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EVALUATION OF PERFORMANCE WITH TOPSIS AND MULTI-MOORA METHODS BASED ON ENTROPY: A RESEARCH IN FOOD BUSINESS

Yıl 2025, Cilt: 3 Sayı: 1, 21 - 35, 30.06.2025

Öz

In a rapidly globalizing world, the human population continues to increase. The increasing population needs food to continue its existence. Situations such as climate change in line with global warming have made the food industry very important. In addition, the effects of globalization have also been reflected in the food sector. The food industry is considered an important industry that progresses in parallel with human existence and well-being and contributes to the national and international economy. Performance evaluations of enterprises operating in an important sector are of vital importance for the continuation of their existence. This research was conducted to evaluate the general performance of a food company between 2019 and 2023 based on the determined criteria. Performance evaluation criteria were determined as “production, sales, operating profit and number of employees” in line with the opinions of expert academicians. Data on the determined criteria were obtained from the activity reports of the company in question. The criteria, which were weighted objectively with the entropy method, were ranked from highest to lowest in importance as “operating profit”, “sales”, “production” and “number of employees”. The weighted criteria were analyzed with TOPSIS and Multi-MOORA methods and the results were compared. The research results show that the food company in question increased its performance every year from 2019 to 2023 according to both methods.

Kaynakça

  • Agami, N., Saleh, M. & Rasmy, M. (2012). Supply chain performance measurement approaches: review and classification. The Journal of Organizational Management Studies, 1-20.
  • Agarwal, P., Sahai, M., Mishra, V., Bag, M., & Singh, V. (2011). A review of multi-criteria decision making techniques for supplier evaluation and selection. International Journal of İndustrial Engineering Computations, 2(4), 801-810.
  • Agarwal, P., Sahai, M., Mishra, V., Bag, M., & Singh, V. (2011). A review of multi-criteria decision making techniques for supplier evaluation and selection. International Journal of İndustrial Engineering Computations, 2(4), 801-810.
  • Assaf, A. G., Oh, H., & Tsionas, M. (2017). Bayesian approach for the measurement of tourism performance: A case of stochastic frontier models. Journal of Travel Research, 56(2), 172-186.
  • Ban, A. I., Ban, O. I., Bogdan, V., Popa, D. C. S., & Tuse, D. (2020). Performance evaluation model of Romanian manufacturing listed companies by fuzzy AHP and TOPSIS. Technological and Economic Development of Economy, 26(4), 808-836.
  • Banker, R. D., Charnes, A., Cooper, W. W., Swarts, J., & Thomas, D. (1989). An introduction to data envelopment analysis with some of its models and their uses. Research in governmental and nonprofit accounting, 5(1), 125-163.
  • Bera, B., Shit, P. K., Sengupta, N., Saha, S., & Bhattacharjee, S. (2022). Susceptibility of deforestation hotspots in Terai-Dooars belt of Himalayan Foothills: A comparative analysis of VIKOR and TOPSIS models. Journal of King Saud University-Computer and Information Sciences, 34(10), 8794-8806.
  • Bhabani, B., & Mahapatro, J. (2024). Performance evaluation of priority-based scheduling in hybrid VANETs for different criteria weights using AHP-AHP and AHP-TOPSIS. IETE Journal of Research, 70(6), 5759-5770.
  • Brans, J. P., Vincke, P., & Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research, 24(2), 228-238.
  • Brauers, W. K. M. (2010). The economy of the Belgian regions tested with MULTIMOORA. Journal of Business Economics and Management, (2), 173-209.
  • Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological And Economic Development of Economy, 16(1), 5-24.
  • Chen, M. F., Tzeng, G. H., & Ding, C. G. (2003). Fuzzy MCDM approach to select service provider. In The 12th IEEE International Conference on Fuzzy Systems. FUZZ'03. (Vol. 1, pp. 572-577). IEEE.
  • Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision making methods. In Fuzzy multiple attribute decision making: Methods and application (pp.289-486). Springer Berlin Heidelberg.
  • Chiu, Y. H., & Wu, M. F. (2010). Performance evaluation of international tourism hotels in Taiwan—application of context-dependent DEA. INFOR: Information Systems and Operational Research, 48(3), 155-170.
  • Deng, H., Yeh, C. H., & Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 27(10), 963-973.
  • Deste, M., & Halifeoğlu, M. (2019). Perakende ticaret sektöründeki işletmelerin tedarik zinciri yönetimi açisindan finansal performans kriterlerinin belirlenmesi: Bist’de bir uygulama. Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(18), 751-774.
  • Eghbali-Zarch, M., Tavakkoli-Moghaddam, R., Esfahanian, F., Sepehri, M. M., & Azaron, A. (2018). Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment. Artificial İntelligence in Medicine, 87, 20-33.
  • Fadli, S., & Imtihan, K. (2019). Implementation of MOORA method in evaluating work performance of honorary teachers. Sinkron: jurnal dan penelitian teknik informatika, 4(1), 128-135.
  • George, J., Tembhare, S. K., & Jain, P. (2024). Comparative study of mcdm techniques: TOPSIS, VIKOR, and MOORA methods ıntegrated with ewm method for vendor selection for manufacturing ındustry. In Decision-Making Models and Applications in Manufacturing Environments (pp. 127-146). Apple Academic Press.
  • Gök-Kısa, A. C., Çeli̇k, P., & Peker, İ. (2022). Performance evaluation of privatized ports by entropy based TOPSIS and ARAS approach. Benchmarking: An International Journal, 29(1), 118-135.
  • Hafezalkotob, A., Hafezalkotob, A., Liao, H., & Herrera, F. (2019). An overview of MULTIMOORA for multi-criteria decision-making: Theory, developments, applications, and challenges. Information Fusion, 51, 145-177.
  • Joshi, S., Sharma, M., & Singh, R. K. (2020). Performance evaluation of agro-tourism clusters using AHP–TOPSIS. Journal of Operations and Strategic Planning, 3(1), 7-30.
  • Kakaei, H., Nourmoradi, H., Bakhtiyari, S., Jalilian, M., & Mirzaei, A. (2022). Effect of COVID-19 on food security, hunger, and food crisis. In COVID-19 and the sustainable development goals (pp. 3-29). Elsevier.
  • Khan, S. A., Chaabane, A., & Dweiri, F. (2019). A knowledge-based system for overall supply chain performance evaluation: a multi-criteria decision making approach. Supply Chain Management: An International Journal, 24(3), 377-396.
  • Kraujalienė, L. (2019). Comparative analysis of multicriteria decision-making methods evaluating the efficiency of technology transfer. Business, Management and Education, 17(1), 72-93.
  • Kumar Sahu, A., Datta, S., & Sankar Mahapatra, S. (2014). Supply chain performance benchmarking using grey-MOORA approach: An empirical research. Grey Systems: Theory and Application, 4(1), 24-55.
  • Kumar, R., Bilga, P. S., & Singh, S. (2017). Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation. Journal of cleaner production, 164, 45-57.
  • Kumar, S., Kr Singh, S., Kumar, T. A., & Agrawal, S. (2020, April). Research methodology: Prioritization of new smartphones using topsis and moora. In International conference of advance research & innovation (ICARI).
  • Kumar, V., Verma, P., Jha, A., Lai, K. K., & Do, M. H. (2022). Dynamics of a medium value consumer apparel supply chain key parameters. International Journal of Productivity and Performance Management, 71(2), 445-476.
  • Kusuma, E. D., & Sisephaputra, B. (2024). Comparison of TOPSIS, MOORA, and WASPAS methods ın website-based employee performance assessment (Case study: Tirta argapura regional public company of drinking water, probolinggo regency). Journal of Emerging Information System and Business Intelligence (JEISBI), 5(3), 53-61.
  • Li, H., Wang, W., Fan, L., Li, Q., & Chen, X. (2020). A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR. Applied Soft Computing, 91, 106207.
  • Majumdar, A., & Adhikari, A. (2021). An integrated TOPSIS-MOORA-based performance evaluation methodology for the key service providers in sharing economy: case of Airbnb superhosts. Benchmarking: An International Journal, 28(2), 600-620.
  • Mali, P. R., Vishwakarma, R. J., Isleem, H. F., Khichad, J. S., & Patil, R. B. (2024). Performance evaluation of bamboo species for structural applications using TOPSIS and VIKOR: A comparative study. Construction and Building Materials, 449, 138307.
  • Mardani, A., Jusoh, A., & Zavadskas, E. K. (2015). Fuzzy multiple criteria decision-making techniques and applications–Two decades review from 1994 to 2014. Expert Systems with Applications, 42(8), 4126-4148.
  • Mardani, A., Jusoh, A., Nor, K., Khalifah, Z., Zakwan, N., & Valipour, A. (2015). Multiple criteria decision-making techniques and their applications–a review of the literature from 2000 to 2014. Economic research-Ekonomska istraživanja, 28(1), 516-571.
  • Mardani, A., Jusoh, A., Zavadskas, E. K., Khalifah, Z., & Nor, K. M. (2015). Application of multiple-criteria decision-making techniques and approaches to evaluating of service quality: a systematic review of the literature. Journal of Business Economics and Management, 16(5), 1034-1068.
  • Mukhametzyanov, I. (2021). Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD. Decision Making: Applications in Management and Engineering, 4(2), 76-105.
  • Nakat, Z., & Bou-Mitri, C. (2021). COVID-19 and the food industry: Readiness assessment. Food control, 121, 107661.
  • Neely, A. (1999). The performance measurement revolution: why now and what next?. International Journal of Operations & Production Management, 19(2), 205-228.
  • Özcan, A., & Ömürbek, N. (2020). Bir demir çelik işletmesinin performansının çok kriterli karar verme yöntemleri ile değerlendirilmesi. IBAD Sosyal Bilimler Dergisi, (8), 77-98.
  • Pathak, D. K., Verma, A., & Kumar, V. (2020). Performance variables of GSCM for sustainability in Indian automobile organizations using TOPSIS method. Business Strategy & Development, 3(4), 590-602.
  • Peng, X., Krishankumar, R., & Ravichandran, K. S. (2021). A novel interval-valued fuzzy soft decision-making method based on CoCoSo and CRITIC for intelligent healthcare management evaluation. Soft Computing, 25, 4213-4241.
  • Petrov, I. (2021). September). Multi-criteria Evaluation of Students’ Performance Based on Hybrid AHP-Entropy Approach with TOPSIS, MOORA and WPM. In International Conference on ICT Innovations (pp. 68-84). Cham: Springer International Publishing.
  • Roy, B., & Hugonnard, J. C. (1982). Ranking of suburban line extension projects on the Paris metro system by a multicriteria method. Transportation Research Part A: General, 16(4), 301-312.
  • Saaty, T. L. (1988). What is the analytic hierarchy process? (pp. 109-121). Springer Berlin Heidelberg.
  • Sevim, F., & Aldogan, E. U. (2024). Evaluation of health systems performance of OECD countries using MOORA method. Journal of Health Management, 26(1), 172-183.
  • Sitorus, F., Cilliers, J. J., & Brito-Parada, P. R. (2019). Multi-criteria decision making for the choice problem in mining and mineral processing: Applications and trends. Expert systems with applications, 121, 393-417.
  • Teixeira, S. J., Ferreira, J. J., Wanke, P., & Moreira Antunes, J. J. (2021). Evaluation model of competitive and innovative tourism practices based on information entropy and alternative criteria weight. Tourism Economics, 27(1), 23-44.
  • Timor, M., & Mimarbasi, H. (2013). Bank branch service efficiency by data envelopment analysis and TOPSIS. Istanbul Manag. J., 24(75), 13-35.
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ENTROPİ TEMELİNDE TOPSIS VE MULTİ-MOORA YÖNTEMLERİYLE PERFORMANSIN DEĞERLENDİRİLMESİ: GIDA İŞLETMESİNDE BİR ARAŞTIRMA

Yıl 2025, Cilt: 3 Sayı: 1, 21 - 35, 30.06.2025

Öz

Hızlı bir şekilde küreselleşen dünyada insan nüfusu artmaya devam etmektedir. Artan nüfus ise varlığını devam ettirebilmek için gıdaya muhtaçtır. Küresel ısınma doğrultusunda iklim değişikleri gibi durumlar gıda endüstrisini oldukça önemli bir hale getirmiştir. Ayrıca küreselleşmenin etkileri de gıda sektörüne yansımıştır. Gıda endüstrisi, insan varlığı ve refahıyla paralel ilerleyen, ulusal ve uluslararası ekonomiye katkı sağlayan önemli bir endüstri olarak kabul edilmektedir. Önem arz eden sektörde faaliyet gösteren işletmelerin performans değerlemeleri işletmelerin varlıklarını devam ettirebilmeleri açısından hayati önem taşımaktadır. Bu araştırma bir gıda işletmesinin 2019-2023 yılları arasında sergilediği genel performansın belirlenen kriterler üzerinden değerlendirilmesi amacıyla yapılmıştır. Performans değerleme kriterleri uzman akademisyen görüşleri doğrultusunda “üretim, satış, faaliyet kârı ve çalışan sayısı” olarak belirlenmiştir. Belirlenen kriterlere ilişkin veriler söz konusu işletmenin faaliyet raporlarından elde edilmiştir. Objektif olarak entropi yöntemiyle ağırlıklandırılan kriterler önem derecesi en yüksekten en düşüğe “faaliyet kârı” , “satış”, “üretim” , “çalışan sayısı” olarak sıralanmıştır. Ağırlıklandırılan kriterler TOPSIS ve Multi-MOORA yöntemleriyle analiz edilmiş ve sonuçlar karşılaştırılmıştır. Araştırma sonuçları, söz konusu gıda işletmesinin iki yönteme göre de 2019 yılından 2023 yılına kadar her yıl performansını arttırdığını göstermektedir.

Kaynakça

  • Agami, N., Saleh, M. & Rasmy, M. (2012). Supply chain performance measurement approaches: review and classification. The Journal of Organizational Management Studies, 1-20.
  • Agarwal, P., Sahai, M., Mishra, V., Bag, M., & Singh, V. (2011). A review of multi-criteria decision making techniques for supplier evaluation and selection. International Journal of İndustrial Engineering Computations, 2(4), 801-810.
  • Agarwal, P., Sahai, M., Mishra, V., Bag, M., & Singh, V. (2011). A review of multi-criteria decision making techniques for supplier evaluation and selection. International Journal of İndustrial Engineering Computations, 2(4), 801-810.
  • Assaf, A. G., Oh, H., & Tsionas, M. (2017). Bayesian approach for the measurement of tourism performance: A case of stochastic frontier models. Journal of Travel Research, 56(2), 172-186.
  • Ban, A. I., Ban, O. I., Bogdan, V., Popa, D. C. S., & Tuse, D. (2020). Performance evaluation model of Romanian manufacturing listed companies by fuzzy AHP and TOPSIS. Technological and Economic Development of Economy, 26(4), 808-836.
  • Banker, R. D., Charnes, A., Cooper, W. W., Swarts, J., & Thomas, D. (1989). An introduction to data envelopment analysis with some of its models and their uses. Research in governmental and nonprofit accounting, 5(1), 125-163.
  • Bera, B., Shit, P. K., Sengupta, N., Saha, S., & Bhattacharjee, S. (2022). Susceptibility of deforestation hotspots in Terai-Dooars belt of Himalayan Foothills: A comparative analysis of VIKOR and TOPSIS models. Journal of King Saud University-Computer and Information Sciences, 34(10), 8794-8806.
  • Bhabani, B., & Mahapatro, J. (2024). Performance evaluation of priority-based scheduling in hybrid VANETs for different criteria weights using AHP-AHP and AHP-TOPSIS. IETE Journal of Research, 70(6), 5759-5770.
  • Brans, J. P., Vincke, P., & Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research, 24(2), 228-238.
  • Brauers, W. K. M. (2010). The economy of the Belgian regions tested with MULTIMOORA. Journal of Business Economics and Management, (2), 173-209.
  • Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological And Economic Development of Economy, 16(1), 5-24.
  • Chen, M. F., Tzeng, G. H., & Ding, C. G. (2003). Fuzzy MCDM approach to select service provider. In The 12th IEEE International Conference on Fuzzy Systems. FUZZ'03. (Vol. 1, pp. 572-577). IEEE.
  • Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision making methods. In Fuzzy multiple attribute decision making: Methods and application (pp.289-486). Springer Berlin Heidelberg.
  • Chiu, Y. H., & Wu, M. F. (2010). Performance evaluation of international tourism hotels in Taiwan—application of context-dependent DEA. INFOR: Information Systems and Operational Research, 48(3), 155-170.
  • Deng, H., Yeh, C. H., & Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 27(10), 963-973.
  • Deste, M., & Halifeoğlu, M. (2019). Perakende ticaret sektöründeki işletmelerin tedarik zinciri yönetimi açisindan finansal performans kriterlerinin belirlenmesi: Bist’de bir uygulama. Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(18), 751-774.
  • Eghbali-Zarch, M., Tavakkoli-Moghaddam, R., Esfahanian, F., Sepehri, M. M., & Azaron, A. (2018). Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment. Artificial İntelligence in Medicine, 87, 20-33.
  • Fadli, S., & Imtihan, K. (2019). Implementation of MOORA method in evaluating work performance of honorary teachers. Sinkron: jurnal dan penelitian teknik informatika, 4(1), 128-135.
  • George, J., Tembhare, S. K., & Jain, P. (2024). Comparative study of mcdm techniques: TOPSIS, VIKOR, and MOORA methods ıntegrated with ewm method for vendor selection for manufacturing ındustry. In Decision-Making Models and Applications in Manufacturing Environments (pp. 127-146). Apple Academic Press.
  • Gök-Kısa, A. C., Çeli̇k, P., & Peker, İ. (2022). Performance evaluation of privatized ports by entropy based TOPSIS and ARAS approach. Benchmarking: An International Journal, 29(1), 118-135.
  • Hafezalkotob, A., Hafezalkotob, A., Liao, H., & Herrera, F. (2019). An overview of MULTIMOORA for multi-criteria decision-making: Theory, developments, applications, and challenges. Information Fusion, 51, 145-177.
  • Joshi, S., Sharma, M., & Singh, R. K. (2020). Performance evaluation of agro-tourism clusters using AHP–TOPSIS. Journal of Operations and Strategic Planning, 3(1), 7-30.
  • Kakaei, H., Nourmoradi, H., Bakhtiyari, S., Jalilian, M., & Mirzaei, A. (2022). Effect of COVID-19 on food security, hunger, and food crisis. In COVID-19 and the sustainable development goals (pp. 3-29). Elsevier.
  • Khan, S. A., Chaabane, A., & Dweiri, F. (2019). A knowledge-based system for overall supply chain performance evaluation: a multi-criteria decision making approach. Supply Chain Management: An International Journal, 24(3), 377-396.
  • Kraujalienė, L. (2019). Comparative analysis of multicriteria decision-making methods evaluating the efficiency of technology transfer. Business, Management and Education, 17(1), 72-93.
  • Kumar Sahu, A., Datta, S., & Sankar Mahapatra, S. (2014). Supply chain performance benchmarking using grey-MOORA approach: An empirical research. Grey Systems: Theory and Application, 4(1), 24-55.
  • Kumar, R., Bilga, P. S., & Singh, S. (2017). Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation. Journal of cleaner production, 164, 45-57.
  • Kumar, S., Kr Singh, S., Kumar, T. A., & Agrawal, S. (2020, April). Research methodology: Prioritization of new smartphones using topsis and moora. In International conference of advance research & innovation (ICARI).
  • Kumar, V., Verma, P., Jha, A., Lai, K. K., & Do, M. H. (2022). Dynamics of a medium value consumer apparel supply chain key parameters. International Journal of Productivity and Performance Management, 71(2), 445-476.
  • Kusuma, E. D., & Sisephaputra, B. (2024). Comparison of TOPSIS, MOORA, and WASPAS methods ın website-based employee performance assessment (Case study: Tirta argapura regional public company of drinking water, probolinggo regency). Journal of Emerging Information System and Business Intelligence (JEISBI), 5(3), 53-61.
  • Li, H., Wang, W., Fan, L., Li, Q., & Chen, X. (2020). A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR. Applied Soft Computing, 91, 106207.
  • Majumdar, A., & Adhikari, A. (2021). An integrated TOPSIS-MOORA-based performance evaluation methodology for the key service providers in sharing economy: case of Airbnb superhosts. Benchmarking: An International Journal, 28(2), 600-620.
  • Mali, P. R., Vishwakarma, R. J., Isleem, H. F., Khichad, J. S., & Patil, R. B. (2024). Performance evaluation of bamboo species for structural applications using TOPSIS and VIKOR: A comparative study. Construction and Building Materials, 449, 138307.
  • Mardani, A., Jusoh, A., & Zavadskas, E. K. (2015). Fuzzy multiple criteria decision-making techniques and applications–Two decades review from 1994 to 2014. Expert Systems with Applications, 42(8), 4126-4148.
  • Mardani, A., Jusoh, A., Nor, K., Khalifah, Z., Zakwan, N., & Valipour, A. (2015). Multiple criteria decision-making techniques and their applications–a review of the literature from 2000 to 2014. Economic research-Ekonomska istraživanja, 28(1), 516-571.
  • Mardani, A., Jusoh, A., Zavadskas, E. K., Khalifah, Z., & Nor, K. M. (2015). Application of multiple-criteria decision-making techniques and approaches to evaluating of service quality: a systematic review of the literature. Journal of Business Economics and Management, 16(5), 1034-1068.
  • Mukhametzyanov, I. (2021). Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD. Decision Making: Applications in Management and Engineering, 4(2), 76-105.
  • Nakat, Z., & Bou-Mitri, C. (2021). COVID-19 and the food industry: Readiness assessment. Food control, 121, 107661.
  • Neely, A. (1999). The performance measurement revolution: why now and what next?. International Journal of Operations & Production Management, 19(2), 205-228.
  • Özcan, A., & Ömürbek, N. (2020). Bir demir çelik işletmesinin performansının çok kriterli karar verme yöntemleri ile değerlendirilmesi. IBAD Sosyal Bilimler Dergisi, (8), 77-98.
  • Pathak, D. K., Verma, A., & Kumar, V. (2020). Performance variables of GSCM for sustainability in Indian automobile organizations using TOPSIS method. Business Strategy & Development, 3(4), 590-602.
  • Peng, X., Krishankumar, R., & Ravichandran, K. S. (2021). A novel interval-valued fuzzy soft decision-making method based on CoCoSo and CRITIC for intelligent healthcare management evaluation. Soft Computing, 25, 4213-4241.
  • Petrov, I. (2021). September). Multi-criteria Evaluation of Students’ Performance Based on Hybrid AHP-Entropy Approach with TOPSIS, MOORA and WPM. In International Conference on ICT Innovations (pp. 68-84). Cham: Springer International Publishing.
  • Roy, B., & Hugonnard, J. C. (1982). Ranking of suburban line extension projects on the Paris metro system by a multicriteria method. Transportation Research Part A: General, 16(4), 301-312.
  • Saaty, T. L. (1988). What is the analytic hierarchy process? (pp. 109-121). Springer Berlin Heidelberg.
  • Sevim, F., & Aldogan, E. U. (2024). Evaluation of health systems performance of OECD countries using MOORA method. Journal of Health Management, 26(1), 172-183.
  • Sitorus, F., Cilliers, J. J., & Brito-Parada, P. R. (2019). Multi-criteria decision making for the choice problem in mining and mineral processing: Applications and trends. Expert systems with applications, 121, 393-417.
  • Teixeira, S. J., Ferreira, J. J., Wanke, P., & Moreira Antunes, J. J. (2021). Evaluation model of competitive and innovative tourism practices based on information entropy and alternative criteria weight. Tourism Economics, 27(1), 23-44.
  • Timor, M., & Mimarbasi, H. (2013). Bank branch service efficiency by data envelopment analysis and TOPSIS. Istanbul Manag. J., 24(75), 13-35.
  • Triantaphyllou, E., & Lin, C. T. (1996). Development and evaluation of five fuzzy multiattribute decision-making methods. İnternational Journal Of Approximate Reasoning, 14(4), 281-310.
  • Türegün, N. (2022). Financial performance evaluation by multi-criteria decision-making techniques. Heliyon, 8(5).
  • Verma, P., Kumar, V., Mittal, A., Rathore, B., Jha, A., & Rahman, M. S. (2023). The role of 3S in big data quality: a perspective on operational performance indicators using an integrated approach. The TQM Journal, 35(1), 153-182.
  • Wang, T. C., & Lee, H. D. (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications, 36(5), 8980-8985.
  • Wang, Y., Hong, L., Liu, Z., Sun, L., & Liu, L. (2024). Rheological performance evaluation of activated carbon powder modified asphalt based on TOPSIS method. Case Studies in Construction Materials, 20, e02963.
  • Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501-2519.
  • Yıldiz, A., & Yayla, A. Y. (2015). Multi-criteria decision-making methods for supplier selection: A literature review. South African Journal of Industrial Engineering, 26(2), 158-177.
  • Yi, L., Li, T., Wang, X., Ge, G., & Zhang, T. (2022). Corporate social responsibility performance evaluation from the perspective of stakeholder heterogeneity based on fuzzy analytical hierarchy process integrated TOPSIS. Corporate Social Responsibility and Environmental Management, 29(4), 918-935.
  • Zavadskas, E. K., Govindan, K., Antucheviciene, J., & Turskis, Z. (2016). Hybrid multiple criteria decision-making methods: A review of applications for sustainability issues. Economic research-Ekonomska istraživanja, 29(1), 857-887.
  • Zekan, B., Önder, I., & Gunter, U. (2019). Benchmarking of Airbnb listings: How competitive is the sharing economy sector of European cities?. Tourism Economics, 25(7), 1029-1046.
  • Žižović, M., & Pamucar, D. (2019). New model for determining criteria weights: Level Based Weight Assessment (LBWA) model. Decision Making: Applications in Management and Engineering, 2(2), 126-137.
Toplam 60 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mikro İktisat (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Zeliha Çakıroğlu 0000-0002-3956-1927

Hatice Seçil Fettahlıoğlu 0000-0001-9725-213X

Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 14 Mayıs 2025
Kabul Tarihi 23 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 3 Sayı: 1

Kaynak Göster

APA Çakıroğlu, Z., & Fettahlıoğlu, H. S. (2025). ENTROPİ TEMELİNDE TOPSIS VE MULTİ-MOORA YÖNTEMLERİYLE PERFORMANSIN DEĞERLENDİRİLMESİ: GIDA İŞLETMESİNDE BİR ARAŞTIRMA. Kahramanmaraş İstiklal Üniversitesi Sosyal Bilimler Dergisi, 3(1), 21-35.

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