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Performance comparison of software programs in solving the cardinality constrained portfolio optimization problem

Yıl 2026, Cilt: 41 Sayı: 1 , 595 - 608 , 31.03.2026
https://doi.org/10.17341/gazimmfd.1635003
https://izlik.org/JA37CL34SA

Öz

Cardinality constrained portfolio optimization is one of the important problems in financial management, where the number of assets in the portfolio is limited. Unlike classical portfolio optimization, this problem is more complex in terms of computation since it also involves asset selection. In the literature, optimization software packages are widely used to solve this problem using exact solution methods. Commonly used software packages are Lingo, Gams/Dicopt, and Gurobi. In this study, the performances of the software programs in terms of solution quality, computational efficiency, and practicality in application were analyzed and compared by using these software programs in solving the cardinality-constrained portfolio optimization problem. The analyses were performed on data sets belonging to the Hang Seng, DAX 100, FTSE 100, S&P 100, and Nikkei 225 stock market indices, which are widely used in the literature. To make a correct and unbiased comparison, each software program was applied under the same conditions by maintaining consistency in parameters, restrictions, and calculation settings. Additionally, sensitivity analysis was conducted based on the number of assets. As a result of the comparison, it was seen that Gurobi is more successful than other solvers in terms of both computation time and better performance metric values. In addition, Gurobi's good performance and higher adaptability to large data sets are due to its advanced solver algorithms.

Kaynakça

  • 1. Kelce M.G., Atalay K.D., Derya T., Improved Konno Yamazaki model: Portfolio optimization based on stochastic and fuzzy programming, Journal of The Faculty of Engineering and Architecture of Gazi University, 40 (2), 995-1009, 2025.
  • 2. Grobys K., Junttila J.P., Kolari J.W., A stablecoin that’s actually stable: A portfolio optimization approach, Journal of Financial Stability, 81, 101458, 2025.
  • 3. Cho P., Kim K., Novel approach for deep learning-based market forecasting and portfolio selection incorporating market efficiency, Expert Systems with Applications, 292, 128610, 2025.
  • 4. Markowitz H., Portfolio selection, The Journal of Finance, 7 (1), 77-91, 1952.
  • 5. World Federation of Exchanges, (n.d.), Number of listed companies, Focus. https://focus.world-exchanges.org/articles/number-listed-companies.
  • 6. Bienstock D., Computational study of a family of mixed-integer quadratic programming problems, Mathematical Programming, 74 (2), 121–140, 1996.
  • 7. Speranza M.G., A heuristic algorithm for a portfolio optimization model applied to the Milan stock market, Computers & Operations Research, 23 (5), 433–441, 1996.
  • 8. Magill M.J., Constantinides G. M., Portfolio selection with transactions costs, Journal of Economic Theory, 13 (2), 245–263, 1976.
  • 9. Konno H., Yamazaki H., Mean-absolute deviation portfolio optimization model and its applications to the Tokyo stock market, Management Science, 37 (5), 519–531, 1991.
  • 10. Anagnostopoulos K.P., Mamanis G., The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms, Expert Systems with Applications, 38 (11), 14208-14217, 2011.
  • 11. Salo A., Doumpos M., Liesiö J., Zopounidis C., Fifty years of portfolio optimization, European Journal of Operational Research, 318, 1-18, 2024.
  • 12. Zhang H., Zhao Y., Wang F., Zhang A., Yang P., Shen X., A new evolutionary algorithm based on MOEA/D for portfolio optimization, 10th International Conference on Advanced Computational Intelligence, IEEE, 831-836, 2018.
  • 13. Zhao H., Chen Z.G., Zhan Z.H., Kwong S., Zhang J., Multiple populations co-evolutionary particle swarm optimization for multi-objective cardinality constrained portfolio optimization problem, Neurocomputing, 430, 58–70, 2021.
  • 14. Cura T., A rapidly converging artificial bee colony algorithm for portfolio optimization, Knowledge-Based Systems, 233, 107505, 2021.
  • 15. Kalayci C.B., Polat O., Akbay M.A., An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization, Swarm and Evolutionary Computation, 54, 100662, 2020.
  • 16. Peng Y., Linetsky V., Partially egalitarian portfolio selection, Operations Research Letters, 52, 107055, 2024.
  • 17. Ignatov A.N., Ivanov S.V., Comparing the solvers for the mixed integer linear programming problems and the software environments that call them, Bulletin of the South Ural State University Series-Mathematical Modelling Programming & Computer Software, 17 (3), 57-72, 2024.
  • 18. Junová J., Kopa M., Measures of stochastic non-dominance in portfolio optimization, European Journal of Operational Research, 321(1), 269-283, 2024.
  • 19. Savaei E.S., Alinezhad E., Eghtesadifard M., Stock portfolio optimization for risk-averse investors: A novel hybrid possibilistic and flexible robust approach, Expert Systems with Applications, 250, 123754, 2024.
  • 20. Torrente M.L., Uberti P., A rescaling technique to improve numerical stability of portfolio optimization problems, Soft Computing, 27(18), 12831-12842, 2023.
  • 21. He X., Zhang W., Two-stage international portfolio models with higher moment risk measures, Computers & Operations Research, 154, 106200, 2023.
  • 22. Selçuklu S.B., Coit D.W., Felder F.A., Electricity generation portfolio planning and policy implications of Turkish power system considering cost, emission, and uncertainty, Energy Policy, 173, 113393, 2023.
  • 23. Goli A., Integration of blockchain-enabled closed-loop supply chain and robust product portfolio design, Computers & Industrial Engineering, 179, 109211, 2023.
  • 24. Abolmakarem S., Abdi F., Kaveh Khalili-Damghani K., Didehkhani H., Predictive multi-period multi-objective portfolio optimization based on higher order moments: Deep learning approach, Computers & Industrial Engineering. 183, 109450, 2023.
  • 25. Yadav S., Kumar A., Mehlawat M.K., Gupta P., Charles V.A multi-objective sustainable financial portfolio selection approach under an intuitionistic fuzzy framework, Information Sciences, 646, 119379, 2023.
  • 26. Fassino C., Torrente M.L., Uberti P., A singular value decomposition based approach to handle ill-conditioning in optimization problems with applications to portfolio theory, Chaos, Solitons & Fractals, 165, 112746, 2022.
  • 27. Bertsimas D., Stellato B., Online mixed-integer optimization in milliseconds, INFORMS Journal on Computing, 34 (4), 2229-2248, 2022.
  • 28. Simoglou C.K., Biskas P.N., Marneris I.G., Roumkos C.G., Long-term electricity procurement portfolio optimization, Electric Power Systems Research, 202, 107582, 2022.
  • 29. Xu D., Bai Z., Jin X., Yang X., Chen S., Zhou M., A mean-variance portfolio optimization approach for high-renewable energy hub, Applied Energy, 325, 119888, 2022.
  • 30. Barro D., Consigli G., Varun V., A stochastic programming model for dynamic portfolio management with financial derivatives, Journal of Banking & Finance, 140, 106445, 2022.
  • 31. Wu Q., Liu X., Qin J., Zhou L., Mardani A., Deveci M., An integrated multi-criteria decision-making and multi-objective optimization model for socially responsible portfolio selection, Technological Forecasting and Social Change, 184, 121977, 2022.
  • 32. Hesarsorkh A.H., Ashayeri J., Naeini A.B., Pharmaceutical R&D project portfolio selection and scheduling under uncertainty: A robust possibilistic optimization approach, Computers & Industrial Engineering, 155, 107114, 2021.
  • 33. Hashemizadeh A., Ju Y. Optimizing renewable energy portfolios with a human development approach by fuzzy interval goal programming, Sustainable Cities and Society, 75, 103396, 2021.
  • 34. Gong X., Yu C., Min L., A cloud theory-based multi-objective portfolio selection model with variable risk appetite, Expert Systems with Applications, 176, 114911, 2021.
  • 35. Gong X., Yu C., Min L., Ge Z., Regret theory-based fuzzy multi-objective portfolio selection model involving DEA cross-efficiency and higher moments, Applied Soft Computing, 100, 106958, 2021.
  • 36. Shaverdi M., Yaghoubi S., Ensafian H., A multi-objective robust possibilistic model for technology portfolio optimization considering social impact and different types of financing, Applied Soft Computing, 86, 105892, 2020.
  • 37. Mutran V.M., Ribeiro C.O., Nascimento C.A., Chachuat B., Risk-conscious optimization model to support bioenergy investments in the Brazilian sugarcane industry, Applied energy, 258, 113978, 2020.
  • 38. Ahmadi-Javid A., Fallah-Tafti M., Portfolio optimization with entropic value-at-risk, European Journal of Operational Research, 279 (1), 225-241, 2019.
  • 39. Goli A., Zare H.K., Tavakkoli-Moghaddam R., Sadeghieh A., Hybrid artificial intelligence and robust optimization for a multi-objective product portfolio problem case study: The dairy products industry, Computers & Industrial Engineering, 137, 106090, 2019.
  • 40. Forouli A., Gkonis N., Nikas A., Siskos E., Doukas H., Tourkolias C., Energy efficiency promotion in Greece in light of risk: Evaluating policies as portfolio assets, Energy, 170, 818-831, 2019.
  • 41. Nowrouzi A., Panahi M., Ghaffarzade, H., Ataei A., Optimizing Iran's natural gas export portfolio by presenting a conceptual framework for non-systematic risk based on portfolio theory, Energy Strategy Reviews, 26, 100403, 2019.
  • 42. Pankaj Gupta P., Mehlawat M K., Yadav S., Kumar A., A polynomial goal programming approach for intuitionistic fuzzy portfolio optimization using entropy and higher moments, Applied Soft Computing. 85, 105781, 2019.
  • 43. Mansour N., Cherif M.D., Abdelfattah W., Multi-objective imprecise programming for financial portfolio selection with fuzzy returns, Expert Systems with Applications, 138, 112810, 2019.
  • 44. Post T., Karabatı S., Arvanitis S., Portfolio optimization based on stochastic dominance and empirical likelihood, Journal of Econometrics, 206 (1), 167-186, 2018.
  • 45. Mohebbi N., Najafi A.A., Credibilistic multi-period portfolio optimization based on scenario tree, Physica A: Statistical Mechanics and Its Applications, 492, 1302-1316, 2018.
  • 46. Chen Y., Trifkovic M., Optimal scheduling of a microgrid in a volatile electricity market environment: Portfolio optimization approach, Applied energy, 226, 703-712, 2018.
  • 47. Charwand M., Gitizadeh M., Sian, P., A new active portfolio risk management for an electricity retailer based on a drawdown risk preference, Energy, 118, 387-398, 2017.
  • 48. Robert W. Hanks R.W., Weir J.D., Lunday B.J., Robust goal programming using different robustness echelons via norm-based and ellipsoidal uncertainty sets, European Journal of Operational Research, 262 (2), 636-646, 2017.
  • 49. Yu J.R., Chiou W.J.P., Lee W.Y., Yu K.C., Does entropy model with return forecasting enhance portfolio performance, Computers & Industrial Engineering, 114, 175-182, 2017.
  • 50. Mehlawat M.K., Credibilistic mean-entropy models for multi-period portfolio selection with multi-choice aspiration levels, Information Sciences, 35, 9-26, 2016.
  • 51. Rysz M., Vinel A., Krokhmal P., Pasiliao E.L., A scenario decomposition algorithm for stochastic programming problems with a class of downside risk measures, INFORMS Journal on Computing, 27 (2), 416-430, 2015.
  • 52. Mathuria P., Bhakar R., Li F., GenCo's optimal power portfolio selection under emission price risk, Electric Power Systems Research, 121, 279-286, 2015.
  • 53. Jafarzadeh M., Tareghian H.R., Rahbarnia F., Ghanbari R., Optimal selection of project portfolios using reinvestment strategy within a flexible time horizon, European Journal of Operational Research, 243 (2), 658-664, 2015.
  • 54. Li X., Guo S., Yu L., Skewness of fuzzy numbers and its applications in portfolio selection, IEEE Transactions on Fuzzy Systems, 23 (6), 2135–2143, 2015.
  • 55. Davari-Ardakani H., Aminnayeri M., Seifi A., A study on modeling the dynamics of statistically dependent returns, Physica A: Statistical Mechanics and its Applications, 405, 35-51, 2014.
  • 56. Gülpιnar N., Canakoglu E., Pachamanova D., Robust investment decisions under supply disruption in petroleum markets, Computers & Operations Research, 44, 75-91, 2014.
  • 57. Beyer H.G., Finck S., Breuer T., Evolution on trees: On the design of an evolution strategy for scenario-based multi-period portfolio optimization under transaction costs, Swarm and Evolutionary Computation, 17, 74-87, 2014.
  • 58. Sharma A., Mehra A., Portfolio selection with a minimax measure in safety constraint, Optimization, 62 (11), 1473-1500, 2013.
  • 59. Ahmadi A., Charwand M., Aghaei J., Risk-constrained optimal strategy for retailer forward contract portfolio, International Journal of Electrical Power & Energy Systems, 53, 704-713, 2013.
  • 60. Ustun O., Kasimbeyli R., Combined forecasts in portfolio optimization: a generalized approach, Computers & Operations Research, 39 (4), 805-819, 2012.
  • 61. Wu D., An Index tracking model: One application of integer programming, In Modeling Risk Management for Resources and Environment in China, Springer Berlin Heidelberg, 77-82, 2011.
  • 62. Khalilpour R., Karimi I. A., Investment portfolios under uncertainty for utilizing natural gas resources, Computers & chemical engineering, 35(9), 1827-1837, 2011.
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Varlık sayısı kısıtlı portföy optimizasyon probleminin çözümünde yazılım programlarının performans karşılaştırması

Yıl 2026, Cilt: 41 Sayı: 1 , 595 - 608 , 31.03.2026
https://doi.org/10.17341/gazimmfd.1635003
https://izlik.org/JA37CL34SA

Öz

Varlık sayısı kısıtlı portföy optimizasyonu, portföydeki varlık sayısının sınırlı olduğu finansal yönetimde önemli problemlerden biridir. Klasik portföy optimizasyonu aksine, bu problemde ayrıca varlık seçimi söz konusu olması nedeniyle hesaplama açısından daha karmaşıktır. Literatürde bu problemi kesin çözüm yöntemleri kullanarak çözmek için yaygın olarak optimizasyon yazılım paket programları kullanılmaktadır. Genellikle kullanılan yazılım paket programları, Lingo, Gams/Dicopt ve Gurobi’dır. Bu çalışmada, varlık sayısı kısıtlı portföy optimizasyonu probleminin çözümünde bu yazılım programları kullanılarak çözüm kalitesi, hesaplama verimliliği ve uygulamada pratiklik açısından yazılım programlarının performansları analiz edilmiş ve karşılaştırılmıştır. Analizler, literatürde yaygın olarak kullanılan Hang Seng, DAX 100, FTSE 100, S&P 100 ve Nikkei 225 borsa endekslerine ait veri setleri üzerine gerçekleştirilmiştir. Doğru ve tarafsız bir karşılaştırma yapabilmek için her bir yazılım programı, parametreler, kısıtlamalar ve hesaplama ayarlarındaki tutarlılığı koruyarak aynı koşullar altında uygulanmıştır. Ayrıca varlık sayısına göre duyarlılık analizi yapılmıştır. Karşılaştırma sonucunda Gurobi'nin hem hesaplama süresi hem de daha iyi performans metriği değerlerine sahip olması açısından diğer çözücülerden daha başarılı olduğu görülmüştür. Buna ek olarak, Gurobi’nin iyi bir performans sergilemesi ve büyük veri setlerine karşı daha yüksek uyum yeteneğine sahip olması, gelişmiş çözücü algoritmalarına sahip olmasından kaynaklanmaktadır.

Kaynakça

  • 1. Kelce M.G., Atalay K.D., Derya T., Improved Konno Yamazaki model: Portfolio optimization based on stochastic and fuzzy programming, Journal of The Faculty of Engineering and Architecture of Gazi University, 40 (2), 995-1009, 2025.
  • 2. Grobys K., Junttila J.P., Kolari J.W., A stablecoin that’s actually stable: A portfolio optimization approach, Journal of Financial Stability, 81, 101458, 2025.
  • 3. Cho P., Kim K., Novel approach for deep learning-based market forecasting and portfolio selection incorporating market efficiency, Expert Systems with Applications, 292, 128610, 2025.
  • 4. Markowitz H., Portfolio selection, The Journal of Finance, 7 (1), 77-91, 1952.
  • 5. World Federation of Exchanges, (n.d.), Number of listed companies, Focus. https://focus.world-exchanges.org/articles/number-listed-companies.
  • 6. Bienstock D., Computational study of a family of mixed-integer quadratic programming problems, Mathematical Programming, 74 (2), 121–140, 1996.
  • 7. Speranza M.G., A heuristic algorithm for a portfolio optimization model applied to the Milan stock market, Computers & Operations Research, 23 (5), 433–441, 1996.
  • 8. Magill M.J., Constantinides G. M., Portfolio selection with transactions costs, Journal of Economic Theory, 13 (2), 245–263, 1976.
  • 9. Konno H., Yamazaki H., Mean-absolute deviation portfolio optimization model and its applications to the Tokyo stock market, Management Science, 37 (5), 519–531, 1991.
  • 10. Anagnostopoulos K.P., Mamanis G., The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms, Expert Systems with Applications, 38 (11), 14208-14217, 2011.
  • 11. Salo A., Doumpos M., Liesiö J., Zopounidis C., Fifty years of portfolio optimization, European Journal of Operational Research, 318, 1-18, 2024.
  • 12. Zhang H., Zhao Y., Wang F., Zhang A., Yang P., Shen X., A new evolutionary algorithm based on MOEA/D for portfolio optimization, 10th International Conference on Advanced Computational Intelligence, IEEE, 831-836, 2018.
  • 13. Zhao H., Chen Z.G., Zhan Z.H., Kwong S., Zhang J., Multiple populations co-evolutionary particle swarm optimization for multi-objective cardinality constrained portfolio optimization problem, Neurocomputing, 430, 58–70, 2021.
  • 14. Cura T., A rapidly converging artificial bee colony algorithm for portfolio optimization, Knowledge-Based Systems, 233, 107505, 2021.
  • 15. Kalayci C.B., Polat O., Akbay M.A., An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization, Swarm and Evolutionary Computation, 54, 100662, 2020.
  • 16. Peng Y., Linetsky V., Partially egalitarian portfolio selection, Operations Research Letters, 52, 107055, 2024.
  • 17. Ignatov A.N., Ivanov S.V., Comparing the solvers for the mixed integer linear programming problems and the software environments that call them, Bulletin of the South Ural State University Series-Mathematical Modelling Programming & Computer Software, 17 (3), 57-72, 2024.
  • 18. Junová J., Kopa M., Measures of stochastic non-dominance in portfolio optimization, European Journal of Operational Research, 321(1), 269-283, 2024.
  • 19. Savaei E.S., Alinezhad E., Eghtesadifard M., Stock portfolio optimization for risk-averse investors: A novel hybrid possibilistic and flexible robust approach, Expert Systems with Applications, 250, 123754, 2024.
  • 20. Torrente M.L., Uberti P., A rescaling technique to improve numerical stability of portfolio optimization problems, Soft Computing, 27(18), 12831-12842, 2023.
  • 21. He X., Zhang W., Two-stage international portfolio models with higher moment risk measures, Computers & Operations Research, 154, 106200, 2023.
  • 22. Selçuklu S.B., Coit D.W., Felder F.A., Electricity generation portfolio planning and policy implications of Turkish power system considering cost, emission, and uncertainty, Energy Policy, 173, 113393, 2023.
  • 23. Goli A., Integration of blockchain-enabled closed-loop supply chain and robust product portfolio design, Computers & Industrial Engineering, 179, 109211, 2023.
  • 24. Abolmakarem S., Abdi F., Kaveh Khalili-Damghani K., Didehkhani H., Predictive multi-period multi-objective portfolio optimization based on higher order moments: Deep learning approach, Computers & Industrial Engineering. 183, 109450, 2023.
  • 25. Yadav S., Kumar A., Mehlawat M.K., Gupta P., Charles V.A multi-objective sustainable financial portfolio selection approach under an intuitionistic fuzzy framework, Information Sciences, 646, 119379, 2023.
  • 26. Fassino C., Torrente M.L., Uberti P., A singular value decomposition based approach to handle ill-conditioning in optimization problems with applications to portfolio theory, Chaos, Solitons & Fractals, 165, 112746, 2022.
  • 27. Bertsimas D., Stellato B., Online mixed-integer optimization in milliseconds, INFORMS Journal on Computing, 34 (4), 2229-2248, 2022.
  • 28. Simoglou C.K., Biskas P.N., Marneris I.G., Roumkos C.G., Long-term electricity procurement portfolio optimization, Electric Power Systems Research, 202, 107582, 2022.
  • 29. Xu D., Bai Z., Jin X., Yang X., Chen S., Zhou M., A mean-variance portfolio optimization approach for high-renewable energy hub, Applied Energy, 325, 119888, 2022.
  • 30. Barro D., Consigli G., Varun V., A stochastic programming model for dynamic portfolio management with financial derivatives, Journal of Banking & Finance, 140, 106445, 2022.
  • 31. Wu Q., Liu X., Qin J., Zhou L., Mardani A., Deveci M., An integrated multi-criteria decision-making and multi-objective optimization model for socially responsible portfolio selection, Technological Forecasting and Social Change, 184, 121977, 2022.
  • 32. Hesarsorkh A.H., Ashayeri J., Naeini A.B., Pharmaceutical R&D project portfolio selection and scheduling under uncertainty: A robust possibilistic optimization approach, Computers & Industrial Engineering, 155, 107114, 2021.
  • 33. Hashemizadeh A., Ju Y. Optimizing renewable energy portfolios with a human development approach by fuzzy interval goal programming, Sustainable Cities and Society, 75, 103396, 2021.
  • 34. Gong X., Yu C., Min L., A cloud theory-based multi-objective portfolio selection model with variable risk appetite, Expert Systems with Applications, 176, 114911, 2021.
  • 35. Gong X., Yu C., Min L., Ge Z., Regret theory-based fuzzy multi-objective portfolio selection model involving DEA cross-efficiency and higher moments, Applied Soft Computing, 100, 106958, 2021.
  • 36. Shaverdi M., Yaghoubi S., Ensafian H., A multi-objective robust possibilistic model for technology portfolio optimization considering social impact and different types of financing, Applied Soft Computing, 86, 105892, 2020.
  • 37. Mutran V.M., Ribeiro C.O., Nascimento C.A., Chachuat B., Risk-conscious optimization model to support bioenergy investments in the Brazilian sugarcane industry, Applied energy, 258, 113978, 2020.
  • 38. Ahmadi-Javid A., Fallah-Tafti M., Portfolio optimization with entropic value-at-risk, European Journal of Operational Research, 279 (1), 225-241, 2019.
  • 39. Goli A., Zare H.K., Tavakkoli-Moghaddam R., Sadeghieh A., Hybrid artificial intelligence and robust optimization for a multi-objective product portfolio problem case study: The dairy products industry, Computers & Industrial Engineering, 137, 106090, 2019.
  • 40. Forouli A., Gkonis N., Nikas A., Siskos E., Doukas H., Tourkolias C., Energy efficiency promotion in Greece in light of risk: Evaluating policies as portfolio assets, Energy, 170, 818-831, 2019.
  • 41. Nowrouzi A., Panahi M., Ghaffarzade, H., Ataei A., Optimizing Iran's natural gas export portfolio by presenting a conceptual framework for non-systematic risk based on portfolio theory, Energy Strategy Reviews, 26, 100403, 2019.
  • 42. Pankaj Gupta P., Mehlawat M K., Yadav S., Kumar A., A polynomial goal programming approach for intuitionistic fuzzy portfolio optimization using entropy and higher moments, Applied Soft Computing. 85, 105781, 2019.
  • 43. Mansour N., Cherif M.D., Abdelfattah W., Multi-objective imprecise programming for financial portfolio selection with fuzzy returns, Expert Systems with Applications, 138, 112810, 2019.
  • 44. Post T., Karabatı S., Arvanitis S., Portfolio optimization based on stochastic dominance and empirical likelihood, Journal of Econometrics, 206 (1), 167-186, 2018.
  • 45. Mohebbi N., Najafi A.A., Credibilistic multi-period portfolio optimization based on scenario tree, Physica A: Statistical Mechanics and Its Applications, 492, 1302-1316, 2018.
  • 46. Chen Y., Trifkovic M., Optimal scheduling of a microgrid in a volatile electricity market environment: Portfolio optimization approach, Applied energy, 226, 703-712, 2018.
  • 47. Charwand M., Gitizadeh M., Sian, P., A new active portfolio risk management for an electricity retailer based on a drawdown risk preference, Energy, 118, 387-398, 2017.
  • 48. Robert W. Hanks R.W., Weir J.D., Lunday B.J., Robust goal programming using different robustness echelons via norm-based and ellipsoidal uncertainty sets, European Journal of Operational Research, 262 (2), 636-646, 2017.
  • 49. Yu J.R., Chiou W.J.P., Lee W.Y., Yu K.C., Does entropy model with return forecasting enhance portfolio performance, Computers & Industrial Engineering, 114, 175-182, 2017.
  • 50. Mehlawat M.K., Credibilistic mean-entropy models for multi-period portfolio selection with multi-choice aspiration levels, Information Sciences, 35, 9-26, 2016.
  • 51. Rysz M., Vinel A., Krokhmal P., Pasiliao E.L., A scenario decomposition algorithm for stochastic programming problems with a class of downside risk measures, INFORMS Journal on Computing, 27 (2), 416-430, 2015.
  • 52. Mathuria P., Bhakar R., Li F., GenCo's optimal power portfolio selection under emission price risk, Electric Power Systems Research, 121, 279-286, 2015.
  • 53. Jafarzadeh M., Tareghian H.R., Rahbarnia F., Ghanbari R., Optimal selection of project portfolios using reinvestment strategy within a flexible time horizon, European Journal of Operational Research, 243 (2), 658-664, 2015.
  • 54. Li X., Guo S., Yu L., Skewness of fuzzy numbers and its applications in portfolio selection, IEEE Transactions on Fuzzy Systems, 23 (6), 2135–2143, 2015.
  • 55. Davari-Ardakani H., Aminnayeri M., Seifi A., A study on modeling the dynamics of statistically dependent returns, Physica A: Statistical Mechanics and its Applications, 405, 35-51, 2014.
  • 56. Gülpιnar N., Canakoglu E., Pachamanova D., Robust investment decisions under supply disruption in petroleum markets, Computers & Operations Research, 44, 75-91, 2014.
  • 57. Beyer H.G., Finck S., Breuer T., Evolution on trees: On the design of an evolution strategy for scenario-based multi-period portfolio optimization under transaction costs, Swarm and Evolutionary Computation, 17, 74-87, 2014.
  • 58. Sharma A., Mehra A., Portfolio selection with a minimax measure in safety constraint, Optimization, 62 (11), 1473-1500, 2013.
  • 59. Ahmadi A., Charwand M., Aghaei J., Risk-constrained optimal strategy for retailer forward contract portfolio, International Journal of Electrical Power & Energy Systems, 53, 704-713, 2013.
  • 60. Ustun O., Kasimbeyli R., Combined forecasts in portfolio optimization: a generalized approach, Computers & Operations Research, 39 (4), 805-819, 2012.
  • 61. Wu D., An Index tracking model: One application of integer programming, In Modeling Risk Management for Resources and Environment in China, Springer Berlin Heidelberg, 77-82, 2011.
  • 62. Khalilpour R., Karimi I. A., Investment portfolios under uncertainty for utilizing natural gas resources, Computers & chemical engineering, 35(9), 1827-1837, 2011.
  • 63. Xidonas P., Mavrotas G., Zopounidis C., Psarras J., IPSSIS: An integrated multicriteria decision support system for equity portfolio construction and selection, European Journal of Operational Research, 210 (2), 398-409, 2011.
  • 64. Yu J.R., Lee W.Y., Portfolio rebalancing model using multiple criteria, European Journal of Operational Research, 209 (2), 166–175, 2011.
  • 65. Cura T., Particle swarm optimization approach to portfolio optimization, Nonlinear analysis: Real world applications, 10(4), 2396-2406, 2009.
  • 66. Bacanin N., Tuba M., Firefly algorithm for cardinality constrained mean‐variance portfolio optimization problem with entropy diversity constraint, The Scientific World Journal, 2014 (1), 721521, 2014.
  • 67. Strumberger I., Tuba E., Bacanin N., Beko M., Tuba M., Hybridized artificial bee colony algorithm for constrained portfolio optimization problem, In 2018 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2018.
  • 68. Chang T.J., Meade N., Beasley J.E., Sharaiha Y.M., Heuristics for cardinality constrained portfolio optimisation, Computers & Operations Research, 27 (13), 1271-1302, 2000.
  • 69. Fernández A., Gómez S., Portfolio selection using neural networks, Computers & Operations Research, 34 (4), 1177-1191, 2007.
  • 70. Sadigh A.N., Mokhtari H., Iranpoor M., Ghomi S.M.T., Cardinality constrained portfolio optimization using a hybrid approach based on particle swarm optimization and hopfield neural network, Advanced Science Letters, 17 (1), 11-20, 2012.
  • 71. March C., Pérez C., Salido M.A., Developing an algorithm selector for green configuration in scheduling problems, arXiv preprint arXiv:2409.08641, 2024.
Toplam 71 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Endüstri Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Derya Deliktaş 0000-0003-2676-1628

Büşra Tutumlu 0000-0002-0662-8128

Özden Üstün

Gönderilme Tarihi 7 Şubat 2025
Kabul Tarihi 23 Ocak 2026
Yayımlanma Tarihi 31 Mart 2026
DOI https://doi.org/10.17341/gazimmfd.1635003
IZ https://izlik.org/JA37CL34SA
Yayımlandığı Sayı Yıl 2026 Cilt: 41 Sayı: 1

Kaynak Göster

APA Deliktaş, D., Tutumlu, B., & Üstün, Ö. (2026). Varlık sayısı kısıtlı portföy optimizasyon probleminin çözümünde yazılım programlarının performans karşılaştırması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 41(1), 595-608. https://doi.org/10.17341/gazimmfd.1635003
AMA 1.Deliktaş D, Tutumlu B, Üstün Ö. Varlık sayısı kısıtlı portföy optimizasyon probleminin çözümünde yazılım programlarının performans karşılaştırması. GUMMFD. 2026;41(1):595-608. doi:10.17341/gazimmfd.1635003
Chicago Deliktaş, Derya, Büşra Tutumlu, ve Özden Üstün. 2026. “Varlık sayısı kısıtlı portföy optimizasyon probleminin çözümünde yazılım programlarının performans karşılaştırması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 41 (1): 595-608. https://doi.org/10.17341/gazimmfd.1635003.
EndNote Deliktaş D, Tutumlu B, Üstün Ö (01 Mart 2026) Varlık sayısı kısıtlı portföy optimizasyon probleminin çözümünde yazılım programlarının performans karşılaştırması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 41 1 595–608.
IEEE [1]D. Deliktaş, B. Tutumlu, ve Ö. Üstün, “Varlık sayısı kısıtlı portföy optimizasyon probleminin çözümünde yazılım programlarının performans karşılaştırması”, GUMMFD, c. 41, sy 1, ss. 595–608, Mar. 2026, doi: 10.17341/gazimmfd.1635003.
ISNAD Deliktaş, Derya - Tutumlu, Büşra - Üstün, Özden. “Varlık sayısı kısıtlı portföy optimizasyon probleminin çözümünde yazılım programlarının performans karşılaştırması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 41/1 (01 Mart 2026): 595-608. https://doi.org/10.17341/gazimmfd.1635003.
JAMA 1.Deliktaş D, Tutumlu B, Üstün Ö. Varlık sayısı kısıtlı portföy optimizasyon probleminin çözümünde yazılım programlarının performans karşılaştırması. GUMMFD. 2026;41:595–608.
MLA Deliktaş, Derya, vd. “Varlık sayısı kısıtlı portföy optimizasyon probleminin çözümünde yazılım programlarının performans karşılaştırması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 41, sy 1, Mart 2026, ss. 595-08, doi:10.17341/gazimmfd.1635003.
Vancouver 1.Derya Deliktaş, Büşra Tutumlu, Özden Üstün. Varlık sayısı kısıtlı portföy optimizasyon probleminin çözümünde yazılım programlarının performans karşılaştırması. GUMMFD. 01 Mart 2026;41(1):595-608. doi:10.17341/gazimmfd.1635003