Research Article
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Year 2024, Volume: 3 Issue: 1, 398 - 419, 29.07.2024

Abstract

References

  • Alinezhad, A., & Taherinezhad, A. (2020). Control Chart Recognition Patterns Using Fuzzy Rule-Based System. Iranian Journal of Optimization, 12(2), 149-160. Dor: https://dorl.net/dor/20.1001.1.25885723.2020.12.2.2.0
  • Alinezhad, A., & Taherinezhad, A. (2021). Performance Evaluation of Production Chain using Two-Stage DEA Method (Case Study: Iranian Poultry Industry). new economy and trade, 16(3), 105-130. Doi: https://doi.org/10.30465/jnet.2022.36849.1741
  • Alinezhad, A., Heidaryan, L., & Taherinezhad, A. (2023). Ranking the Measurement System of Auto Parts Companies via MSA–MADM Combinatorial Method under Fuzzy Conditions. Sharif Journal of Industrial Engineering & Management, 38.1(2), 15-27. Doi: https://doi.org/10.24200/j65.2022.56897.2176
  • Alinezhad, A., Makui, A., & Mavi, R. K. (2007). An inverse DEA model for inputs/outputs estimation with respect to decision maker’s preferences: The case of Refah bank of IRAN. Mathematical Sciences, 1(1-2), 61-70.
  • Amini, A., Alinezhad, A., & Yazdipoor, F. (2019). A TOPSIS, VIKOR and DEA integrated evaluation method with belief structure under uncertainty to rank alternatives. International Journal of Advanced Operations Management, 11(3), 171-188.
  • Ashour, O. M., & Kremer, G. E. O. (2013). A simulation analysis of the impact of FAHP–MAUT triage algorithm on the Emergency Department performance measures. Expert Systems with Applications, 40(1), 177-187. Doi: https://doi.org/10.1016/j.eswa.2012.07.024
  • Badiru, A. B., Pulat, P. S., & Kang, M. (1993). DDM: Decision support system for hierarchical dynamic decision making. Decision Support Systems, 10(1), 1-18. Doi: https://doi.org/10.1016/0167-9236(93)90002-K
  • Brehmer, B. (1992). Dynamic decision making: Human control of complex systems. Acta psychologica, 81(3), 211-241. Doi: https://doi.org/10.1016/0001-6918(92)90019-A
  • Campanella, G., & Ribeiro, R. A. (2011). A framework for dynamic multiple-criteria decision making. Decision Support Systems, 52(1), 52-60. Doi: https://doi.org/10.1016/j.dss.2011.05.003
  • Chang, T. H. (2014). Fuzzy VIKOR method: A case study of the hospital service evaluation in Taiwan. Information Sciences, 271, 196-212. Doi: https://doi.org/10.1016/j.ins.2014.02.118
  • Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9. Doi: https://doi.org/10.1016/S0165-0114(97)00377-1
  • Chen, Y., & Li, B. (2011). Dynamic multi-attribute decision making model based on triangular intuitionistic fuzzy numbers. Scientia Iranica, 18(2), 268-274. Doi: https://doi.org/10.1016/j.scient.2011.03.022
  • Chen, Y., Li, K. W., & He, S. (2010, October). Dynamic multiple criteria decision analysis with application in emergency management assessment. In 2010 IEEE International Conference on Systems, Man and Cybernetics (pp. 3513-3517). IEEE. Doi: https://doi.org/10.1109/ICSMC.2010.5642410
  • Dubois, D. J. (1980). Fuzzy sets and systems: theory and applications (Vol. 144). Academic press. Gilboy, N., Tanabe, T., Travers, D., & Rosenau, A. M. (2011). Emergency severity index (esi): A triage tool for emergency department. Rockville, MD: Agency for Healthcare Research and Quality.
  • Hu, J., & Yang, L. (2011). Dynamic stochastic multi-criteria decision making method based on cumulative prospect theory and set pair analysis. Systems Engineering Procedia, 1, 432-439. Doi: https://doi.org/10.1016/j.sepro.2011.08.064
  • İşler, M. & Çalık, A. (2022). An approach to islamic investment decision making based on integrated Entropy and WASPAS methods. Journal of Optimization and Decision Making, 1(2), 100-113. Retrieved from https://dergipark.org.tr/en/pub/jodm/issue/76302/1257617
  • Jassbi, J. J., Ribeiro, R. A., & Varela, L. R. (2014). Dynamic MCDM with future knowledge for supplier selection. Journal of Decision Systems, 23(3), 232-248. Doi: https://doi.org/10.1080/12460125.2014.886850
  • Khalili, J., & Alinezhad, A. (2018). Performance evaluation in green supply chain using BSC, DEA and data mining. International journal of supply and operations management, 5(2), 182-191. Doi: https://dx.doi.org/10.22034/2018.2.6
  • Kiani Mavi, R., Makui, A., Fazli, S., & Alinezhad, A. (2010). A forecasting method in data envelopment analysis with group decision making. International Journal of Applied Management Science, 2(2), 152-168. Doi: https://doi.org/10.1504/IJAMS.2010.031084
  • Klir, G. J., & Folger, T. A. (1987). Fuzzy sets, uncertainty, and information. Prentice-Hall, Inc.
  • Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic (Vol. 4, pp. 1-12). New Jersey: Prentice hall.
  • Lin, Y. H., Lee, P. C., & Ting, H. I. (2008). Dynamic multi-attribute decision making model with grey number evaluations. Expert Systems with Applications, 35(4), 1638-1644. Doi: https://doi.org/10.1016/j.eswa.2007.08.064
  • Lourenzutti, R., & Krohling, R. A. (2016). A generalized TOPSIS method for group decision making with heterogeneous information in a dynamic environment. Information Sciences, 330, 1-18. Doi: https://doi.org/10.1016/j.ins.2015.10.005
  • Norouziyan, S. (2022). Application of Analytic Hierarchy Process Method and VIKOR for ABS Market of Countries. Journal of Optimization and Decision Making, 1(1), 19-27. Retrieved from https://dergipark.org.tr/en/pub/jodm/issue/76301/1257552
  • Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European journal of operational research, 156(2), 445-455. Doi: https://doi.org/10.1016/S0377-2217(03)00020-1
  • Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European journal of operational research, 178(2), 514-529. Doi: https://doi.org/10.1016/j.ejor.2006.01.020
  • Peng, K. H., & Tzeng, G. H. (2013). A hybrid dynamic MADM model for problem-improvement in economics and business. Technological and Economic Development of Economy, 19(4), 638-660. Doi: https://doi.org/10.3846/20294913.2013.837114
  • Ramadan, Ö. & Özdemir, Y. S. (2022). Prioritization of rail system projects by using FUZZY AHP and PROMETHEE. Journal of Optimization and Decision Making, 1 (2), 114-122. Retrieved from https://dergipark.org.tr/en/pub/jodm/issue/76302/1257621
  • Sabry, A. A. F., Abdel Salam, W. N., Abdel Salam, M. M., Moustafa, K. S., Gaber, E. M., & Beshey, B. N. (2023). Impact of implementing five-level triage system on patients outcomes and resource utilization in the emergency department of Alexandria main university hospital. Egyptian Journal of Anaesthesia, 39(1), 546-556. Doi: https://doi.org/10.1080/11101849.2023.2234712
  • Sarrafha, K., Kazemi, A., & Alinezhad, A. (2014). A multi-objective evolutionary approach for integrated production-distribution planning problem in a supply chain network. Journal of Optimization in Industrial Engineering, 7(14), 89-102. Dor: https://dorl.net/dor/20.1001.1.22519904.2014.7.14.8.6
  • Schweizer, B., & Sklar, A. (2011). Probabilistic metric spaces. Courier Corporation.
  • Stewart, J. V. (2003). Vital Signs and resuscitation. CRC Press. Doi: https://doi.org/10.1201/9781498713771
  • Taherinezhad, A., & Alinezhad, A. (2022). COVID-19 Crisis Management: Global Appraisal using Two-Stage DEA and Ensemble Learning Algorithms. Scientia Iranica, (Article in press). Doi: https://doi.org/10.24200/sci.2022.58911.5962
  • Taherinezhad, A., & Alinezhad, A. (2023). Nations performance evaluation during SARS-CoV-2 outbreak handling via data envelopment analysis and machine learning methods. International Journal of Systems Science: Operations & Logistics, 10(1), 2022243. Doi: https://doi.org/10.1080/23302674.2021.2022243
  • Travers, D. A., Waller, A. E., Bowling, J. M., Flowers, D., & Tintinalli, J. (2002). Five-level triage system more effective than three-level in tertiary emergency department. Journal of emergency nursing, 28(5), 395-400. Doi: https://doi.org/10.1067/men.2002.127184
  • Wang, L., Zhang, Z. X., & Wang, Y. M. (2015). A prospect theory-based interval dynamic reference point method for emergency decision making. Expert Systems with Applications, 42(23), 9379-9388. Doi: https://doi.org/10.1016/j.eswa.2015.07.056
  • Wei, G. W. (2009). Some geometric aggregation functions and their application to dynamic multiple attribute decision making in the intuitionistic fuzzy setting. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 17(02), 179-196. Doi: https://doi.org/10.1142/S0218488509005802
  • Zadeh, L. A. (1983). Linguistic variables, approximate reasoning and dispositions. Medical Informatics, 8(3), 173-186. Doi: https://doi.org/10.3109/14639238309016081

Fuzzy VIKOR Method for Dynamic MADM Problem Solution in ESI 5-Level Triage System

Year 2024, Volume: 3 Issue: 1, 398 - 419, 29.07.2024

Abstract

Multiple Attribute Decision Making (MADM) tool make preference decisions over multiple attributes’ alternatives available, which in most of the cases conflict among themselves. The classic MADM includes techniques that consider a set of fixed and predefined attributes when making a decision. However, majority of real-world decisions occur in dynamic and unstable scenarios. Therefore, classic MADM will not be the answer to our problems in the real world and uncertainty. This paper addresses a flexible framework for dynamic MADM, based on the concept of fuzzy sets theory and the VIKOR method to provide a rational, scientific and systematic process for prioritizing patients in Emergency Department (ED) under a fuzzy environment where the uncertainty, subjectivity, and vagueness are addressed with linguistic variables parameterized by triangular fuzzy numbers. Finally, the computational results are discussed in detail. Dynamic decisions arise in many applications including military, medical, management, sports and emergency situations. Therefore, this study can affect a wide range of applied fields.

References

  • Alinezhad, A., & Taherinezhad, A. (2020). Control Chart Recognition Patterns Using Fuzzy Rule-Based System. Iranian Journal of Optimization, 12(2), 149-160. Dor: https://dorl.net/dor/20.1001.1.25885723.2020.12.2.2.0
  • Alinezhad, A., & Taherinezhad, A. (2021). Performance Evaluation of Production Chain using Two-Stage DEA Method (Case Study: Iranian Poultry Industry). new economy and trade, 16(3), 105-130. Doi: https://doi.org/10.30465/jnet.2022.36849.1741
  • Alinezhad, A., Heidaryan, L., & Taherinezhad, A. (2023). Ranking the Measurement System of Auto Parts Companies via MSA–MADM Combinatorial Method under Fuzzy Conditions. Sharif Journal of Industrial Engineering & Management, 38.1(2), 15-27. Doi: https://doi.org/10.24200/j65.2022.56897.2176
  • Alinezhad, A., Makui, A., & Mavi, R. K. (2007). An inverse DEA model for inputs/outputs estimation with respect to decision maker’s preferences: The case of Refah bank of IRAN. Mathematical Sciences, 1(1-2), 61-70.
  • Amini, A., Alinezhad, A., & Yazdipoor, F. (2019). A TOPSIS, VIKOR and DEA integrated evaluation method with belief structure under uncertainty to rank alternatives. International Journal of Advanced Operations Management, 11(3), 171-188.
  • Ashour, O. M., & Kremer, G. E. O. (2013). A simulation analysis of the impact of FAHP–MAUT triage algorithm on the Emergency Department performance measures. Expert Systems with Applications, 40(1), 177-187. Doi: https://doi.org/10.1016/j.eswa.2012.07.024
  • Badiru, A. B., Pulat, P. S., & Kang, M. (1993). DDM: Decision support system for hierarchical dynamic decision making. Decision Support Systems, 10(1), 1-18. Doi: https://doi.org/10.1016/0167-9236(93)90002-K
  • Brehmer, B. (1992). Dynamic decision making: Human control of complex systems. Acta psychologica, 81(3), 211-241. Doi: https://doi.org/10.1016/0001-6918(92)90019-A
  • Campanella, G., & Ribeiro, R. A. (2011). A framework for dynamic multiple-criteria decision making. Decision Support Systems, 52(1), 52-60. Doi: https://doi.org/10.1016/j.dss.2011.05.003
  • Chang, T. H. (2014). Fuzzy VIKOR method: A case study of the hospital service evaluation in Taiwan. Information Sciences, 271, 196-212. Doi: https://doi.org/10.1016/j.ins.2014.02.118
  • Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9. Doi: https://doi.org/10.1016/S0165-0114(97)00377-1
  • Chen, Y., & Li, B. (2011). Dynamic multi-attribute decision making model based on triangular intuitionistic fuzzy numbers. Scientia Iranica, 18(2), 268-274. Doi: https://doi.org/10.1016/j.scient.2011.03.022
  • Chen, Y., Li, K. W., & He, S. (2010, October). Dynamic multiple criteria decision analysis with application in emergency management assessment. In 2010 IEEE International Conference on Systems, Man and Cybernetics (pp. 3513-3517). IEEE. Doi: https://doi.org/10.1109/ICSMC.2010.5642410
  • Dubois, D. J. (1980). Fuzzy sets and systems: theory and applications (Vol. 144). Academic press. Gilboy, N., Tanabe, T., Travers, D., & Rosenau, A. M. (2011). Emergency severity index (esi): A triage tool for emergency department. Rockville, MD: Agency for Healthcare Research and Quality.
  • Hu, J., & Yang, L. (2011). Dynamic stochastic multi-criteria decision making method based on cumulative prospect theory and set pair analysis. Systems Engineering Procedia, 1, 432-439. Doi: https://doi.org/10.1016/j.sepro.2011.08.064
  • İşler, M. & Çalık, A. (2022). An approach to islamic investment decision making based on integrated Entropy and WASPAS methods. Journal of Optimization and Decision Making, 1(2), 100-113. Retrieved from https://dergipark.org.tr/en/pub/jodm/issue/76302/1257617
  • Jassbi, J. J., Ribeiro, R. A., & Varela, L. R. (2014). Dynamic MCDM with future knowledge for supplier selection. Journal of Decision Systems, 23(3), 232-248. Doi: https://doi.org/10.1080/12460125.2014.886850
  • Khalili, J., & Alinezhad, A. (2018). Performance evaluation in green supply chain using BSC, DEA and data mining. International journal of supply and operations management, 5(2), 182-191. Doi: https://dx.doi.org/10.22034/2018.2.6
  • Kiani Mavi, R., Makui, A., Fazli, S., & Alinezhad, A. (2010). A forecasting method in data envelopment analysis with group decision making. International Journal of Applied Management Science, 2(2), 152-168. Doi: https://doi.org/10.1504/IJAMS.2010.031084
  • Klir, G. J., & Folger, T. A. (1987). Fuzzy sets, uncertainty, and information. Prentice-Hall, Inc.
  • Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic (Vol. 4, pp. 1-12). New Jersey: Prentice hall.
  • Lin, Y. H., Lee, P. C., & Ting, H. I. (2008). Dynamic multi-attribute decision making model with grey number evaluations. Expert Systems with Applications, 35(4), 1638-1644. Doi: https://doi.org/10.1016/j.eswa.2007.08.064
  • Lourenzutti, R., & Krohling, R. A. (2016). A generalized TOPSIS method for group decision making with heterogeneous information in a dynamic environment. Information Sciences, 330, 1-18. Doi: https://doi.org/10.1016/j.ins.2015.10.005
  • Norouziyan, S. (2022). Application of Analytic Hierarchy Process Method and VIKOR for ABS Market of Countries. Journal of Optimization and Decision Making, 1(1), 19-27. Retrieved from https://dergipark.org.tr/en/pub/jodm/issue/76301/1257552
  • Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European journal of operational research, 156(2), 445-455. Doi: https://doi.org/10.1016/S0377-2217(03)00020-1
  • Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European journal of operational research, 178(2), 514-529. Doi: https://doi.org/10.1016/j.ejor.2006.01.020
  • Peng, K. H., & Tzeng, G. H. (2013). A hybrid dynamic MADM model for problem-improvement in economics and business. Technological and Economic Development of Economy, 19(4), 638-660. Doi: https://doi.org/10.3846/20294913.2013.837114
  • Ramadan, Ö. & Özdemir, Y. S. (2022). Prioritization of rail system projects by using FUZZY AHP and PROMETHEE. Journal of Optimization and Decision Making, 1 (2), 114-122. Retrieved from https://dergipark.org.tr/en/pub/jodm/issue/76302/1257621
  • Sabry, A. A. F., Abdel Salam, W. N., Abdel Salam, M. M., Moustafa, K. S., Gaber, E. M., & Beshey, B. N. (2023). Impact of implementing five-level triage system on patients outcomes and resource utilization in the emergency department of Alexandria main university hospital. Egyptian Journal of Anaesthesia, 39(1), 546-556. Doi: https://doi.org/10.1080/11101849.2023.2234712
  • Sarrafha, K., Kazemi, A., & Alinezhad, A. (2014). A multi-objective evolutionary approach for integrated production-distribution planning problem in a supply chain network. Journal of Optimization in Industrial Engineering, 7(14), 89-102. Dor: https://dorl.net/dor/20.1001.1.22519904.2014.7.14.8.6
  • Schweizer, B., & Sklar, A. (2011). Probabilistic metric spaces. Courier Corporation.
  • Stewart, J. V. (2003). Vital Signs and resuscitation. CRC Press. Doi: https://doi.org/10.1201/9781498713771
  • Taherinezhad, A., & Alinezhad, A. (2022). COVID-19 Crisis Management: Global Appraisal using Two-Stage DEA and Ensemble Learning Algorithms. Scientia Iranica, (Article in press). Doi: https://doi.org/10.24200/sci.2022.58911.5962
  • Taherinezhad, A., & Alinezhad, A. (2023). Nations performance evaluation during SARS-CoV-2 outbreak handling via data envelopment analysis and machine learning methods. International Journal of Systems Science: Operations & Logistics, 10(1), 2022243. Doi: https://doi.org/10.1080/23302674.2021.2022243
  • Travers, D. A., Waller, A. E., Bowling, J. M., Flowers, D., & Tintinalli, J. (2002). Five-level triage system more effective than three-level in tertiary emergency department. Journal of emergency nursing, 28(5), 395-400. Doi: https://doi.org/10.1067/men.2002.127184
  • Wang, L., Zhang, Z. X., & Wang, Y. M. (2015). A prospect theory-based interval dynamic reference point method for emergency decision making. Expert Systems with Applications, 42(23), 9379-9388. Doi: https://doi.org/10.1016/j.eswa.2015.07.056
  • Wei, G. W. (2009). Some geometric aggregation functions and their application to dynamic multiple attribute decision making in the intuitionistic fuzzy setting. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 17(02), 179-196. Doi: https://doi.org/10.1142/S0218488509005802
  • Zadeh, L. A. (1983). Linguistic variables, approximate reasoning and dispositions. Medical Informatics, 8(3), 173-186. Doi: https://doi.org/10.3109/14639238309016081
There are 38 citations in total.

Details

Primary Language English
Subjects Industrial Engineering
Journal Section Research Articles
Authors

Ali Taherinezhad 0000-0003-2304-1983

Alireza Alinezhad This is me

Saber Gholami This is me

Publication Date July 29, 2024
Published in Issue Year 2024 Volume: 3 Issue: 1

Cite

APA Taherinezhad, A., Alinezhad, A., & Gholami, S. (2024). Fuzzy VIKOR Method for Dynamic MADM Problem Solution in ESI 5-Level Triage System. Journal of Optimization and Decision Making, 3(1), 398-419.
AMA Taherinezhad A, Alinezhad A, Gholami S. Fuzzy VIKOR Method for Dynamic MADM Problem Solution in ESI 5-Level Triage System. JODM. July 2024;3(1):398-419.
Chicago Taherinezhad, Ali, Alireza Alinezhad, and Saber Gholami. “Fuzzy VIKOR Method for Dynamic MADM Problem Solution in ESI 5-Level Triage System”. Journal of Optimization and Decision Making 3, no. 1 (July 2024): 398-419.
EndNote Taherinezhad A, Alinezhad A, Gholami S (July 1, 2024) Fuzzy VIKOR Method for Dynamic MADM Problem Solution in ESI 5-Level Triage System. Journal of Optimization and Decision Making 3 1 398–419.
IEEE A. Taherinezhad, A. Alinezhad, and S. Gholami, “Fuzzy VIKOR Method for Dynamic MADM Problem Solution in ESI 5-Level Triage System”, JODM, vol. 3, no. 1, pp. 398–419, 2024.
ISNAD Taherinezhad, Ali et al. “Fuzzy VIKOR Method for Dynamic MADM Problem Solution in ESI 5-Level Triage System”. Journal of Optimization and Decision Making 3/1 (July 2024), 398-419.
JAMA Taherinezhad A, Alinezhad A, Gholami S. Fuzzy VIKOR Method for Dynamic MADM Problem Solution in ESI 5-Level Triage System. JODM. 2024;3:398–419.
MLA Taherinezhad, Ali et al. “Fuzzy VIKOR Method for Dynamic MADM Problem Solution in ESI 5-Level Triage System”. Journal of Optimization and Decision Making, vol. 3, no. 1, 2024, pp. 398-19.
Vancouver Taherinezhad A, Alinezhad A, Gholami S. Fuzzy VIKOR Method for Dynamic MADM Problem Solution in ESI 5-Level Triage System. JODM. 2024;3(1):398-419.