ORIGINAL_ARTICLE
A simulation approach for assembly line improvement of Iran Heavy Diesel Company
[Naeini1] The current research aims to investigate improvement possibility of Iran Heavy Diesel (DESA) Company Assembly Line by Simulation. Therefore, in addition to reviewing the existing thematic literature in relation to the system, model, simulation, assembly lines and the improvement solutions, as well as studying similar research background and history, the primary model of company assembly line was created by using data collection instruments like documents reviewing and observation. After presentation of created model and a summery of descriptive data, the model was validated by averages test. Data were analyzed by ARENA and SPSS using software. In the next stage, the improvable point of assembly line, namely Test room was identified by interviewing with 48 people related with assembly line. Then, after describing the reason of attention to this point, the use of new technology was proposed for achieving improvement in this section. The results of testing this proposition by simulation model showed that if such system is implemented in assembly line, the cycle time will improve by 33% and the queue time in test station will reduce by 62%. Finally, based on these results, the discussion and conclusion were represented, and some suggestions were given for managers and directions for further researches were provided in conclusion.
https://imj.ut.ac.ir/article_50705_a15322d0a619ec9d5113d6d77110757c.pdf
2014-12-22
635
664
10.22059/imj.2014.50705
assembly line
Iran Heavy Diesel
simulation
Hassanali
Aghajani
aghajani@umz.ac.ir
1
Associate Prof., Industrial Management, University of Mazandaran, Babolsar, Iran
LEAD_AUTHOR
Hamzeh
Samadi
hamzeh_samadi@yahoo.com
2
PhD Candidate, Department of Public Administration, Islamic Azad University, Science and Reserch Branch, Tehran, Iran
AUTHOR
Hossein
Samadi
hossein_samadi_m@yahoo.com
3
MSc. Public Administration, Young Researcher Club, Qaemshahr, Iran
AUTHOR
Hossein
Lotfi
hossein_samadi@yahoo.com
4
MSc. of Industrial Management, University of Mazandaran, Babolsar, Iran
AUTHOR
Azadeh, A., Hatefi, S. M., & Kor, H. (2012). Performance improvement of a multi product assembly shop by integrated fuzzy simulation approach. Journal of Intelligent Manufacturing, 23(5), 1861-1883. doi: 10.1007/s10845-011-0501-0
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Shanon, R. (1992). The science and art of systems’ simulation. Translated by: Arab, A. A. Tehran: University Publication Center. (in persian)
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Shokouhi, A. & Shahriari, H. (2010). The problem of control duration of steady multi-object manufacturing in complex assembly systems. International Journal of Industrial Engineering and Production Management, 2(21), 23-35. (in persian)
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Sheldon, A. R. (1995). Simulation. Translated by: Azarnoush, H. & Niroumand, H. Mashhad Ferdousi University (in persian)
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35
ORIGINAL_ARTICLE
An analysis of housing market in Tehran Province using system dynamics
[Naeini1] Housing is the third top factor in human life, after food and clothing in all communities. Many variables are involved in determining housing prices and it is impossible to ignore housing feedback. One of the powerful tools to determine the impact of policies in this field is using system dynamics approach. According to this approach, the cause-and- effect diagram of factors affecting housing prices in Tehran is presented for dynamic model. To simulate the intended model Vensim software was used. Finally, scenario making was conducted to consider various policies in housing market. The results show that enhanceing other provinces' facilities, increasing the supply of affordable housing by the government as well as loaning for construction decrease the housing price thereby the price would be balanced.
https://imj.ut.ac.ir/article_52449_6c43fa5d0da6d4b7eaadc933ca9a9cae.pdf
2014-12-22
665
683
10.22059/imj.2014.52449
housing market
housing supply and demand
System Dynamics
Ali Mohamad
Ahmadvand
alimohamad.ahmadvand@gmail.com
1
Prof., Industrial Engineering Department, Faculty of Engineering, Eyvanekey Institute of Higher Education, Semnan, Iran
AUTHOR
Hadiseh
Khodadadi Abyazani
hadisekhodadadi@gmail.com
2
MSc. in Industrial Engineering, Faculty of Engineering, Eyvanekey Institute of Higher Education, Semnan, Iran
LEAD_AUTHOR
zeinab
Mohammadiani
zeinabmohammadiani@yahoo.com
3
MSc. in Industrial Engineering, Faculty of Engineering, Eyvanekey Institute of Higher Education, Semnan, Iran
AUTHOR
Atefi, Y., Minooei, F. & Dargahi, R. (2010). Housing affordability: A study of real estate market in Iran. In Proceedings of the 28th International Conference of System Dynamics Society. (In Persian)
1
Che, J. (2005). Modeling Shanghai Real Estate Market: Dynamic Insight into the Sustaining House Price Growth. It’s online at: http:∥www. systemdynamics.org.
2
Daneshpour, S. A & Hosseini, S. (2012). Place of physical factors in the reduction of housing prices, Journal of Specialized of Architecture & Urbanism, 5(9): 61-71. (In Persian)
3
Economic Statistics Department of the Central Bank of the Islamic Republic of Iran, It’s online at: http://www.cbi.ir
4
Eichholtz, P. & Lindenthal, T. (2014). Demographics, human capital, and the demand for housing. Journal of Housing Economics, 26: 19-32.
5
Forrester, J. W. (1969). Urban Dynamics, The MIT Press, Cambridge. pp. 14-15.
6
GhafelehBashi, H. (2009). System dynamics model of housing market. Thesis. University of Science and Technology. Tehran. (In Persian)
7
Ho, Y. F., Wang, H. L. & Liu, C. C. (2010). Dynamics model of housing market surveillance system for taichung city. In Proceeding of the 28th International Conference of the System Dynamics Society, Korean System Dynamics Society, Seoul, Korea, ISBN. pp. 978-1.
8
Hu, Y. (2003). Study of system dynamics for urban housing development in Hong Kong. Dissertation. The Hong Kong Polytechnic University. Hong Kong.
9
Inanloo, A. (2002). Planning and analysis of housing supply and demand in north of Qazvin. Thesis. Tarbiat Modarres University. Tehran. (In Persian)
10
Keynes, J. M. (1937). The general theory of employment. The Quarterly Journal of Economics, 51(2): 209-223.
11
Ministry of Works and Urban Development, the Islamic Republic of Iran, It’s online at: http://www.mrud.ir
12
Nazari, M. (2010). Monetary policy and the housing bubble in Tehran, Journal of Economic Research, 91: 229-241. (In Persian)
13
Paciorek, A. (2013). Supply constraints and housing market dynamics. Journal of Urban Economics, 77: 11-26.
14
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17
Sterman, J. D. (2000). Business dynamics: systems thinking and modeling for a complex world (Vol. 19). Irwin/McGraw-Hill. Boston.
18
Taheri, S. (2010). Housing market analysis in Iran by using system dynamics model. Thesis. Tehran University. Tehran. (In Persian)
19
ORIGINAL_ARTICLE
Drawing the cause-and-effect relations during the time in a dynamic strategy map
[Naeini1] One of the biggest organizational challenges in deployment of strategies is the time lag between the cause-and-effect relations according to a lag between indicators of strategic objectives. With adding the time factor, the strategic objectives at different times have a causal relationship with each other. The causal relationships between strategic objectives are essential. In this paper, after dividing strategic objectives into four aspects of balanced scorecard in the National Iranian Oil Refining & Distribution Company, the fuzzy DEMATEL method is used to draw the cause-and-effect relation between strategic objectives in the strategic map considering the time delay from the lag key performance indices. By drawing the dynamic strategy map, the network relation between strategic objectives during the time identified and by defining strategic initiatives for the strategic objectives of the lag key performance indices, improvement for the strategic objectives of the lead key performance indices will appear.
https://imj.ut.ac.ir/article_51986_90a46fe1c2256818389a13250c91bf39.pdf
2014-12-22
685
707
10.22059/imj.2014.51986
balanced scorecard
dynamic strategy map
Fuzzy DEMATEL
lead & lag key performance indicators
Mojtaba
Akbarian
mojtabaakbaryan@gmail.com
1
PhD Candidate, Industrial Engineering, Islamic Azad University, Science and Research branch, Tehran, Iran
LEAD_AUTHOR
Esmaeil
Najafi
najafi1515@yahoo.com
2
Assistant Prof., Department of Industrial Engineering, Islamic Azad University, Science and Research branch, Tehran, Iran
AUTHOR
Farhad
Hosseinzadeh Lotfi
hosseinzadeh.lotfi@iauctb.ac.ir
3
Prof., Department of Mathematics, Islamic Azad University, Science and Research branch, Tehran, Iran
AUTHOR
Reza
Tavakkoli-Moghaddam
tavakoli@ut.ac.ir
4
Prof., Department of Industrial Engineering, University of Tehran, Tehran, Iran
AUTHOR
Alvandi, M., Fazli, S., Yazdania, L. & Aghaee, M. (2012). An integrated MCDM method in ranking BSC perspectives and key performance indicators (KPIs). Management science, 2(3): 994-1004. (In Persian)
1
Amiri, M., Salehi Sadaghiyani, J., Payani. N. & Shafieezadeh, M. (2011). Developing a DEMATEL method to prioritize distribution centers in supply chain. Management Science, 1(3): 279-288. (In Persian)
2
Chang, B. & Chang, C. & Wu, C. (2011). Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 38(3): 1850-1858.
3
Chen, F., Hsu, T. & Tzeng, G. (2011). A balanced scorecard approach to establish a performance evaluation and relationship model for hot spring hotels based on a hybrid MCDM model combining DEMATEL and ANP. International Journal of Hospitality Management, 30)4(: 908-932.
4
Creelman, J. & Makhijani, N. (2008). How leading organizations successfully implement corporate strategy with the balanced scorecard. The OTI Thought Leadership Series.
5
Dehghan Mashtani, M., Kamfiroozi, M. H. & Bonyadi Naeini, A. (2012). Development of using balanced scorecard in universities to improve performance: A fuzzy DEMATEL-shapley value goal programming approach. International journal of information, Security and Systems Management, 1(2): 86-94. (In Persian)
6
Falatoonitoosi, E., Leman, Z. & Sorooshian, S. (2012). Casual strategy mapping using integrated BSC and MCDM DEMATEL. Journal of American Science, 8(1): 125-155. (In Persian)
7
Fontela, E. & Gabuse, A. (1976). The DEMATEL observer: Battelle Institute, Geneva Research Center.
8
Hematian, M., Danaeia, A. & Shahhosseinib, M. (2012). An empirical study to measure the relative efficiency and strategic planning using BSC-DEA and DEMATEL. Management science letters, 2(4): 1109-1122. (In Persian)
9
Heydariyeha, S. A., Javidniab, M. & Mehdiabadib, A. (2012). A new approach to analyze strategy map using an integrated BSC and FUZZY DEMATEL. Management Science Letters, 2(1): 161-170. (In Persian)
10
Hung, W. (2012). Constructing a strategy map for banking institutions with key performance indicators of the balanced scorecard. Evaluation and Program Planning, 35(3): 303–320.
11
Jassbi, J., Mohamadnejad, F. & Nasrollahzadeh, H. (2011). A fuzzy DEMATEL framework for modeling cause and effect relationships of strategy. Expert Systems with Applications, 38(5): 5967–5973. (In Persian)
12
Kaplan, R. S. & Norton, D. P. (1992) The balanced scorecard measures that drive performance. Harvard Business Review, 70(1): 71–80.
13
Kaplan, R. & Norton, D. (2004). Strategy maps: Converting intangible assets into tangible outcomes, Harvard Business School Press.
14
Kaplan, R. S. & Norton, D. P. (2001). The strategy focused organization: how balanced scorecard companies Thrive in New Business Environment. Harvard Business School Press. Boston. MA
15
Kaplan, R. S. & Norton, D. P. (1996b). Using the balanced scorecard as a strategic management system. Harvard Business Review, 74(2): 70-82.
16
Lee, W. S., Huang, A. & Cheng, C. M. (2011). Analysis of decision making factors for equity investment by DEMATEL and analytic network process. Expert System with Application, 38(7): 8375–8383.
17
Liou, J., Tzeng, G. H. & Chang, H. (2007). Airline safety measurement using a hybrid model. Journal of Air Transport Management, 13(1): 243-249.
18
Najeeb Shaik, M. & Abdul-Kader, W. (2014). Comprehensive performance measurement and causal-effect decision making model for reverse logistics enterprise. Computers & Industrial Engineering, 68(1): 87-103.
19
Nissen, V. (2006). Modeling corporate strategy with the fuzzy balanced scorecard. Proceedings symposium on fuzzy systems in computer science FSCS, Magdeburg: 121-138.
20
Niven, P. R. (2006). Balanced scorecard step-by-step: Maximizing performance and maintaining results. 2nd Edition. John Wiley &Sons.
21
Ren, J., Manzardo, A., Toniolo, S. & Scipioni, A. (2013). Sustainability of hydrogen supply chain. Part I: Identification of critical criteria and cause–effect analysis for enhancing the sustainability using DEMATEL. International journal of hydrogen energy, 38(33): 14159-14171.
22
Sachin, K. & Ravi Kant, P. (2014). A hybrid approach based on fuzzy DEMATEL and FMCDM to predict success of knowledge management adoption in supply chain. Applied soft computing, 18(1): 126-135.
23
Seyedhosseini, S. M., Taleghani, E., Bakhsha, A. & Partovi, S. (2011). Extracting leanness criteria by employing the concept of Balanced Scorecard. Expert systems with applications, 38(1): 5967–5973. (In Persian)
24
Shyan Horng, J., Hsing Liu, C., Fang Chou, S. & Yen Tsai, C. (2013). Creativity as a critical criterion for future restaurant space design: Developing a novel model with DEMATEL application. International Journal of Hospitality Management, 33(1): 96-105.
25
Trevithick, S., Flabouris, A., Tall, G.V. & Webber, C. (2003). International EMS systems. New south wales. Australia.
26
Tseng, M. (2010). Implementation and performance evaluation using the fuzzy network balanced scorecard. Computers & Education, 55(1):188-201.
27
Wei, W. & Wu. (2008). Choosing knowledge management strategies by using a combined ANP and DEMATEL approach. Expert Systems with Applications, 35: 828–835.
28
Wu, W. W. & Lee, Y. T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2): 499-507.
29
ORIGINAL_ARTICLE
A method for solving possibilistic multi-objective linear programming problems with fuzzy decision variables
[Naeini1] In this paper, a new method is proposed to find the fuzzy optimal solution of fuzzy multi-objective linear programming problems (FMOLPp) with fuzzy right hand side and fuzzy decision variables. Due to the imprecise nature of available resources, determination of a definitive solution to the model seems impossible. Therefore, the proposed model is designed in order to make fuzzy decisions. The model resolves the deficiencies of the previous models presented in this field and its main advantage is simplicity. To illustrate the efficiency of the proposed method, it is applied to the problem of allocating orders to suppliers. Due to the nature of the fuzzy solutions obtained from solving the model, the decision maker will be faced with more flexibility in decision making.
https://imj.ut.ac.ir/article_51985_4a0bbade4618f93e959e5cf7a592b72b.pdf
2014-12-22
709
724
10.22059/imj.2014.51985
Allocation
fuzzy decision variable
fuzzy ranking
multi-objective linear programming
possibilistic linear programming
Triangular Fuzzy Numbers
Mahnaz
Hosseinzadeh
mhosseinzadeh@ut.ac.ir
1
PhD of Operations Research Management, University of Tehran, Tehran, Iran
AUTHOR
Mohammad Bagher
Menhaj
mbmenhaj@aut.ac.ir
2
Prof. in Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Aliyeh
Kazemi
aliyehkazemi@ut.ac.ir
3
Assistant Prof., Industrial Management, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Allahviranloo, T., Lotfi, F.H., Kiasary, M.K., Kiani, N.A. & Alizadeh, L., (2008). Solving full fuzzy linear programming problem by the ranking function. Applied Mathematical Science, 2, 19–32. (In Persian )
1
Buckley, J.J. & Feuring, T. (2000). Evolutionary algorithm solution to fuzzy problems: fuzzy linear programming. Fuzzy Sets and Systems, 109, 35–53.
2
Dehghan, M., Hashemi, B. & Ghatee, M. (2006). Computational methods for solving fully fuzzy linear systems. Applied Mathematics and Computations, 179: 328–343. (In Persian)
3
Farquhar, P. (1984). Utility assessment methods. Management Science, 30: 1283–1300.
4
Harker, P. & Vargas, L. (1987). The theory of ratio scale estimations: Saaty’s analytic hierarchy process. Management Science, 33(11): 1383–1403.
5
Hashemi, S.M., Modarres, M., Nasrabadi, E. & Nasrabadi, M. M. (2006). Fully fuzzified linear programming, solution and duality. Journal of Intelligent Fuzzy Systems, 17, 253–261. (In Persian)
6
Keeney, R. & Raiffa, H. (1976). Decision with Multiple Objectives: Preference and Value Trade-off, John Wiley. New York.
7
Kumar, A., Kaur, J. & Singh, P. (2011). A new method for solving fully fuzzy linear programming problems. Applied Mathematical Modeling. 35, 817–823.
8
Lai, Y. J. & Hwang, C. L. (1992). Fuzzy Mathematical Programming: methods and applications, Springer. Berlin.
9
Lotfi, F. H., Allahviranloo, T., Jondabeha, M. A. & Alizadeh, L. (2009). Solving a fully fuzzy linear programming using lexicography method and fuzzy approximate solution. Applied Mathematical Modeling, 33, 3151–3156. (In Persian)
10
Mahdavi Amiri, N. & Nasseri, S. H. (2007). Duality results and a dual simplex method for linear programming problems with trapezoidal fuzzy variables. Fuzzy Sets and Systems, 158: 1961 – 1978. (In Persian)
11
Maleki, H. R., Tata, M. & Mashinchi, M. (2000). Linear programming with fuzzy variables. Fuzzy Sets and Systems, 109: 21–33. (In Persian)
12
Menhaj, M. B., (2007). Fuzzy Computations. Daneshnegar Publication, Tehran, 324–329. (In Persian)
13
Saaty, T. (1986). Axiomatic foundation of the analytic hierarchy process. Management Science, 32(7): 841–855.
14
Shafer, G. A. (1976). Mathematical Theory of Evidence, Princeton University Press.
15
Steuer, R. (1986). Multiple criteria optimization: Theory, Computation and Applications, John Wiley. New York.
16
Tanaka, H., Guo, P. & Zimmermann, H.J. (2000). Possibility distributions of fuzzy decision variables obtained from possibilistic linear programming problems. Fuzzy Sets and Systems, 113: 323-332.
17
Tanaka, H., Okuda, T. & Asai, K. (1973). On fuzzy mathematical programming. Journal of Cybernetics Systems, 3: 37–46.
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Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8: 338–353.
19
Zimmermann, H.J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1: 45–55.
20
ORIGINAL_ARTICLE
Explanation of factors affecting supply risk management in automotive industry supply chain: A Grounded Theory
This article aims to identify what and how factors influence management of supply risk in automotive industry supply chain. Based on grounded theory and 13qualitative in-depth interviews with 10 managers in Iran's automotive industry supply chain, a theoretical framework is developed to examine antecedents and effective factors of this process. This framework suggests a broad range of factors causing an automobile manufacturer to manage the supply risk. These factors are called perceptual-attributive factors. In addition varied contextual and intervening factors are affecting automobile manufacturer decisions thereby guiding their actions/reactions in this process. Such factors are categorized as bedder factors and contingency factors.
https://imj.ut.ac.ir/article_50699_2c0560c3cf65ec0064e11bddf054745a.pdf
2014-12-22
725
746
10.22059/imj.2014.50699
automotive supply chain
Grounded theory
supply risk
Masoud
Simkhah
m_simkhah@yahoo.com
1
Ph.D. Student, Allame Tabatabaei University, Tehran,Iran
LEAD_AUTHOR
Kamran
Feizi
kamfeizi@yahoo.com
2
Prof., Allame Tabatabaei University, Tehran,Iran
AUTHOR
Laya
Olfat
olfat90@gmail.com
3
Associate Prof., Allame Tabatabaei University, Tehran,Iran
AUTHOR
Maghsod
Amiri
mg_amiri@yahoo.com
4
Prof., Allame Tabatabaei University, Tehran,Iran
AUTHOR
Aguilar, J. (1984). Trust and exchange: expressive and instrumental dimensions of reciprocity in a peasant community, Ethos, 12(1): 3-29.
1
Bazargan, A. (2008). Introduction to qualitative and mixed research methods: Common approaches in behavioral science. Didar. Tehran. (In Persian)
2
Chopra, S. & Meindle, P. (2007). Supply chain management. Prentice Hall. Harlow.
3
Cousins, P., Lamming, R. C. and Bowen, F. (2004). The role of risk in environment-related initiatives, International Journal of Operations & Production Management, 24(6): 554-565.
4
Daft, R. (2000). Organization theory and design, Cultural research Bureau. Tehran. (In Persian )
5
Deshmukh, V. (2007). The design of a decision support system for supply chain risk management, Massachusetts Institute of Technology. Massachusetts.
6
Frosdick, M. (1997). The techniques of risk management are insufficient in themselves. Disaster Prevention and Management, 6(3): 165-177.
7
Gilovich, T. (1991). How we know what isn’t so. Simon and Schuster. New York.
8
Hatch, M. (2007). Organization theory: modern, symbolic and postmodern perspectives. Afkar press. Tehran. (In Persian)
9
Hendricks, K. B. & Singhal, V. R. (2005). An empirical analysis of the effects of supply chain disruption on long-run stock price performance and equity risk of the firm. Production and Operations Management, 14(1): 35-52.
10
Hines, P., Lamming, R., Jones, D., Cousins, P. & Rich, N. (1999). Value stream management: Strategy and excellence in the supply chain. Prentice-Hall. Harlow.
11
Hood, J. & Young, P. (2005). Risk financing in UK local authorities: Is there a case for risk pooling? International Journal of Public Sector Management, 18(6): 563-578.
12
Kahneman, D. & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47: 263-291.
13
Kilgore, M. (2003). Mitigating Supply Chain Risks. White Paper, Chainalytics LLC. Atlanta.
14
Knight, F. H. (1921). Risk, Uncertainty and Profit. Houghton Mifflin. Boston.
15
Kraljic, P. (1983). Purchasing must become supply management. Harvard Business Review, 61(5): 109-117.
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21
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34
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35
Simchi-Levi, D. Kaminesky, P. Simchi-Levi, E. (2009). Designing and Management the Supply Chain, Sharif university press. Tehran. (In Persian)
36
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37
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39
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40
Waters, D. (2007). Supply chain risk management, Kogan page limited. London.
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42
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44
ORIGINAL_ARTICLE
An analysis of a new combined structural model of the Balanced Scorecard and European Foundation for Quality Management models
The present study aims to address the problem of inadequate coverage of various aspects of organizational performance by balanced scorecard (BSC) model through its developing based on the European foundation for quality management (EFQM) model. To this end, a developed BSC model, encompassing variables of stakeholders, resource effectiveness, processes effectiveness, human resource management effectiveness, leadership method, strategy and performance was proposed and, then, validated in a new structural model. The findings firstly revealed an appropriate fitness of the proposed model. Regarding the relationship among variables, staff management effectiveness, resource effectiveness, leadership method and processes effectiveness, respectively, imposed the most “direct impacts” on the organizational performance. On the other hand, “summated impacts” (direct and indirect) on the organizational performance were imposed by leadership method, staff management effectiveness, resource effectiveness and stakeholders variables, respectively. Managers can apply the model suggested in this study to measure performance in the respective organizations as well as a weighting criteria guide on how to accurately measure different dimensions of performance.
https://imj.ut.ac.ir/article_51447_e1f755b3094494070446a4402a09ab08.pdf
2014-12-22
747
765
10.22059/imj.2014.51447
Balanced scorecard (BSC)
combination of total quality management models
European Foundation for Quality Management (EFQM)
organizational performance measurement
Structural Equation Modeling
Mohammad
Sharifi-Tehrani
msharifit@chmail.ir
1
Ph.D. Candidate, Marketing Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Iran
LEAD_AUTHOR
Javad
Yousefi
yousefi.j@skpnu.ac.ir
2
Instructor, Department of Tourism Management, Payame Noor University (PNU), Tehran, Iran
AUTHOR
Akbariyyan, M. & Najafi, A. A. (2009). Integration of the EFQM excellence model and strategic management for improving organizational performance. Journal of Industrial Management, 1(2): 19-34. (In Persian).
1
Alolah, T., Stewart, R.A., Panuwatwanich, K. & Mohamed, S. (2014). Determining the causal relationships among balanced scorecard perspectives on school safety performance: Ccase of Saudi Arabia. Accident aAnalysis and pPrevention, 68: 57–74.
2
Armstrong, M. (2009). Armstrong’s handbook of performance management: an evidence-based guide to delivering high performance. London and Philadelphia: Kogan Page. London & Philadelphia.
3
Aydin, S., Kahraman, C. & Kaya, I. (2012). A new fuzzy multicriteria decision making approach: an application for European quality award assessment. Knowledge-Based Systems, 32, 37-46.
4
Azimian, M., Shahin, A., Alinaghian, M. & Badri, S. M. A. (2013). Integrative approach of DEA and mMalmquist for performance analysis of projects. Journal of Industrial Management, 5(1): 43-62. (In Persian)
5
Cavalieri, S., Gaiardelli, P. & Ierace, S. (2007). Aligning strategic profiles with operational metrics in after-sales service. International Journal of Productivity and Performance Management, 56(5/6): 436-455.
6
Chinda, T. & Mohamed, S. (2008). Structural equation model of construction safety culture. Engineering, Construction and Architectural Management, 15(2): 114-131.
7
Dabbaghi, A., Malek, A. M. (2010). Proposing a procedure to evaluate and rank corporate vision statements using a mixed methodology. Journal of Industrial Management, 2(4): 57-74. (In Persian)
8
Dehghan, N. & Sharifi-Tehrani, M. (2011). Strategic planning for the nNational mMuseum of Iran. Tourism Studies, 16: 53-90. (In Persian)
9
Dror, S. (2008). The bBalanced scorecard versus quality award models as strategic frameworks. Total Quality Management, 19(6): 583-593.
10
Eskildsen, J. K., Kristensen, K. & Juhl, H. J. (2001). The criterion weights of the EFQM excellence model. International Journal of Quality & Reliability Management, 18(8): 783-795.
11
Giessner, S. R., Knippenberg, D. V. & Sleebos, E. (2009). License to fail? How leader group prototypicality moderates the effects of leader performance on perceptions of leadership effectiveness. The Leadership Quarterly, 20: 434–451.
12
Groene, O., Brandt, E., Schmidt, W. & Moller, J. (2009). The Balanced balanced scorecard of acute settings: development process, definition of 20 strrategic objectives and implementation. International Journal for Quality in Health Care, 21(4): 259-271.
13
Huang, H.-C, Chu, W., Lai, M. -C. & Lin, L. -H. (2009). Strategic linkage process and value-driven system: A dynamic analysis of high-tech firms in a newly-industrialized country. Expert Systems with Applications, 36: 3965-3974.
14
Hunger, J. D. & Wheelen, T. L. (2010). Essentials of Strategic Management, Translated by A’arabi, S. M. & Rezvani, H. R. Cultural Research Burreau: . Tehran. (In Persian)
15
Ivanov, C.-I. & Avasilcai, S. (2014). Performance measurement models: an An analysis for measuring innovation processes performance. Procedia - Social and Behavioral Sciences, 124: 397-404.
16
Kaffashpur, A., Rahimnia, F. & Nabizadeh, T. (2011). Investigating the influence of perceived value on users’ Attitudes toward internet advertisements. New Marketing Research Journal, 1(3): 79-98. (In Persian)
17
Kaplan, R. S. & Norton, D. P. (1992). The balanced scorecard measures that drive performance. Hardvard Business Review, 70(1): 9-71.
18
Kaplan, R. S. & Norton, D. P. (1995). Having trouble with your strategy? Then map it. Hardvard Business Review on advances in Strategy,71-94.
19
Kline, R.B. (2011). Principles and practice of structural equation modeling. New York: Guilford. New York.
20
Lee, N. (2006). Measuring the performance of public sector organisations: a case study on public schools in Malaysia. Measuring Business Excellence, 10(4): 50-64.
21
Loppolo, G., Saija, G. & Salomone, R. (2012). Developing a territory balanced scorecard approach to mange projects for local development: two Two case studies. Land Use Policy, 29: 629-640.
22
Mir-Fakhreddini, S. H. & Amiri, Y. (2011). Proposing solutions to improve e-banking services using BSC, FANP & FUZZY TOPSIS (Case study: selected banks in Fars province). Journal of Industrial Management, 2(5): 141-158. (In Persian)
23
Monavarian, A. & Zoghikhah, Z. (2012). Formulating organizational strategy through integrating SWOT and BSC using QFD and MBNQA supplemented by performance measurement. Quarterly Journal of Management and Development Process, 24 (4): 21-53. (In Persian)
24
Niven, R. P. (2008). Balanced scorecard step-by-step for government and nonprofit agencies. New Jersey: John Wiley & Sons. New Jersey.
25
Perlman, Y. (2013). Causal relationships in the balanced scorecard: A path analysis approach. Journal of Management and Strategy, 4(1): 70-79.
26
Podobnik, D. & Dolinsek, S. (2008). Competitiveness and performance development: an integrated management model. Journal of Organizational Change, 21(2): 213-229.
27
Rajab-baigy, M., Forouzandeh, L., Mortazavi, M., . & Bigdeli A. (2011). Devising strategic goals and building a model for attaining them based on balanced scorecard. Quarterly Journal of Management and Development Process, 24 (2): 23-50. (In Persian)
28
Shafiee, M. Hosseinzadeh Lotfi, F. & Saleh, H. (2014). Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach. Applied Mathematical Modeling, In press.
29
Shams Layalestani, M. F. F., Raji, M. & Khajepour, M. (2013). Performance evaluation by using combined method: BSC, TOPSIS and AHP. Journal of Industrial Management, 5(1): 81-100. (In Persian)
30
Sharifi-Tehrani, M., Verbic, M. & Chung, J. Y. (2013). An analysis of adopting dual pricing for museums: The case of the National Museum of Iran. Annnuals of Tourism Research, 43(1): 58-80.
31
Sim, K. & Koh, H. (2001). Balanced scorecard: a rising trend in strategic performance measurement. Measuring Business Excellence, 5(2): 18-26.
32
Van A., E. M., Letens, G., Coleman, G.D., Farris, J. & Goubergen, D.V.et al (2005). Assessing maturity and effectiveness of enterprise performance measurement system. International Journal of Productivity Management, 54(5/6): 400-418.
33
Wongrassamee, S., Gardiner, P. D. & Simmons, J. E. L. (2003). Performance measurement tools: the balanced scorecard and the EFQM excellence model. Measuring Business Excellence, 7(1): 14-29.
34
Wu, S-I & Liu, S-I (2010). The performance measurement perspectives and causal relationship for ISO-certified companies: A case of opto-electronic industry. International Journal of Quality & Reliability Management, 27(1): 27-47.
35
Yang, H., Yeung, J.F.Y., Chan, A.P.C., Chiang, Y.H. & Chan, D.et al (2010). A critical review of performance measurement in construction. Journal of Facilities Management, 8(4): 269-284.
36
ORIGINAL_ARTICLE
Developing several pricing models in green supply chain under risk by Game Theory Approach
Green supply chain management is an environmental approach in supply chain management that aims to decrease ecological risks in products’ life cycle. Closed-loop supply chain by collecting and recycling the harmful production in nature attempts to achieve this goal. In this paper, due to different strategies in collecting products, several pricing models in two-echelon closed supply chain are presented. The interactions between the manufacturer and the retailer in pricing are investigated based on Stackelberg game and the optimal decisions of manufacturer and retailer are obtained in each model. Moreover, because of the dynamic nature of the supply chain in the real world, risk factor based on the mean-variance model is considered in the closed-loop. Finally, the presented models are analyzed using a numerical example and the best model is selected by comparing the profits. Moreover, sensitivity analyses are performed on collecting rate, recycling rate and the risk aversion. Results show that the coordinating model between the manufacturer and the retailer can be an appropriate substitution in high collecting rates and low risk aversion values.
https://imj.ut.ac.ir/article_52079_89708f042e4bb2858183b667672d4f0b.pdf
2014-12-22
767
789
10.22059/imj.2014.52079
closed-loop
Green supply chain
pricing
risk
Stackelberg game
Ghazaleh
Allameh
gh.allameh@student.alzahra.ac.ir
1
MSc. Student in Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
AUTHOR
Maryam
Esmaeili
esmaeili_m@alzahra.ac.ir
2
Assistant Prof. in Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
LEAD_AUTHOR
Taraneh
Tajvidi
t.tajvidi@alzahra.ac.ir
3
Assistant Prof. in Mathematics, Faculty of Science, Alzahra University, Tehran, Iran
AUTHOR
Abdoli, G. (2011). Game Theory and Applications, University of Tehran. Tehran. (In Persian)
1
Andiç, E., Yurt, Ö. & Baltacıoğlu, T. (2012). Green supply chains: Efforts and potential applications for the Turkish market. Resources, Conservation and Recycling, 58: 50-68.
2
Barari, S. A. (2012). A decision framework for the analysis of green supply chain contracts: An evolutionary game approach. Expert systems with applications, 39(3): 2965-2976.
3
Chung, S. L. A. M. C. (2008). Optimal policy for a closed-loop supply chain inventory system with remanufacturing. Mathematical and Computer Modelling, 48(5): 867-881.
4
Esmaeili, M. & Zeephongsekul, P. (2010). Seller-buyer models of supply chain management with an asymmetric information structure. International Journal of Production Economics, 123(1): 146-154.
5
Esmaeili , M., Abad, P. L. & Aryanezhad, M. B. (2009). Seller-buyer relationship when end demand is sensitive to price and promotion. Asia-Pacific Journal of Operational Research, 26(05): 605-621. (In Persian)
6
Esmaeili, M., Aryanezhad, M. B. & Zeephongsekul, P. (2009). A game theory approach in seller--buyer supply chain. European Journal of Operational Research, 195(2): 442-448.
7
Fatemi Ghomi, M. T. (2001). Production planing and invertory control, Amirkabir University of Technology. Tehran. (In Persian)
8
Haji, R., Maarefatdoost, M. M. & Ebrahimi, B. (2009). Finding the cost of inventory in make to order supply chain under vendor managed inventory program. Industrial Management, 1(3): (In Persian)
9
Huang, M., Song, M., Lee, L. H. & Ching, W. K. (2013). Analysis for strategy of closed-loop supply chain with dual recycling channel. International Journal of Production Economics, 144(2): 510-520.
10
Karray, S. (2011). Effectiveness of retail joint promotions under different channel structures. European Journal of Operational Research, 210(3): 745-751.
11
Majumder, P. & Groenevelt, H. (2001). Competition in Remanufacturing. Production and Operations Management, 10(2): 125-141.
12
Mirghafoori, H., Morovati Sharifabadi, A. & Assadian Ardakani, F. (2012). Evaluation of suppliers risk in supply chain using combining Fuzzy VIKOR and GRA techniques. Industrial Management, 4(2): 153-178. (In Persian)
13
Nagurney, A. & Yu, M. (2012). Sustainable fashion supply chain management under oligopolistic. International Journal of Production Economics, 135(2): 532-540.
14
Pishvaee, M. & Torabi, S. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets and Systems, 2668-2683. (In Persian)
15
Sadeghi Moghadam, M. R., Momeni, M. & Nalchigar, S. (2010). Material Flow Modeling in Supply Chain Management with Genetic Algorithm Approach. Industrial Management, 1(2), 71-88. (In Persian)
16
Savaskan, R. C. & Van Wassenhove, L. N. (2006). Reverse channel design: the case of competing retailers. Management Science, 52: 1-14.
17
Savaskan, R. C., Bhattacharya, S. & Van Wassenhove, L. N. (2004). Closed-loop supply chain models with product remanufacturing. Management science, 50(2): 239-252.
18
Xiang-yun, C. & Jian-jun, Z. (2008). Pricing and coordination analysis for a closed-loop supply chain based on game theory. Wireless Communications, Networking and Mobile Computing. WiCOM'08. 4th International Conference on (pp. 1-5). IEEE.
19
Xiao, T. & Yang, D. (2008). Price and service competition of supply chains with risk-averse retailers under demand uncertainty. International Journal of Production Economics, 187-200.
20
Xie, J. & Wei, J. C. (2009). Coordinating advertising and pricing in a manufacturer--retailer channel. European Journal of Operational Research, 197(2): 785-791.
21
Xu, G., Dan, B., Zhang, X. & Liu, C. (2014). Coordinating a dual-channel supply chain with risk-averse under a two-way revenue sharing contract. International Journal of Production Economics, 171-179.
22
Zhao, R., Neighbour, G., Han, J., McGuire, M. & Deutz, P. (2012). Using game theory to describe strategy selection for environmental risk and carbon emissions reduction in the green supply chain. Journal of Loss Prevention in the Process Industries, 25(6): 927-936.
23
ORIGINAL_ARTICLE
Performance assessment and ranking of Iranian insurance companies using an integrated model with experts preferences
This paper presents an integrated Data envelopment analysis (DEA) – Principal component analysis (PCA) – Analytical hierarchy process (AHP) to achieve the efficiency scores and ranks of the insurance companies. Fourteen insurance companies with thirteen input and output variables have been considered for the purpose of this study. Since the DEA model is sensitive to the number of variables in comparison to number of DMUs, to reduce data dimension, the PCA method is used. Obviously, the final ranks from PCA-DEA model is very subjective and only based on the pattern and distribution of data sets. Therefore, for incorporating the expert preferences, the AHP model is combined with two previous models and the final ranking is done by the integrated DEA-PCA-AHP and PCA-DEA model. The results of the model show that DANA, RAZI and DEY have become the best rank among insurance companies.
https://imj.ut.ac.ir/article_50702_10d6e0ca5dfa0915e5d55fc7bcd8c14e.pdf
2014-12-22
791
807
10.22059/imj.2014.50702
Analytical Hierarchy Process (AHP)
Data Envelopment Analysis (DEA)
Iranian Insurance companies
Principal Component Analysis (PCA)
Hashem
Omrania
h.omrani@uut.ac.ir
1
Assistant Prof., Industrial Engineering, Urmia University of Technology, Urmia, Iran
LEAD_AUTHOR
Ramin
Gharizadeh Beiragh
ramin.gharizadeh@ine.uut.ac.ir
2
MSc. Student of Industrial Engineering, Urmia University of Technology, Urmia, Iran
AUTHOR
Saeed
Shafie Kaleibari
sin_shin_88@yahoo.com
3
BS in Industrial Engineering, Payam e Noor University of Tabriz, Tabriz, Iran
AUTHOR
Adler, N. & Golany, B. (2002). Including principal component weights to improve discrimination in data envelopment analysis. Journal of the Operational Research Society, 53: 985-991.
1
Alam Tabriz, A., Rajabipoor Meybodi, A. & Zareian, M. (2010). Studying the application of fuzzy topsis in improvement of efficiency measurement of bank branches using DEA, journal of industrial management, 1(3): 99-118. (In Persian)
2
Barros, C. P., Nektarios, M. & Assaf, A. (2010). Efficiency in the Greek insurance industry. European Journal of Operational Research, 205: 431-436.
3
Bian, Y. (2012). A Gram–Schmidt process-based approach for improving DEA discrimination in the presence of large dimensionality of data set. Expert Systems with Applications, 39: 3793-3799.
4
Charnes, A., Cooper, W. W. & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2: 429-444.
5
Charnes, A., Cooper, W. W., Lewin, A. Y. & Seiford, L. M. (1994). Data envelopment analysis: theory, methodology and applications. Kluwer Academic, Boston.
6
Eling, M. & Luhnen, M. (2010). Efficiency in the international insurance industry: A cross-country comparison. Journal of Banking & Finance, 34: 1497-1509.
7
Fan, L. L. (2006). Structural health monitoring base on principal components analysis implemented on a distributed and open system. Department of Building & Construction, City University of Hong Kong.
8
Ganley, J. A. & Cubbin, J. S. (1992). Public sector efficiency measurement: applications of data envelopment analysis. Elsevier Science Publishers. Amsterdam, New York.
9
Hui, Z. & Honggeng, Y. (2011) Application of weighted principal component analysis in comprehensive evaluation for power quality. IEEE, 3: 369-372.
10
Jenkins, L. & Anderson, M. (2003). A multivariate statistical approach to reducing the number of variables in data envelopment analysis. European Journal of Operational Research, 147: 51-61.
11
Kao, C. & Hwang, S. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185: 418-429.
12
Khazaei, M. Izadbakhsh, H. (2009). Combination of DEA and PCA for Full Ranking of Decision Making Units, journal of industrial management, 1(2): 55-70. (In Persian)
13
Liang Liang, Yongjun Li, Shibing Li. (2009). Increasing the discriminatory power of DEA in the presence of the undesirable outputs and large dimensionality of data sets with PCA. Expert Systems with Applications, 36: 5895-5899.
14
Premachandra, I.M. (2001). A note on DEA vs. principal component analysis: An improvement to Joe Zhu’s approach. European Journal of Operational Research, 132: 553-560.
15
Saaty, T. L. (1980). The analytic hierarchy process. McGraw- Hill. New York.
16
Saaty, T. L. (1985). Decision making for leaders. Belmont, Life Time Leaning Publications. California.
17
Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48: 9-26.
18
Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132: 400-410.
19
Seiford, L.M. & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142: 16-20.
20
Shahriari, S. Razavi, S. Asgharizadeh, E (2013). Fuzzy data envelopment analysis and a new approach FIEP/AHP for full ranking of decision making units: A case study of humanities faculty of Tehran University, journal of industrial management, 5(1): 21-42. (In Persian)
21
Shanmugam, R. & Johnson, C. (2007). At a crossroad of data envelopment and principal component analyses. Omega, 35: 351-364.
22
Yao, S., Han, Z. & Feng, G. (2007). On technical efficiency of China's insurance industry after WTO accession. China Economic Review, 18: 66-86.
23
Zhu, J. (1998). Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities. European Journal of Operational Research, 111: 50-61.
24
ORIGINAL_ARTICLE
Modeling the effect of external sanctions on contractor's claims in DB project in Iran
Claimed have became one of the inherent attribute of construction industry. In order to determine the most appropriate strategy for prevention and resolution of these claims, some studies have been conducted to identify claim`s causes but they are common in construction industry. It seems that the reason of making these claims is a better understanding of complexity and the casual variables. In this regard, one can refer to sanctions and their possible effects on the construction industry over recent years, resulting in the creation of remarkable claims. A “cause and effect model” of the impact of sanctions on creating claims in DB project has developed by the information collected through interviews as well as the study of documents related to the recent claims. Then, the studied system is simulated under three different sanction scenarios. The results show that incidence of sanctions influence other aspects of the project and will cause new claims and affect time and cost as the main causes of claims and this effect increases with the severity of sanctions thereby casuing vigorous growth and exponential claims.
https://imj.ut.ac.ir/article_50687_b9b5da3c5400bbeaa98f0f39fdbc7197.pdf
2014-12-22
809
829
10.22059/imj.2014.50687
causes of claims
cause and effect model
sanctions
DB project
Mahmoud
Golabchi
golabchi@ut.ac.ir
1
Prof., University of Tehran, Tehran, Iran
AUTHOR
Hadi
Talkhabi
hadi.talkhabi@yahoo.com
2
MSc. Student in Project Management and Construction, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Majid
Parchami Jalal
parchamijalal@ut.ac.ir
3
Assistant Prof., University of Tehran, Tehran, Iran
AUTHOR
Mohammad
Mirkazemi Mood
mmirkazemi@ut..ac.ir
4
PhD Candidate, Management, University of Tehran, Tehran, Iran
AUTHOR
Acharya, N. & Lee, Y. (2006). Conflicting factors in construction Korean
1
perspective. Engineering, construction and architectural management. 13(6): 543-566.
2
Aghakhani, H. (2012). A new pattern of prevention and management of claims in DB and EPC contract. Thesis. Amirkabir University. Tehran. (In Persian)
3
Chester, M. & Hendrickson, CH. (2005). Cost impacts, scheduling impacts, and the claims process during construction. Journal of construction engineering and management. 131(1): 102-107.
4
Edwin, H. W. (2006). Dispute resolution management for international construction projects in China. Management decision. 43(4): 589-602.
5
Fathi, Z. (2008). Decisive causees of claim incidence in urban project contracts. Thesis. University of Science and Technology. Tehran. (In Persian)
6
Forrester, J. W. (1975). Collected papers of J. W. Forrester. Wright Allen Press.
7
Cambridge. MA, USA.
8
Ghorbani, A. (2005) The study of the major causes and origins of the financial claims of contractors and the solution to control them in Civil projects of Iran. Thesis. Amirkabir University. Tehran. (In Persian)
9
Ketabi, M. (2009). Analysis of the causes of claims of contractors with the approach of its prevention in the contract award phase. Thesis. Amirkabir University. Tehran. (In Persian)
10
Keyvani, B. (2006). Identifying claims of DBB contracts and analysis of their causes. Thesis. University of Tehran. Tehran. (In Persian)
11
Khaki, Gh. (2005). Research methodology with the approach to dissertation writing, sixth edition. Baztab Publications. Tehran. (In Persian)
12
Love, P., Davis, P., London, K. & Jasper, T. (2008). Causal modelling of construction Disputes. Proceedings of the 24th annual conference of ARCOM (Association of researchers in construction management), Cardiff, UK.
13
Mohaghar, A. & Morovati Sharif Abadi, A. (2006). Modeling just in time production using system dynamics approach. Management reasearch in Iran special issue management, 46: 269-292. (In Persian)
14
Mohaghar, A., Jabbarzadeh, Y., Amozad, H. & Mokhtarzadeh, N. (2013). Dynamic behavior of the domestic industry as a result of fluctuations in customs tariffs- case study: Application of system dynamics methodology. Journal of Industrial Management Studies, 11(28): 1-19. (In Persian)
15
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29
ORIGINAL_ARTICLE
Designing a model for selecting construction projects in public sector
Much research hasn’t been done in the area of project selection in the public sector. In this paper, we tried to choose projects in the public sector, and a linear programming model is presented. In an effort to build the model, the decision criteria were identified using the Delphi method and then the criteria were reviewed at conceptual combining. The model was constructed in a linear, goal and mixed integer programming approach. After building the model, it was tested with data on budget bill, then, the results were analyzed and the model was revised several times. The results of the model execution confirmed its validity. This type of model as a decision support system can be used for project selection in the public sector.
https://imj.ut.ac.ir/article_52033_01f42cde89ed3b4cb8dd6c4ef97eaf07.pdf
2014-12-22
831
847
10.22059/imj.2014.52033
Goal Programming
Linear programming
project selection
Public Sector
Ali
Mohaghar
amohaghar@ut.ac.ir
1
Associate Prof., Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
AUTHOR
Mohamad Reza
Mehregan
mehregan@ut.ac.ir
2
Prof., Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
AUTHOR
Adel
Azar
azara@modares.ac.ir
3
Prof., Industrial Management, Faculty of Economic and Administrative Sciences, Tarbiat Modares University, Tehran, Iran
AUTHOR
Nasser
Motahari
n.motahari@um.ac.ir
4
Assistant Prof., Management, Faculty of Economic and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
LEAD_AUTHOR
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9
Tian, Q., et al. (2002). A hybrid knowledge and model system for R&D project selection. Expert Systems with Applications, 23(3): 265-271.
10
ORIGINAL_ARTICLE
English Abstracts
https://imj.ut.ac.ir/article_55453_32717cba6ed32461258675e9fa35b162.pdf
2014-12-22
1
10
10.22059/imj.2014.55453