Hospital Facility Location Selection Using Permanent Analytics Process
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Hospital Facility Location Selection Using Permanent Analytics Process

Authors: C. Ardil


In this paper, a new MCDMA approach, the permanent analytics process is proposed to assess the immovable valuation criteria and their significance in the placement of the healthcare facility. Five decision factors are considered for the value and selection of immovables. In the multiple factor selection problems, the priority vector of the criteria used to compare several immovables is first determined using the permanent analytics method, a mathematical model for the multiple criteria decisionmaking process. Then, to demonstrate the viability and efficacy of the suggested approach, twenty potential candidate locations were evaluated using the hospital site selection problem's decision criteria. The ranking accuracy of estimation was evaluated using composite programming, which took into account both the permanent analytics process and the weighted multiplicative model. 

Keywords: Hospital Facility Location Selection, Permanent Analytics Process, Multiple Criteria Decision Making (MCDM)

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[1] Ardil, C., Bilgen, S. (2017). Online Performance Tracking. SocioEconomic Challenges, 1(3), 58-72.
[2] 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.
[3] Mardani,A., Kazimieras Zavadskas,E., Khalifah, Z., Zakuan, N., Jusoh,A., Md Nor, K.,Khoshnoudi, M. (2017).A review of multi-criteria decision-making applications to solve energy management problems: Two decades from 1995 to 2015, Renewable and Sustainable Energy Reviews, 71: 216-256.
[4] Liang,G.-S., Wang.,M.-J J. (1991). A fuzzy multi-criteria decision-making method for facility site selection, International Journal of Production Research, 29 , 2313-2330.
[5] Chiadamrong, N., (1999). An integrated fuzzy multi-criteria decision making method for manufacturing strategies selection, Computers & Industrial Engineering, Volume 37, Issues 1–2, 433-436.
[6] Kahraman,C., Ruan, D., Doǧan, I.(2003).Fuzzy group decision-making for facility location selection, Information Sciences, Volume 157,135-153.
[7] Scholz, M., Franz, M. Hinz, O. (2017). Effects of decision space information on MAUT-based systems that support purchase decision processes, Decision Support Systems, Volume 97, 43-57.
[8] Tahri,M., Kaspar, J., Vacik, H., Marusak,R. (2021). Multi-attribute decision making and geographic information systems: potential tools for evaluating forest ecosystem services, Annals of Forest Science, 78, 41.
[9] Dyer, J. S., Fishburn, P. C., Steuer, R. E., Wallenius, J., Zionts, S. (1992). Multiple Criteria Decision Making, Multiattribute Utility Theory: The Next Ten Years, Management Science, Vol. 38, No. 5, 645-654.
[10] Saaty, T.L. (1977). A scaling method for priorities in hierarchical structures, Journal of Mathematical Psychology, 15, 234–281.
[11] Saaty,T.L., Vargas, L.G. (1984).Comparison of eigenvalue, logarithmic least squares and least squares methods in estimating ratios, Mathematical Modelling, 5, 309-324.
[12] Saaty, T. L.(1987). Rank Generation, Preservation, and Reversal in the Analytic Hierarchy Process, Decision Sciences, 18, 157-177.
[13] Saaty, T. L.(2008). Decision Making with the Analytic Hierarchy Process, International Journal of Services Sciences, Vol. 1, No. 1, pp. 83-98.
[14] Chen,C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets Systems, 114, 1–9.
[15] Chou,Y.-C., Yen, H.Y., Dang, V.T., Sun, C.-C. (2019). Assessing the Human Resource in Science and Technology for Asian Countries: Application of Fuzzy AHP and Fuzzy TOPSIS, Symmetry, 11, 251.
[16] Falqi, I.I., Ahmed, M., Mallick, J. (2019). Siliceous Concrete Materials Management for Sustainability Using Fuzzy-TOPSIS Approach, Appl. Sci, 9, 3457.
[17] 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.
[18] Wu, X., Liao,H., Zavadskas, E. K., Antuchevičienė,J. (2022). A probabilistic linguistic VIKOR method to solve MCDM problems with inconsistent criteria for different alternatives. Technological and Economic Development of Economy, 28(2), 559–580.
[19] Salimian, S., Mousavi,S. M., Antucheviciene, J. (2022). An Interval-Valued Intuitionistic Fuzzy Model Based on Extended VIKOR and MARCOS for Sustainable Supplier Selection in Organ Transplantation Networks for Healthcare Devices, Sustainability, vol. 14, no. 7, p. 3795.
[20] Fei, L., Xia, J., Feng, Y., Liu, L. (2019). An ELECTRE-Based Multiple Criteria Decision Making Method for Supplier Selection Using Dempster-Shafer Theory, IEEE Access, vol. 7, pp. 84701-84716.
[21] Rouyendegh,B.D., Erkan, T. E. (2013). An Application of the Fuzzy ELECTRE Method for Academic Staff Selection, Human Factors and Ergonomics in Manufacturing & Service Industries 23 (2), 107-115.
[22] asemi,M. Ahmadi,E. (2018). A New Fuzzy ELECTRE Based Multiple Criteria Method for Personnel Selection, Scientia Iranica, 25(2), 943-953.
[23] Abdullah, L., Chan, W., Afshari, A. (2019). Application of PROMETHEE method for green supplier selection: a comparative result based on preference functions,J Ind Eng Int 15, 271–285.
[24] Brans,J. P., Vincke, Ph. (1985). Note—A Preference Ranking Organisation Method (The PROMETHEE Method for Multiple Criteria Decision-Making), Management Science,Vol. 31, No. 6,647-656.
[25] Kabassi, K., Martinis, A. (2021). Sensitivity Analysis of PROMETHEE II for the Evaluation of Environmental Websites, Applied Sciences, 11 (19), 9215.
[26] Morfoulaki,M., Papathanasiou, J. (2021). Use of PROMETHEE MCDA Method for Ranking Alternative Measures of Sustainable Urban Mobility Planning, Mathematics, (6), 602.
[27] Wu,X., Liao, H. (2018). An approach to quality function deployment based on probabilistic linguistic term sets and ORESTE method for multi-expert multi-criteria decision making, Information Fusion, Volume 43,13-26.
[28] Bourguignon,B., Massart,D.L. (1994).The Oreste method for multicriteria decision making in experimental chemistry, Chemometrics and Intelligent Laboratory Systems, Volume 22, Issue 2, 241-256.
[29] Li, J., Chen, Q., Niu,L.L., Wang ,Z.-X. (2020). An ORESTE approach for multi-criteria decision-making with probabilistic hesitant fuzzy information, Int. J. Mach. Learn. & Cyber. 11, 1591–1609.
[30] Hodgett,R.E. (2016).Comparison of Multi-Criteria Decision-Making Methods for Equipment Selection, The International Journal of Advanced Manufacturing Technology, 85 (5-8). 1145-1157.
[31] Sotoudeh-Anvari,A. (2022).The applications of MCDM methods in COVID-19 pandemic: A state of the art review, Appl Soft Comput.,126, 109238.
[32] Ziemba,P., Becker,A., Becker, J. (2020). A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method, Symmetry, 12, 204.
[33] Barzilai, J. (1997). Deriving weights from pairwise comparison matrices, Journal of the Operational Research Society, 48, 1226–1232.
[34] Choo, E.U., Wedley, W.C. (2004). A common framework for deriving preference values from pairwise comparison matrices, Computers & Operations Research, 31, 893–908.
[35] Cook, W.D., Kress,M.K. (1988). Deriving weights from pairwise comparison ratio matrices: an axiomatic approach, European Journal of Operational Research, 37,355–362.
[36] Hovanov,N., Kolari,J., Sokolov, M. (2008).Deriving weights from general pairwise comparisons matrices. Mathematical Social Sciences, 55, 205-220.
[37] Jones, D.F., Mardle, S.J. (2004). A distance-metric methodology for the derivation of weights from a pairwise comparison matrix, Journal of the Operational Research Society, 55, 869-875.
[38] Kou, G., Lin, C. (2014). A cosine maximization method for the priority vector derivation in AHP, European Journal of Operational Research, Volume 235, Issue 1, 225-232.
[39] Si, S.L., You, X.-Y., Liu, H.-C., Zhang, P. (2018). DEMATEL Technique: A Systematic Review of the State-of-the-Art Literature on Methodologies and Applications, Mathematical Problems in Engineering, 1-33.
[40] Ortíz, M.A., Felizzola,H.A., Isaza, S.N. (2015). A contrast between DEMATEL-ANP and ANP methods for six sigma project selection: a case study in healthcare industry, BMC Med Inform Decis Mak 15 (Suppl 3), S3,.
[41] Chen, J.K. (2021). Improved DEMATEL-ISM integration approach for complex systems, PLoS One, 16(7).
[42] Tuljak-Suban,D., Bajec, P. (2020). Integration of AHP and GTMA to Make a Reliable Decision in Complex Decision-Making Problems: Application of the Logistics Provider Selection Problem as a Case Study, Symmetry, vol. 12, no. 5, p. 766.
[43] Sharma, H., Shanker, S., Barve,A., Muduli,K., Kumar, A., Luthra, S. (2022). Interval-valued intuitionistic fuzzy digraph-matrix approach with PERMAN algorithm for measuring COVID-19 impact on perishable food supply chain, Environ Dev Sustain., 1-40.
[44] Chang,C.-T., Zhao, W.-X., Hajiyev, J. (2019). An Integrated Smartphone and Tariff Plan Selection for Taxi Service Operators: MCDM and RStudio Approach, IEEE Access, 7, 31457-31472.
[45] Rao, R.V. (2007). Decision Making in the Manufacturing Environment: Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods; Springer: London, UK.
[46] Liu, H.-C.,You,J.-X., Zhen, L., Fan, X.-J.(2014). A novel hybrid multiple criteria decision making model for material selection with target-based criteria, Materials and Corrosion, vol. 60, 380–390.
[47] Yang,Y. P.Ou, Shieh,H. M., Leu,J. D., Tzeng, G. H.(2008). A novel hybrid MCDM model combined with DEMATEL and ANP with applications, International Journal of Operations Research, vol. 5, no. 3, 160–168.
[48] Kou,G., Ergu,D., Shang, J. (2014). Enhancing data consistency in decision matrix: adapting Hadamard model to mitigate judgment contradiction, European Journal of Operational Research, vol. 236, no. 1, 261–271.
[49] Karande,P., Zavadskas,E. K., Chakraborty,S. (2016). A study on the ranking performance of some MCDM methods for industrial robot selection problems, International Journal of Industrial Engineering Computations, 7, 399-422.