Uncertain Location of Network Structured Production Units
الموضوعات : Fuzzy Optimization and Modeling JournalAzam Azodi 1 , Jafar Fathali 2 , Mojtaba Ghiyasi 3 , Tahere Sayar 4
1 - Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
2 - Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
3 - Faculty of Industrial Engineering and Management Science, Shahrood University of Technology
4 - Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
الکلمات المفتاحية: Facility Location Problem, Network DEA, Healthcare, Health Centers, Triangular Fuzzy Numbers,
ملخص المقالة :
Facility location problems are one of the most important issues for healthcare organizations and centers to achieve social welfare and respond to customer needs. Proper distribution of health and treatment facilities in cities is vital to minimize costs and improve the efficiency of health centers. The main contribution of the current article is dealing with the uncertainty issue in the p-median location-efficient problem. In this article, the p-median location problem along with network data envelopment analysis (Network DEA) is used in parallel mode to calculate the efficiency of health and treatment centers. In this issue, health centers are considered as parallel networks with two departments that operate independently. Due to the precision of the input and output values, triangular fuzzy numbers and the α-level fuzzy method have been used. The primary results that consider the uncertainty provide efficient solution and suggestions for the potential location of health centers in our case study.
1. Amini, F., & Rezaeenour, J. (2016). Ranking healthcare centers using fuzzy analytic hierarchy process and TOPSIS: Iranian experience. International Journal of Operational Research, 6(1), 25-39.
2. Arya, A., & Yadav, S. P. (2018). Development of intuitionistic fuzzy super-efficiency slack based measure with an application to health sector. Computers & Industrial Engineering, 115, 368-380.
3. Azodi, A., Fathali, J., Ghiyasi, M., & Sayar, T. (2023). Fuzzy balanced allocation problem with efficiency on facilities. Soft Computing, 27(10), 6573-6586.
4. Castelli, L., Pesenti, R., & Ukovich, W. (2004). DEA-like models for the efficiency evaluation of hierarchically structured units. European journal of operational research, 154(2), 465-476.
5. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
6. Cinaroglu, S. (2023). Fuzzy efficiency estimates of the Turkish Health System: A comparison of interval, Bias-Corrected, and fuzzy data envelopment analysis. International Journal of Fuzzy Systems, 25(6), 2356-2379.
7. Ebrahimnejad, A. (2012). Cost efficiency measures with trapezoidal fuzzy numbers in data envelopment analysis based on ranking functions: application in insurance organization and hospital. International Journal of Fuzzy System Applications (IJFSA), 2(3), 51-68.
8. Ebrahimnejad, A., & Amani, N. (2021). Fuzzy data envelopment analysis in the presence of undesirable outputs with ideal points. Complex & intelligent systems, 7(1), 379-400.
9. Färe, R., Grosskopf, S., & Whittaker, G. (2007). Network dea. Modeling data irregularities and structural complexities in data envelopment analysis, 209-240.
10. Garcia-Aguado, C., & Verdegay, J. (1993). On the sensitivity of membership functions for fuzzy linear programming problems. Fuzzy sets and systems, 56(1), 47-49.
11. Gómez-Gallego, J. C., Gómez-Gallego, M., García-García, J. F., & Faura-Martinez, U. (2021). Evaluation of the efficiency of European health systems using fuzzy data envelopment analysis. Healthcare,
12. Hakimi, S. L. (1964). Optimum locations of switching centers and the absolute centers and medians of a graph. Operations research, 12(3), 450-459.
13. Kao, C. (2009a). Efficiency decomposition in network data envelopment analysis: A relational model. European journal of operational research, 192(3), 949-962.
14. Kao, C. (2009b). Efficiency measurement for parallel production systems. European journal of operational research, 196(3), 1107-1112.
15. Kao, C., & Liu, S.-T. (2000). Fuzzy efficiency measures in data envelopment analysis. Fuzzy sets and systems, 113(3), 427-437.
16. Khodagholi, M., Dolati, A., & Hoseinzadeh, A. (2018). The solving an inverse 1-median problem by using alpha-cut fuzzy. Journal of Decisions and Operations Research, 3(1), 58-71.
17. Mahdavi-Amiri, N., & Nasseri, S. (2006). Duality in fuzzy number linear programming by use of a certain linear ranking function. Applied mathematics and computation, 180(1), 206-216.
18. Maleki, H. (2002). Ranking functions and their applications to fuzzy linear programming. Far East Journal of Mathematical Sciences, 4(3), 283-302.
19. Nasseri, S. H., & Niksefat Dogori, P. (2022). A Three-Stage Process for Fuzzy Stochastic Network Data Envelopment Analysis Models. Iranian Journal of Operations Research, 13(1), 184-197.
20. ReVelle, C. S., & Swain, R. W. (1970). Central facilities location. Geographical analysis, 2(1), 30-42.
21. Rostamy-Malkhalifeh, M., Poudineh, E., & Payan, A. (2018). A fully fuzzy method of network data envelopment analysis for assessing revenue efficiency based on ranking functions. Control and Optimization in Applied Mathematics, 3(2), 77-96.
22. Tavassoli, M., & Saen, R. F. (2022). A new fuzzy network data envelopment analysis model for measuring efficiency and effectiveness: assessing the sustainability of railways. Applied Intelligence, 52(12), 13634-13658.
23. Yager, R. R. (1986). A characterization of the extension principle. Fuzzy sets and systems, 18(3), 205-217.
24. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
25. Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 1(1), 3-28.