Bulanık Ortamda VIKOR Kullanarak Hastane Yeri Seçimi için Grup Karar Verme

Yıl 2018, , 435 - 450, 31.08.2018

Yakup Çelikbilek

https://doi.org/10.38079/igusabder.425439

Cited By: 5

Öz

Amaç: Bu çalışmanın amacı, bulanık
ortamda çok kriterli karar verme teknikleri kullanarak 10 kriter altında, bir
sağlık işletmesi için en iyi hastane yerinin seçilmesidir. Bu çalışmanın diğer
bir önemli amacı da, hastane işletmesinin Yönetim Kurulu üyelerinin
ağırlıklarının karar verme sırasında dâhil edilmesidir.

Yöntem:
Değerlendirmeler, özel bir sağlık işletmesi için en iyi hastane yerinin seçimi
üzerine gerçekleştirilmiştir. Bu amaç için; grup karar verme kullanarak Yönetim
Kurulu üyelerinin de yardımıyla bulanık VIKOR (VlseKriterijumska Optimizacija I
Kompromisno Resenje) yöntemi kullanılmıştır. Yönetim Kurulunun tüm üyeleri,
Yönetim Kurulundaki ağırlıklarına göre probleme dâhil edilmişlerdir. Bundan
dolayı, hastane yeri seçim kararını verme amacında olan bu çalışmanın sonuçları
daha objektif olmuştur.

Bulgular: Çalışmanın
sonuçları, yeni bir hastane için en iyi yeri göstermiştir. Ayrıca,
karşılaştırmalar için, hastane yerleri arasındaki farklar da bulanık
sonuçlardan açıkça görülebilir. Bu çalışmayı ve önerilen yöntemi doğrulamak
adına, diğer bir bulanık çok kriterli karar verme yöntemi de probleme
uygulanmıştır. Tüm sonuçların tutarlılığı, hastane yeri seçimi için önerilen
yöntemin uygulanabilirliğini, etkinliğini ve geçerliliğini göstermektedir.

Sonuç: Tüm
sonuçların birlikte değerlendirilmesi, önerilen yöntemin hastane yeri seçimi
için etkili olduğunu ve sağlık işletmelerinin diğer karar verme problemleri
için de uygulanabileceğini göstermektedir. 

Anahtar Kelimeler

Hastane yeri seçimi, bulanık VIKOR, grup karar verme

Group Decision Making for Hospital Location Selection Using VIKOR under Fuzzy Environment

Öz

Aim: The purpose of this study is to select the best
hospital location for a health institution under 10 criteria using multi
criteria decision making techniques under fuzzy environment. Another important
purpose of this study is to include weights of the members of the board of
directors of the institution while making decisions. 

Method:
Evaluations are done to select the
best hospital location for a private health institution. To this aim, fuzzy
VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method is used
with the help of members of the board of directors by using group decision
making. All members of the board of directors are included in the problem
according to the weights in the board of directors. Hence, the results of the study
to make the decision of hospital location selection are more objective.

Findings:
The results of this study
demonstrate the best location for a new hospital. In addition, differences
between the locations are clearly seen at the fuzzy results for comparisons. To
validate the results of this study and the proposed method, another fuzzy multi
criteria decision making method is applied to the problem. Consistency of all
results shows applicability, effectiveness and validity of the proposed method
for hospital location selection.

Conclusion:
Evaluations of all results show that
the proposed method is efficient for hospital location selection problem and
can also be applicable for other decision making problems of health
institutions. 

Anahtar Kelimeler

Hospital location selection, fuzzy VIKOR, group decision making

Kaynakça

• Weber A, Friedrich CJ. Theory of the Location of Industries. Chicago, Illinois: The University of Chicago Press; 1962.
• Singh RK. Facility location selection using extent fuzzy AHP. International Advanced Research Journal in Science, Engineering and Technology. 2016;3(2):47-51.
• Verter V, Dincer MC. An integrated evaluation of facility location, capacity acquisition, and technology selection for designing global manufacturing strategies. European Journal of Operational Research. 1992;60(1):1-18.
• Lin CT, Wu CR, Chen HC. Selecting the location of hospitals in Taiwan to ensure a competitive advantage via GRA. Journal of Grey System. 2006;18(3):263-274.
• Önüt S, Tuzkaya UR, Kemer B. An analytical network process approach to the choice of hospital location. Journal of Engineering and Natural Sciences. 2008;25(4):367-379.
• Lin HY, Liao CJ, Chang YH. Applying fuzzy simple additive weighting system to health examination institution location selection. In: 2010 IEEE 17th International Conference on Industrial Engineering and Engineering Management (IE&EM); 29-31 October, 2010; Xiamen, China. doi: 10.1109/ICIEEM.2010.5646533.
• Lin CT, Tsai MC. Location choice for direct foreign investment in new hospitals in China by using ANP and TOPSIS. Quality & Quantity. 2010;44(2):375-390.
• Shahbandarzadeh H, Ghorbanpour A. The applying ISM/FANP approach for appropriate location selection of health centers. Iranian Journal of Management Studies. 2011;4(2):5-28.
• Chatterjee D, Mukherjee B. Potential hospital location selection using AHP: a study in rural India. International Journal of Computer Applications. 2013;71(17):1-7.
• Chiu JE, Tsai HH. Applying analytic hierarchy process to select optimal expansion of hospital location: The case of a regional teaching hospital in Yunlin. In: 2013 10th International Conference on Service Systems and Service Management (ICSSSM); 17-19 July, 2013; Hong Kong, China.
• Şen H, Demiral MF. Hospital location selection with grey system theory. European Journal of Economics and Business Studies. 2016;5(1):66-79.
• Senvar O, Otay I, Bolturk E. Hospital site selection via hesitant fuzzy TOPSIS. IFAC-PapersOnLine. 2016;49(12):1140-1145.
• Sen H. Hospital location selection with ARAS-G. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics. 2017;1:359-365.
• Opricovic S. Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade. 1998;2(1):5-21.
• Amiri M, Ayazi A, Olfat L, Moradi JS. Group decision making process for supplier selection with VIKOR under fuzzy circumstance case study: an Iranian car parts supplier. International Bulletin of Business Administration. 2011;10(6):62-75.
• Farsi JY, Moradi JS, Jamali B. Which product would be chosen? A fuzzy VIKOR method for evaluation and selection of products in terms of customers' point of view; Case study: Iranian cell phone market. Decision Science Letters. 2012;1(1):23-32.
• Kuo M, Liang G. A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers. Applied Soft Computing. 2012;12(1):476-485.
• Su C, Tzeng G, Tseng H. Improving cloud computing service in fuzzy environment—combining fuzzy DANP and fuzzy VIKOR with a new hybrid FMCDM model. In: 2012 International Conference on Fuzzy Theory and it's Applications (iFUZZY); 16-18 November, 2012; Taichung, Taiwan.
• Mohaghar A, Fathi MR, Jafarzadeh AH. A supplier selection method using AR-DEA and fuzzy VIKOR. International Journal of Industrial Engineering: Theory, Applications and Practice. 2013;20(5-6):387-400.
• Liao H, Xu Z. A VIKOR-based method for hesitant fuzzy multi-criteria decision making. Fuzzy Optimization and Decision Making. 2013;12(4):373-392.
• Bashiri M, Mirzaei M, Randall M. Modeling fuzzy capacitated p-hub center problem and a genetic algorithm solution. Applied Mathematical Modelling. 2013;37(5):3513-3525.
• Kim Y, Chung E. Fuzzy VIKOR approach for assessing the vulnerability of the water supply to climate change and variability in South Korea. Applied Mathematical Modelling. 2013;37(22):9419-9430.
• Chang T. Fuzzy VIKOR method: A case study of the hospital service evaluation in Taiwan. Information Sciences. 2014;271:196-212.
• Afful-Dadzie E, Nabareseh S, Oplatková ZK. Fuzzy VIKOR approach: Evaluating quality of internet health information. In: 2014 Federated Conference on Computer Science and Information Systems (FedCSIS); 7-10 September, 2014; Warsaw, Poland.
• Kavitha C, Vijayalakshmi C. Design of fuzzy multiobjective linear program integrated with fuzzy VIKOR for facility location. Indian Journal of Science and Technology. 2014;7(1):25-34.
• Adhikary P, Roy PK, Mazumdar A. Maintenance contractor selection for small hydropower project: a fuzzy multi-criteria optimization technique approach. International Review of Mechanical Engineering. 2015;9(2):174-181.
• Arunachalam APS, Idapalapati S, Subbiah S. Multi-criteria decision making techniques for compliant polishing tool selection. The International Journal of Advanced Manufacturing Technology. 2015;79(1-4):519-530.
• Lee G, Jun KS, Chung ES. Group decision-making approach for flood vulnerability identification using the fuzzy VIKOR method. Natural Hazards and Earth System Science. 2015;15(4):863-874.
• Büyüközkan G, Göçer F, Smart medical device selection based on interval valued intuitionistic fuzzy VIKOR. In: Kacprzyk J, Szmidt E, Zadrożny S, Atanassov K, Krawczak M, (eds). Advances in Fuzzy Logic and Technology 2017. Poland: Springer, Cham; 2017:306-317.
• Zain ZM. Evaluation of the quality of internet breast cancer information: Fuzzy VIKOR approach. In: International Conference on Intelligent Human Systems Integration; 7-9 January, 2018; Dubai, United Arab Emirates.
• Fenton N, Wang W. Risk and confidence analysis for fuzzy multicriteria decision making. Knowledge-Based Systems. 2006;19(6):430-437.
• Büyüközkan G, Ruan D. Evaluation of software development projects using a fuzzy multi-criteria decision approach. Mathematics and Computers in Simulation. 2008;77(5):464-475.
• Ebrahimnejad S, Mousavi SM, Tavakkoli-Moghaddam R, Hashemi H, Vahdani B. A novel two-phase group decision making approach for construction project selection in a fuzzy environment. Applied Mathematical Modelling. 2012;36(9):4197-4217.
• Wu W, Lee YT. Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications. 2007;32(2):499-507.
• Opricovic S, Tzeng GH. Defuzzification within a multicriteria decision model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2003;11(5):635-652.
• Çelikbilek Y, Adıgüzel Tüylü AN, Esnaf Ş. Industrial coffee machine selection with the Fuzzy analytic hierarchy process. International Journal of Management and Applied Science. 2016;2(2):20-23.