Development and implementation of a computational tool for the multi-criteria decision analysis method proppaga: case study of the sorting of hospital assistance ships of the brazilian navy to face the COVID-19 pandemic / Desenvolvimento e implementação de uma ferramenta computacional para o método multicritério de análise de decisão proppaga: estudo de caso da classificação dos navios de assistência hospitalar da marinha brasileira para enfrentar a pandemia de COVID-19
DOI:
https://doi.org/10.34117/bjdv7n10-211Keywords:
MCDA, COVID-19, Computational tool, Brazilian Navy, PrOPPAGA.Abstract
The multi-criteria decision analysis (MCDA) methods seek to support the decision-maker in choosing the most preferable alternative among the various possible ones, considering the criteria that characterize this preference. However, this task can become very complex, depending on the method used. This is because the algorithms of the methods are not always of a simple application. In this way, it is indispensable to develop computational tools that apply the algorithms of MCDA methods, making it feasible to use them. In this context, the computational tool presented in this article was developed. It uses the PrOPPAGA method in the selection problem of the Brazilian Navy's Hospital Assistance Ship Class of (HAS) to cope with the COVID-19 pandemic. The main contribution of this article is to present a tutorial for using this tool, to make its use feasible by society.
References
Albuquerque, J. P., Fortes, J.M., and Finamore, W. (2008) Probability, Random Variables and Stochastic Processes. Interscience.
Bana and Costa, C., and Vincke, P. (1990) Multiple Criteria Decision Aid: An Overview. Readings in Multiple Criteria Decision Aid. Springer Berlin Heidelberg.
Barrager, S.M. (2016) A new engineering profession is emerging: decision coach. IEEE Engineering Management Review, 44(2), 33–40. Presented in IEEE Engineering Management Review. doi:10.1109/EMR.2016.2568765
Chen, L., and Pan, W. (2021) Review fuzzy multi-criteria decision-making in construction management using a network approach. Applied Soft Computing, 102, 107103. doi:10.1016/j.asoc.2021.107103
Cinelli, M., Kadzinski, M., Gonzalez, M., and Slowinski, R. (2020) How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy. Omega, 96,102261. Doi:10.1016/j.omega.2020.102261
Costa, I. P. de A., Maêda, S.M. do N., Teixeira, L. F. H. de S. de B., Gomes, C. F. S., and Santos, M. D. (2020) Choosing a hospital assistance ship to fight the covid-19 pandemic. Public Health Magazine, 54, 79. doi:10.11606/s1518-8787.2020054002792
Dong, J. (2021) Fuzzy best-worst method based on triangular fuzzy numbers for multi-criteria decision-making. Information Sciences, 25.
Ji, Y., Xu, Y., Qu, S., Xu, Z., Wu, Z., and Nabe, M. (2021) A Novel Two-Stage Multi-Criteria Decision-Making Based on Interval-Valued Pythagorean Aggregation Fuzzy Operators with Self-Confidence Levels. Arabian Journal for Science and Engineering, 46(2), 1561–1584. doi:10.1007/s13369-020-04681-6
Karagöz, S., Deveci, M., Simic, V., and Aydin, N. (2021) Interval type-2 Fuzzy ARAS method for recycling facility location problems. Applied Soft Computing, 102, 107107. doi:10.1016/j.asoc.2021.107107
Kodikara, P. N. (2008) Multi-Objective Optimal Operation of Urban Water Supply Systems. Victoria University, Australia.
Liu, P., Zhu, B., and Wang, P. (2021) A weighting model based on best–worst method and its application for environmental performance evaluation. Applied Soft Computing, 103, 107168. doi:10.1016/j.asoc.2021.107168
Öztürk, M., Tsoukiàs, A., and Vincke, P. (2005) MODELLING PREFERENCE. MULTIPLE CRITERIA DECISION ANALYSIS, 45.
Raiffa, H. (2002) Decision Analysis: A Personal Account of How It Got Started and Evolved. Operations Research, 50(1), 179–185. doi:10.1287/opre.50.1.179.17797
Rezaei, J. (2015) Best-worst multi-criteria decision-making method. Omega, 53, 49–57. doi:10.1016/j.omega.2014.11.009
Rudnik, K. (2021) Ordered fuzzy WASPAS method for selection of improvement projects. Expert Systems With Applications, 18.
Saaty, T. L. (1980) The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill International Book Company. Obtained from https://books.google.com.br/books?id=Xxi7AAAAIAAJ
Song, Y., Thatcher, D., Li, Q., McHugh, T., and Wu, P. (2021) Developing sustainable road infrastructure performance indicators using a model-driven fuzzy spatial multi-criteria decision making method. Renewable and Sustainable Energy Reviews, 138, 110538. doi:10.1016/j.rser.2020.110538
Tang, C., Xu, D., and Chen, N. (2021) Sustainability prioritization of sewage sludge to energy scenarios with hybrid-data consideration: a fuzzy decision-making framework based on full consistency method and fusion ranking model. Environmental Science and Pollution Research, 28(5), 5548–5565. doi:10.1007/s11356-020-10544-2
Tian, G., Hao, N., Zhou, M., Pedrycz, W., Zhang, C., Ma, F., e Li, Z. (2020) Fuzzy Grey Choquet Integral for Evaluation of Multicriteria Decision Making Problems With Interactive and Qualitative Indices. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1–14. doi:10.1109/TSMC.2019.2906635
Zarei, E., Ramavandi, B., Darabi, A. H., and Omidvar, M. (2021) A framework for resilience assessment in process systems using a fuzzy hybrid MCDM model. Journal of Loss Prevention in the Process Industries, 69, 104375. doi:10.1016/j.jlp.2020.104375