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A SIMULATION BASED APPROACH FOR AN INVESTMENT PROJECT EVALUATION UNDER UNCERTAIN AND RISKY ENVIRONMENTS

Year 2010, Volume: 11 Issue: 1, 1 - 16, 24.09.2010

Abstract

Under high uncertainty and risky environments, the future estimations related to project proposals
cannot be certain and really materialized values. It is inevitable that there exists a deviation or gap between
forecasted values and actual values. Thus, project risk level of the proposal should be analyzed
in the assessment phase. Simulation based project evaluation approaches enables to make more reliable
investment decision since they permits including future uncertainty and risk in analyze process. In
addition, many times, project proposals are evaluated with more than one conflicted criteria. The aim
of this paper is to present a new approach that accounts for multiple objectives for evaluating risky
investment projects and determining projects risk level. With the proposed simulation based optimization
approach, necessity values for project parameters are determined to reach the expected profitability
of the investment with the minimum initial investment cost. Also, there is an illustrative example
given in this study as an application of the proposed approach.

References

  • Al-Harbi, K.M.A. (2001). Application of the AHP in project management. International Journal of Project Management 19, 19-27
  • Ammar, E. ve Khalifa, H.A. (2005). Characterization of optimal solutions of uncertainty investment problem. Applied Mathematics and Computation 160, 111-124.
  • Aouni, B. ve Kettani, O. (2001). Goal programming model: A glorious history and a promise future. European Journal of Operational Research 133, 225-231.
  • Armaneri, Ö. ve Yalçınkaya, Ö. (2006). Evaluation of Risky Investment Projects through Simulation and Response Surface Methodology. Lectures on Modeling and Simulation. 7(2), 21-30.
  • Armaneri, Ö., Yalçınkaya, Ö. ve Eski, H. (2005). Riskli Yatırım Projelerinin Simülasyon Metamodelleme Yöntemi ile Ekonomik Açıdan Değerlendirilmesi. TMMOB Makina Mühendisleri Odası, V.Endüstri-İşletme Mühendisliği Kurultayı Bildiriler Kitabı. s.245-250.
  • Badiru, A.B. ve Sieger, D.B. (1998). Neural network as a simulation metamodel in economic analysis of risky projects. European Journal of Operational Research 105, 130-142.
  • Bellman, R. ve Zadeh, L. (1971). Decision making in a fuzzy environment. Management Science 17, 141-164.
  • Borgonovo, E. ve Peccati, L. (2004). Sensitivity analysis in investment project evaluation. International Journal of Production Economics 90, 17-25.
  • Borgonovo, E. ve Peccati, L. (2006). Uncertainty and global sensitivity analysis in the evaluation of investment projects. International Journal of Production Economics 104(1), 62-73.
  • Choobineh, F. ve Behrens, A. (1992). Use of intervals and possibility distributions in economic analysis. Journal of the Operational Research Society 43(9), s.907- 918.
  • Cochrane, J.L. ve Zeleny, M. (1973). Multiple Criteria Decision Making. Univ. South Carolina Press, Columbia, SC.
  • Derringer, G.C. ve Suich, R. (1980). Simultaneous Optimization of Several Response Variables. Journal of Quality Technology. 12, 214-219.
  • Dimova, L., Sevastianov, P. ve Sevastianov, D. (2006). MCDM in a fuzzy setting: Investment projects assessment application. International Journal of Production Economics 100(1), 10-29.
  • Enea, M. ve Piazza T. (2004), Project Selection by Constrained Fuzzy AHP. Fuzzy Optimization and Decision Making 3, 39-62.
  • Eski, H. ve Armaneri, Ö. (2006). Mühendislik Ekonomisi. Gazi Kitabevi, Ankara. Friedman, L.W. (1996). The Simulation Metamodel. Kluwer Academic Publishers, Norwell, Massachusetts.
  • Hooke, R. ve Jeeves, T.A. (1961). Direct search' solution of numerical and statistical problems. Journal of the Association for Computing Machinery. 8 (2), 212-229.
  • Huang, X. (2007). Optimal project selection with random fuzzy parameters. International Journal of Production Economics. 106(2), 513-522.
  • Ignizio, J.P. (1982). Linear Programming in Single and Multiple Objective Systems. Prentice-Hall, Englewood Cliffs, New Jersey.
  • Jovanovic, P. (1999). Application of sensitivity analysis in investment project evaluation under uncertainty and risk. International Journal of Project management 17(4), 217-222.
  • Karsak, E.E. ve Tolga, E. (2001). Fuzzy multicriteria decision-making procedure for evaluating advanced manufacturing system investments. International Journal of Production Economics 69, 49-64.
  • Keeney, R.L. ve Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Trade-Offs. Wiley, New York.
  • Kim, D., Rhee, S. ve Park, H. (2002). Modelling and optimization of a GMA welding process by genetic algorithm and response surface methodology. Interna- tional Journal of Production Research 40, 1699-1711.
  • Kleijnen, J.P.C. ve Sargent, R.G. (2000), A methodology for fitting and validating metamodels in simulation. European Journal of Operational Research 120, 14-29.
  • Law, A.M. ve Kelton, W.D. (1991). Simulation Modeling & Analysis. McGraw-Hill, Inc., Singapore.
  • Lee, J.W. ve Kim, S.H. (2001). An integrated approach for independent information system project selection. International Journal of Project Management. 19, 111-118.
  • Lefley, F. (1996). The payback method of investment appraisal: a review and synthesis. International Journal of Production Economics. 44, 207-244.
  • Lefley, F. (1997). Approaches to risk and uncertainty in the appraisal of new technology capital projects. International Journal of Production Economics 53, 21-33.
  • Madu, C.N. (1990). Simulation in Manufacturing: A Regression Metamodel Approach. Computers & Industrial Engineering 18, 381-389.
  • McHaney, R.W. ve Douglas, D.E. (1997). Multivariate regression metamodel: A DSS application in industry. Decision Support Systems 19, 43-52.
  • Medaglia, A.L., Graves, S.B. ve Ringuest, J.L. (2007). A multiobjective evolutionary approach for linearly constrained project selection under uncertainty. European Journal of Operational Research 179(3), 869-894.
  • Mohamed, S. ve McCowan, A.K. (2001). Modelling Project investment decisions under uncertainty using possibility theory. International Journal of Project Management 19, 231-241.
  • Montgomery, D.C. (2001). Design and Analysis of Experiments. John Wiley & Sons, Inc., New York, USA.
  • Myers, R.H. ve Montgomery, D.C. (1995). Response Surface Methodology: Process and Product Optimization Using Designed Experiments. John Wiley & Sons, Inc., New York, USA.
  • Oral, M., Kettani, O. ve Çınar, Ü. (2001). Project evaluation and selection in a network of collaboration. A consensual disaggregation multi-criterion approach. European Journal of Operational Research 130, 332-346.
  • Pegden, C.D., Shannon, R.E. ve Sadowski, R.P. (1990). Introduction to Simulation Using SIMAN. McGraw-Hill, Inc., New Jersey, USA.
  • Rebiasz, B. (2007). Fuzziness and randomness in investment project risk appraisal. Computers & Operations Research. 34(1), 199-210.
  • Romero, C. (1986). A survey of generalized goal programming. European Journal Operations Research 25, 183-191.
  • Saaty, T.L. (1986). Axiomatic foundation of the analytic hierarchy process. Management Science 32(7), 841-855.
  • Saaty, T.L. ve Vargas, L.G. (2000). Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. International Series in Operations Research and Management Science, Vol. 34, Kluwer, Boston.
  • Sridharan, R. ve Babu, A.S. (1998). Multilevel scheduling decisions in a class of FMS using simulation based metamodels. Journal of the Operational Research Society 49, 591-602.
  • Steuer, R.E. ve Na, P. (2003). Multiple criteria decision making combined with finance. A categorical bibliographic study. European Journal of Operational Research 150, 496-515.
  • Tamiz, M. Jones, D. ve Romero, C. (1998). Theory and Methodology: Goal programming for decision making: An overview of the current state-of-the-art. European Journal of Operational Research 111, 569-581.
  • Teng, J.Y. ve Tzeng, G.H. (1998). Transportation investment project selection using fuzzy multiobjective programming. Fuzzy Sets and Systems 96, 259-280
  • Van Groenendaal, W.J.H. (1998). Estimating NPV variability for deterministic models. European Journal of Operational Research 107, 202-213.
  • Yalçınkaya, Ö. ve Bayhan, G.M. (2009). Modelling and Optimization of Average Travel Time for a Metro Line by Simulation and Response Surface Methodology. European Journal of Operational Research. 196, 225-233.

BELİRSİZ VE RİSKLİ ORTAMLARDA YATIRIM PROJELERİNİN DEĞERLENDİRİLMESİNE YÖNELİK BENZETİM TABANLI BİR YAKLAŞIM

Year 2010, Volume: 11 Issue: 1, 1 - 16, 24.09.2010

Abstract

Belirsizlik ve riskin yüksek olduğu ortamlarda, proje önerilerine ilişkin gelecek ile ilgili yapılacak tahminler kesin ve fiilen gerçekleşen değerler olamaz. Gerçekleşeceği tahmin edilen değerler ile fiilen gerçekleşen değerler arasında az ya da çok sapmalar olması kaçınılmazdır. Bundan dolayı, yatırım projeleri değerlendirilirken, proje önerisine ait risk düzeyinin de mutlaka analiz edilmesi gerekir. Benzetimi temel alan proje değerlendirme yaklaşımları, geleceğin belirsizliğini ve riskini analiz sürecine dahil etmeye izin verdiklerinden daha güvenilir yatırım kararlarının alınmasına olanak sağlar. Bununla birlikte, proje önerileri çoğu zaman birbiriyle çelişen birden fazla kriter göz önüne alınarak değerlendirilir. Bu çalışmanın amacı, riskli yatırım projelerinin değerlendirilmesine ve özellikle proje risk düzeyinin belirlenmesine yönelik birden çok proje amacını dikkate alan yeni bir yaklaşım önermektir. Önerilen bu benzetim tabanlı eniyileme yaklaşımıyla, karar vericinin yatırımdan beklediği hedef karlılık değerine en düşük ilk yatırım maliyeti ile ulaşabilmesi için proje parametre değerlerinin ne olması gerektiği belirlenecektir. Çalışmada ayrıca önerilen yaklaşımın nasıl uygulanacağı konusunda bir örnek yer almaktadır.

References

  • Al-Harbi, K.M.A. (2001). Application of the AHP in project management. International Journal of Project Management 19, 19-27
  • Ammar, E. ve Khalifa, H.A. (2005). Characterization of optimal solutions of uncertainty investment problem. Applied Mathematics and Computation 160, 111-124.
  • Aouni, B. ve Kettani, O. (2001). Goal programming model: A glorious history and a promise future. European Journal of Operational Research 133, 225-231.
  • Armaneri, Ö. ve Yalçınkaya, Ö. (2006). Evaluation of Risky Investment Projects through Simulation and Response Surface Methodology. Lectures on Modeling and Simulation. 7(2), 21-30.
  • Armaneri, Ö., Yalçınkaya, Ö. ve Eski, H. (2005). Riskli Yatırım Projelerinin Simülasyon Metamodelleme Yöntemi ile Ekonomik Açıdan Değerlendirilmesi. TMMOB Makina Mühendisleri Odası, V.Endüstri-İşletme Mühendisliği Kurultayı Bildiriler Kitabı. s.245-250.
  • Badiru, A.B. ve Sieger, D.B. (1998). Neural network as a simulation metamodel in economic analysis of risky projects. European Journal of Operational Research 105, 130-142.
  • Bellman, R. ve Zadeh, L. (1971). Decision making in a fuzzy environment. Management Science 17, 141-164.
  • Borgonovo, E. ve Peccati, L. (2004). Sensitivity analysis in investment project evaluation. International Journal of Production Economics 90, 17-25.
  • Borgonovo, E. ve Peccati, L. (2006). Uncertainty and global sensitivity analysis in the evaluation of investment projects. International Journal of Production Economics 104(1), 62-73.
  • Choobineh, F. ve Behrens, A. (1992). Use of intervals and possibility distributions in economic analysis. Journal of the Operational Research Society 43(9), s.907- 918.
  • Cochrane, J.L. ve Zeleny, M. (1973). Multiple Criteria Decision Making. Univ. South Carolina Press, Columbia, SC.
  • Derringer, G.C. ve Suich, R. (1980). Simultaneous Optimization of Several Response Variables. Journal of Quality Technology. 12, 214-219.
  • Dimova, L., Sevastianov, P. ve Sevastianov, D. (2006). MCDM in a fuzzy setting: Investment projects assessment application. International Journal of Production Economics 100(1), 10-29.
  • Enea, M. ve Piazza T. (2004), Project Selection by Constrained Fuzzy AHP. Fuzzy Optimization and Decision Making 3, 39-62.
  • Eski, H. ve Armaneri, Ö. (2006). Mühendislik Ekonomisi. Gazi Kitabevi, Ankara. Friedman, L.W. (1996). The Simulation Metamodel. Kluwer Academic Publishers, Norwell, Massachusetts.
  • Hooke, R. ve Jeeves, T.A. (1961). Direct search' solution of numerical and statistical problems. Journal of the Association for Computing Machinery. 8 (2), 212-229.
  • Huang, X. (2007). Optimal project selection with random fuzzy parameters. International Journal of Production Economics. 106(2), 513-522.
  • Ignizio, J.P. (1982). Linear Programming in Single and Multiple Objective Systems. Prentice-Hall, Englewood Cliffs, New Jersey.
  • Jovanovic, P. (1999). Application of sensitivity analysis in investment project evaluation under uncertainty and risk. International Journal of Project management 17(4), 217-222.
  • Karsak, E.E. ve Tolga, E. (2001). Fuzzy multicriteria decision-making procedure for evaluating advanced manufacturing system investments. International Journal of Production Economics 69, 49-64.
  • Keeney, R.L. ve Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Trade-Offs. Wiley, New York.
  • Kim, D., Rhee, S. ve Park, H. (2002). Modelling and optimization of a GMA welding process by genetic algorithm and response surface methodology. Interna- tional Journal of Production Research 40, 1699-1711.
  • Kleijnen, J.P.C. ve Sargent, R.G. (2000), A methodology for fitting and validating metamodels in simulation. European Journal of Operational Research 120, 14-29.
  • Law, A.M. ve Kelton, W.D. (1991). Simulation Modeling & Analysis. McGraw-Hill, Inc., Singapore.
  • Lee, J.W. ve Kim, S.H. (2001). An integrated approach for independent information system project selection. International Journal of Project Management. 19, 111-118.
  • Lefley, F. (1996). The payback method of investment appraisal: a review and synthesis. International Journal of Production Economics. 44, 207-244.
  • Lefley, F. (1997). Approaches to risk and uncertainty in the appraisal of new technology capital projects. International Journal of Production Economics 53, 21-33.
  • Madu, C.N. (1990). Simulation in Manufacturing: A Regression Metamodel Approach. Computers & Industrial Engineering 18, 381-389.
  • McHaney, R.W. ve Douglas, D.E. (1997). Multivariate regression metamodel: A DSS application in industry. Decision Support Systems 19, 43-52.
  • Medaglia, A.L., Graves, S.B. ve Ringuest, J.L. (2007). A multiobjective evolutionary approach for linearly constrained project selection under uncertainty. European Journal of Operational Research 179(3), 869-894.
  • Mohamed, S. ve McCowan, A.K. (2001). Modelling Project investment decisions under uncertainty using possibility theory. International Journal of Project Management 19, 231-241.
  • Montgomery, D.C. (2001). Design and Analysis of Experiments. John Wiley & Sons, Inc., New York, USA.
  • Myers, R.H. ve Montgomery, D.C. (1995). Response Surface Methodology: Process and Product Optimization Using Designed Experiments. John Wiley & Sons, Inc., New York, USA.
  • Oral, M., Kettani, O. ve Çınar, Ü. (2001). Project evaluation and selection in a network of collaboration. A consensual disaggregation multi-criterion approach. European Journal of Operational Research 130, 332-346.
  • Pegden, C.D., Shannon, R.E. ve Sadowski, R.P. (1990). Introduction to Simulation Using SIMAN. McGraw-Hill, Inc., New Jersey, USA.
  • Rebiasz, B. (2007). Fuzziness and randomness in investment project risk appraisal. Computers & Operations Research. 34(1), 199-210.
  • Romero, C. (1986). A survey of generalized goal programming. European Journal Operations Research 25, 183-191.
  • Saaty, T.L. (1986). Axiomatic foundation of the analytic hierarchy process. Management Science 32(7), 841-855.
  • Saaty, T.L. ve Vargas, L.G. (2000). Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. International Series in Operations Research and Management Science, Vol. 34, Kluwer, Boston.
  • Sridharan, R. ve Babu, A.S. (1998). Multilevel scheduling decisions in a class of FMS using simulation based metamodels. Journal of the Operational Research Society 49, 591-602.
  • Steuer, R.E. ve Na, P. (2003). Multiple criteria decision making combined with finance. A categorical bibliographic study. European Journal of Operational Research 150, 496-515.
  • Tamiz, M. Jones, D. ve Romero, C. (1998). Theory and Methodology: Goal programming for decision making: An overview of the current state-of-the-art. European Journal of Operational Research 111, 569-581.
  • Teng, J.Y. ve Tzeng, G.H. (1998). Transportation investment project selection using fuzzy multiobjective programming. Fuzzy Sets and Systems 96, 259-280
  • Van Groenendaal, W.J.H. (1998). Estimating NPV variability for deterministic models. European Journal of Operational Research 107, 202-213.
  • Yalçınkaya, Ö. ve Bayhan, G.M. (2009). Modelling and Optimization of Average Travel Time for a Metro Line by Simulation and Response Surface Methodology. European Journal of Operational Research. 196, 225-233.
There are 45 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Özgür Armaneri This is me

Özgür Yalçınkaya

Publication Date September 24, 2010
Published in Issue Year 2010 Volume: 11 Issue: 1

Cite

APA Armaneri, Ö., & Yalçınkaya, Ö. (2010). A SIMULATION BASED APPROACH FOR AN INVESTMENT PROJECT EVALUATION UNDER UNCERTAIN AND RISKY ENVIRONMENTS. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, 11(1), 1-16.
AMA Armaneri Ö, Yalçınkaya Ö. A SIMULATION BASED APPROACH FOR AN INVESTMENT PROJECT EVALUATION UNDER UNCERTAIN AND RISKY ENVIRONMENTS. AUJST-A. September 2010;11(1):1-16.
Chicago Armaneri, Özgür, and Özgür Yalçınkaya. “A SIMULATION BASED APPROACH FOR AN INVESTMENT PROJECT EVALUATION UNDER UNCERTAIN AND RISKY ENVIRONMENTS”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 11, no. 1 (September 2010): 1-16.
EndNote Armaneri Ö, Yalçınkaya Ö (September 1, 2010) A SIMULATION BASED APPROACH FOR AN INVESTMENT PROJECT EVALUATION UNDER UNCERTAIN AND RISKY ENVIRONMENTS. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 11 1 1–16.
IEEE Ö. Armaneri and Ö. Yalçınkaya, “A SIMULATION BASED APPROACH FOR AN INVESTMENT PROJECT EVALUATION UNDER UNCERTAIN AND RISKY ENVIRONMENTS”, AUJST-A, vol. 11, no. 1, pp. 1–16, 2010.
ISNAD Armaneri, Özgür - Yalçınkaya, Özgür. “A SIMULATION BASED APPROACH FOR AN INVESTMENT PROJECT EVALUATION UNDER UNCERTAIN AND RISKY ENVIRONMENTS”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 11/1 (September 2010), 1-16.
JAMA Armaneri Ö, Yalçınkaya Ö. A SIMULATION BASED APPROACH FOR AN INVESTMENT PROJECT EVALUATION UNDER UNCERTAIN AND RISKY ENVIRONMENTS. AUJST-A. 2010;11:1–16.
MLA Armaneri, Özgür and Özgür Yalçınkaya. “A SIMULATION BASED APPROACH FOR AN INVESTMENT PROJECT EVALUATION UNDER UNCERTAIN AND RISKY ENVIRONMENTS”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, vol. 11, no. 1, 2010, pp. 1-16.
Vancouver Armaneri Ö, Yalçınkaya Ö. A SIMULATION BASED APPROACH FOR AN INVESTMENT PROJECT EVALUATION UNDER UNCERTAIN AND RISKY ENVIRONMENTS. AUJST-A. 2010;11(1):1-16.