 3, pp. 413-435, (1980).
1
[2]Hettich R. and Kortanek K.O., Semi-infinite programming: theory, methods and applications, SIAM Review, Vol. 35, pp.380-429, (1993).
2
[3]Hettich R. and Still G., Second order optimality conditions for generalized semi-infinite programming problems, Optimization, Vol. 34, pp. 195-211, (1995).
3
[4]Kennedy M.P. and Chua L.O., Unifying the Tank and Hopfield linear programming circuit and the Canonical nonlinear programming circuit of Chua and Lin"}, IEEE Trans. Circuits Systems, CAS-34, pp. 210-214, (1987).
4
[5]Kortanek K.O. and Medvedev V.G., Semi-infinite programming and new applications in Finance, Encyclopedia of Optimization, C Floudas and Panos Pardalos (Eds.), Kluwer Academic, accepted (2005).
5
[6]Kostyukova O.I. and Tchemisova T.V., Convex semi-infinite programming: explicit optimality conditions, Tech. Rep., Preprint, (2005).
6
[7]Kostyukova O.I. and Tchemisova T.V. and Yermalinskaya S.A., Convex semi-infinite programming: implicit optimality criterion based on the concept of immobile points, (to appears).
7
[8]Lin J.S and Hwang C., Enhancement of the global convergence of using iterative dynamic programming to solve optimal control problems"}, Ind. Eng. Chem. Res., Vol. 37, pp. 2469-2478, (1998).
8
[9]Luenberger D.G., Linear and nonlinear programming, 2nd edn., Addison-Wesley, Reading, MA, (1984).
9
[10]Luhandjula M.K. and Ouanes M., A cutting-plane approach for semi-infinite mathematical programming, African Journal of Science and Technology (AJST), Science and Engineering Series, Vol. 2, No 1, pp. 1-10, (2001).
10
[11]Luus R., Iterative dynamic programming, London, UK: Chapman and Hall/CRC, (2000).
11
[12]Luus R., Effect of the choice of final time in optimal control of nonlinear systems, Can. J. Chem. Eng. Vol. 69, pp. 144-151, (1991).
12
[13]Luus R., Numerical convergence properties of iterative dynamic programming when applied to high dimensional systems, Chem. Eng. Res. Des. Vol. 74, pp. 55-62, (1996).
13
[14]Luus, R., Optimal control by dynamic programming using accessible grid points and region reduction, Hung. J. Ind. Chem. Vol. 17, pp. 523-543, (1989).
14
[15]Luus, R., Optimal control by dynamic programming using systematic reduction in grid size, Int. J. Control Vol. 19, pp. 995-1013, (1990).
15
[16]Maa C.-Y. and Shanblatt M.A., Linear and quadratic programming neural network analysis, IEEE Trans. Neural Networks, Vol. 3, pp. 580-594, (1992).
16
[17]Polak E., Semi-infinite optimization in engereeng desigh, in semi-infinite programming and applications, int. Symp., Austin/Tex.1981, Lect. Notes Econ. Math. Syst., N 215, pp. 236-248, (1983).
17
[18]Song Q. and Leland R.P., An optimal control model of neural networks for constrained optimization problems, Optim. Control Appl. Meth., Vol. 19, pp. 371-376, (1998).
18
[19]Weber G.-W., Generalized semi-infinite optimization: theory and applications in optimal control and discrete optimization, in special issue' Optimality conditions, general convexity and duality in vector optimization', A.Cambini, L.Martein (eds), J. Statistic and Management Systems, N. 5, p.359-388, (2002).
19
ORIGINAL_ARTICLE
Alternative ranking method in Dynamic Data Envelopment Analysis (DDEA)
The motivation of this paper is to propose such equitable method for ranking all decision making units (DMUs) in dynamic Data Envelopment Analysis (DDEA) framework. As far as we are aware there is not more studies in dynamic DEA literature. What's more, in such cases the best operating unit is important to be sampled for the others in under evaluated time periods. However, in this special concept of DEA, quasi-fixed inputs or intermediate products are the source of inter temporal dependence between consecutive periods. Hence, in order to have suitable ranking for units operating in dynamic environment the minimum and maximum efficiency values of each DMU in dynamic state are computed. Also, we assume that the sum of efficiency values of all DMUs in dynamic state is equal to unity. Thereafter, the rank of each DMU is determined through the combination of its maximum and minimum efficiency values. A real case of Iranian gas companies highlights the applicability of the proposed method in Dynamic framework.
http://ijo.iaurasht.ac.ir/article_515896_c7257e3b2106d2c9a7310e0a7c4b23ab.pdf
2015-12-01T11:23:20
2017-11-22T11:23:20
839
848
Keywords: efficiency
dynamic DEA (DDEA)
ranking
DMU
DEA
Mahnaz
Maghbouli
mmaghbouli@gmail.com
true
1
Islamic Azad university-East Azerbaijan Branch
Islamic Azad university-East Azerbaijan Branch
Islamic Azad university-East Azerbaijan Branch
LEAD_AUTHOR
Omid
Yaghbubi agreh
yaghubi_omid@yahoo.com
true
2
Department of Mathematics, Azerbaijan shahid Madani University, Tabriz, Iran
Department of Mathematics, Azerbaijan shahid Madani University, Tabriz, Iran
Department of Mathematics, Azerbaijan shahid Madani University, Tabriz, Iran
AUTHOR
Zahra
Mohammadnezhad
mohammadnejhad_z@yahoo.com
true
3
Department of Mathematics, Azerbaijan shahid Madani University, Tabriz, Iran
Department of Mathematics, Azerbaijan shahid Madani University, Tabriz, Iran
Department of Mathematics, Azerbaijan shahid Madani University, Tabriz, Iran
AUTHOR
ORIGINAL_ARTICLE
A hybrid BSC-DEMATEL- FIS approach for performance measurement in Food Industry
Organizational performance is a complex issue given that performance is a multifaceted phenomenon whose components may have distinct managerial priorities and may even be mutually inconsistent. Recently, the balanced scorecard approach (BSC), as an effective multi-criteria evaluation concept received much attention in organizational performance measurement. Although the BSC conceptual framework has been widely accepted in the business community, the proper method of implementing the framework remains an issue. Hence, this study has developed a hybrid expert system composition of BSC, Decision Making Trial and Evaluation Laboratory (DEMATEL) and fuzzy inference system (FIS) to evaluate the food producing companies. To this aim, this paper applied a graph theory based technique (DEMATEL) to determine critical criteria of BSC’s perspectives. The results show that “Profit”, “Customer satisfaction”, “Customer communication”, “Innovation management” and “Organizational asset management”, are of more importance in performance evaluation. In the next step, these criteria shape a fuzzy rule based inference system with linguistic variables and the membership functions are adjusted by experts. The results of proposed approach in food industry of Guilan province, Iran, show its applicability for evaluating the food companies.
http://ijo.iaurasht.ac.ir/article_519142_0f38bb909f6b465ba3158820ac715412.pdf
2015-12-01T11:23:20
2017-11-22T11:23:20
839
848
Balanced Scorecard (BSC)
DEMATEL
Fuzzy Inference System (FIS)
Food Industry
Performance evaluation
shabnam
Jalalat
shabnamjalalat@yahoo.com
true
1
Department of Business Management, Rasht Branch, Islamic Azad University, Rasht, Iran.
Department of Business Management, Rasht Branch, Islamic Azad University, Rasht, Iran.
Department of Business Management, Rasht Branch, Islamic Azad University, Rasht, Iran.
LEAD_AUTHOR
Mehdi
Fadaei
fadaei@iaurasht.ac.ir
true
2
Department of Management, Rasht Branch, Islamic Azad University, Rasht, Iran
Department of Management, Rasht Branch, Islamic Azad University, Rasht, Iran
Department of Management, Rasht Branch, Islamic Azad University, Rasht, Iran
AUTHOR
Mahdi
Homayounfar
homayounfar@iaurasht.ac.ir
true
3
Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
AUTHOR
Bentes, A.V. Carneiro, J. da Silva, J.F. Kimura, H. 2012. Multidimensional assessment of organizational performance: Integrating BSC and AHP. Journal of Business Research, 65, 1790–1799.
1
Chang, B. Chang, C.W. Wu, C.H. 2011. Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 38, 1850-1858.
2
Dessler, G. 2000. Human resource management. New Jersey: Prentice-Hall, 8th edition.
3
Gabus, A. Fontela, E. 1972. World problems and invitation to further thought within the framework of DEMATEL, Switzerland Geneva: Battelle Geneva Research Centre.
4
Green, G.I., Keim, R.T. 1983. After implementation what’s next? Evaluation. Journal of System Management, 34 (9), 10-15
5
Huang, C.Y. Shyu, J.Z. Tzeng, G.H. 2007. Reconfiguring the innovation policy portfolios for Taiwan’s SIP mall industry. Technovation, 29, 744-765.
6
Kaplan, R.S., Norton, D.P. 1992. The balance scorecard – measures that drive performance. Harvard Business Review, 70, 71–79.
7
Li, C.W. Tzeng, G.H. 2009. Identification of a threshold value for the DEMATEL method using the maximum mean de-entropy algorithm to find critical services provided by a semiconductor intellectual property mall. Expert Systems with Applications, 36, 9891-9898.
8
Lin, C.J. Wu, W.W. 2008. A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34, 205-213.
9
Liou, J.J.H. Tzeng, G.H. 2007. A non-additive model for evaluating airline service quality. Journal of Air Transport Management, 13, 131-138.
10
Liou, J.J.H. Chuang, Y.T. 2010. Developing a hybrid multi-criteria model for selection of outsourcing providers. Expert Systems with Applications, 37, 3755-3761.
11
Lueg, Rainer. 2015. Strategy maps: the essential link between the balanced scorecard and action. Journal of Business Strategy, 36 , 34 – 40.
12
Rahimnia, Fariborz , Samira, Keyvanipoor , Moghadasian, Mahdi. 2014. Analysis of BSC perspectives as related to the alignment of environmental uncertainty and supply chain strategy. Benchmarking: An International Journal, 21 , 903 – 916.
13
Sankar, N.R. Prabhu, B.S. 2001. Modified approach for prioritization of failures in a system failure mode and effects analysis. International Journal ofQualityandReliability Management, 18, 324-335.
14
Sivanandam, S.N. Sumathi, S. Deepa, S.N. 2007. Introduction to Fuzzy Logic Using Matlab. Springer, New York, USA.
15
Solatian, Payam, Abbasi, Seyed Hamidreza, Shabaninia, Fereidoon. 2012. Simulation Study of Flow Control Based on PID ANFIS Controller for Non-Linear Process Plants . American Journal of Intelligent System, 2 (5), 104-110.
16
Toloie-Eshlaghi, A., Homayonfar, M. 2011. MCDM Methodologies and Applications: A Literature Review from 1999 to 2009. Research Journal of International Studies, 21, 86-127.
17
Tsai, W.H. Chou, W.C. 2009. Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP and ZOGP. Expert Systems with Applications, 36, 1444-1458.
18
Tzeng, G.H. Chiang, C.H. Li, C.W. 2006. Evaluating intertwined effects in elearning programs: a novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32, 1028-1044.
19
Vaez-Ghasemi, M., F Hosseinzadeh Lotfi, L Taghizadeh, Multi-component balanced scorecard for perspectives efficiency measurement, Applied Mathematical Sciences, Vol. 6, 2012, no. 9, 403 - 410
20
Wang, Y.L. Tzeng, G.H. 2012. Brand marketing for creating brand value based on a MCDM model combining DEMATEL with ANP and VIKOR methods. Expert Systems with Applications, 39, 5600–5615.
21
Wu, W.W. 2008. Choosing knowledge management strategies by using a combined ANP and DEMATEL approach. Expert Systems with Applications, 35, 828–835.
22
Wu, W.W. 2012. Segmenting critical factors for successful knowledge management implementation using the fuzzy DEMATEL method. Applied Soft Computing, 12, 527-535.
23
Wu, W.W. Lee, Y.T. 2007. Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32, 499-507.
24
Wu, H.Y., Tzeng, G.H., & Chen, Y.H. 2009. A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard. Expert Systems with Applications, 36, 10135–10147.
25
Wu, H.Y. Lin, Y.K. Chang, C.H. 2011. Performance evaluation of extension education centers in universities based on the balanced scorecard. Evaluation and Program Planning, 34, 37–50.
26
Yang, M. Khan, F.I. Sadiq, R. 2011. Prioritization of environmental issues in offshore oil and gas operations: A hybrid approach using fuzzy inference system and fuzzy analytic hierarchy process, Process Safety and Environmental Protection, 89, 22–34.
27
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28
ORIGINAL_ARTICLE
A new approach to fuzzy quantities ordering based on distance method and its applications for solving fuzzy linear programming
Many ranking methods have been proposed so far. However, there is yet no method that can always give a satisfactory solution to every situation; some are counterintuitive, not discriminating; some use only the local information of fuzzy values; some produce different ranking for the same situation. For overcoming the above problems, we propose a new method for ranking fuzzy quantities based on the distance method. Then, an application of using fuzzy ordering in the fuzzy mathematical programming as well as fuzzy primal simplex algorithm is indicated. In particular, we emphasize that the fuzzy ordering will be useful when a decision maker needs to evaluate the optimality condition in any solving process.
http://ijo.iaurasht.ac.ir/article_522574_c3dd2d1913e459a1396bfb421a87e3ee.pdf
2015-12-01T11:23:20
2017-11-22T11:23:20
849
855
Fuzzy linear programming
Fuzzy number
fuzzy ordering
simplex algorithm
S.H
Nasseri
nasseri@umz.ac.ir
true
1
Department of Mathematics, University of Mazandaran, Babolsar, Iran
Department of Mathematics, University of Mazandaran, Babolsar, Iran
Department of Mathematics, University of Mazandaran, Babolsar, Iran
LEAD_AUTHOR
E.
Darban Jafari
true
2
Department of Mathematics, University of Mazandaran, Babolsar, Iran.
Department of Mathematics, University of Mazandaran, Babolsar, Iran.
Department of Mathematics, University of Mazandaran, Babolsar, Iran.
AUTHOR
R.
Chameh
true
3
Department of Mathematics, University of Mazandaran, Babolsar, Iran.
Department of Mathematics, University of Mazandaran, Babolsar, Iran.
Department of Mathematics, University of Mazandaran, Babolsar, Iran.
AUTHOR
ORIGINAL_ARTICLE
Sensitivity Analysis in Two-Stage DEA
Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs) which uses a set of inputs to produce a set of outputs. In some cases, DMUs have a two-stage structure, in which the first stage utilizes inputs to produce outputs used as the inputs of the second stage to produce final outputs. One important issue in two-stage DEA is the sensitivity of the results of an analysis to perturbations in the data. The current paper looks into combined model for two-stage DEA and applies the sensitivity analysis to DMUs on the entire frontier. In fact, necessary and sufficient conditions for preserving a DMU's efficiency classiffication are developed when various data changes are applied to all DMUs.
http://ijo.iaurasht.ac.ir/article_523396_208dd85e0b9da782dea305895cf651c7.pdf
2015-12-01T11:23:20
2017-11-22T11:23:20
857
864
Two-stage DEA
Weighted sum model
Combined DEA model
Sensitivity Analysis
Athena
Forghani
athenaforghani@yahoo.com
true
1
Islamic Azad University, Science and Research Branch
Islamic Azad University, Science and Research Branch
Islamic Azad University, Science and Research Branch
LEAD_AUTHOR
Esmaeil
Najafi
najafi1515@yahoo.com
true
2
Islamic Azad University, Science and Research Branch
Islamic Azad University, Science and Research Branch
Islamic Azad University, Science and Research Branch
AUTHOR