Evaluation and Selection of Most Preferable Supplementary Blood Centers in The Case of Tehran

Document Type: Research Paper

Authors

School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

Background: The efficiency of health system services is a critical measure for societies development. During the last fifty years, the world has witnessed a massive increase in health expenditure, and health-related cost, especially in developing countries, is the main obstacle in the way of advance in health care systems. As a remarkable portion of this cost belongs to blood supply chains, almost any improvement in performance is considered as a critical part of health systems, which contributes to modifying cost-savings and responsiveness policies. 
Method: In this paper, a novel multi-criteria decision-making technique is conceptually proposed and presented to location supplementary blood centers so as to prevent disruption to a large extent. In this respect, Grey theory and TOPSIS, a distance-based multiple criteria method, are employed to integrate and evaluate the alternative performance for selecting supplementary blood centers. From a research perspective, TOPSIS method is improved to more effectively tackle grey numbers by presenting a degree of likelihood instead of converting grey numbers into crisp numbers functions, that provides the more flexible ranking procedure.
Results: The real data from Tehran blood transfusion center is applied to validate the method and provide insight into its operational execution, obtained results and validity. Overall, this paper found the proposed hybridized methodology to provide relatively consistent results of top-performing alternatives comparing with the more complicated and less intuitively appealing grey-rough set theory approach. 
Conclusion: The proposed hybrid methodology is a useful tool for managers, as well as researchers, who seek to evaluate alternative performance in various studies related to multi-criteria decision making. The technique can also be applied in a regular spreadsheet situation, can take into consideration a variety of metrics, both tangible and intangible, and can be devised with a minimal outside effort from decision-makers and be based completely on archival data if necessary.

Keywords

References

  1. World Health Organization, & Research for International Tobacco Control. WHO report on the global tobacco epidemic, the MPOWER package. World Health Organization. 2008.
  2. Hosseini-Motlagh SM, Cheraghi S, Ghatreh Samani M. A robust optimization model for blood supply chain network design. International Journal of Industrial Engineering & Production Research. 2016;27(4): 425-444.
  3. Fahimnia B, Jabbarzadeh A, Ghavamifar A, Bell M. Supply chain design for efficient and effective blood supply in disasters. International Journal of Production Economics. 2017; 183: 700-709.
  4. Dillon M, Oliveira F, Abbasi B. A two-stage stochastic programming model for inventory management in the blood supply chain. International Journal of Production Economics. 2017; 187: 27-41.
  5. Ensafian H, Yaghoubi S, Yazdi MM. Raising quality and safety of platelet transfusion services in a patient-based integrated supply chain under uncertainty. Computers & Chemical Engineering. 2017; 106: 355-372.
  6. Cheraghi S, Hosseini-Motlagh, SM. Responsive and reliable injured-oriented blood supply chain for disaster relief: a real case study. Annals of Operations Research. 2018: 1-39.
  7. Samani MRG, Hosseini-Motlagh SM. An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research. 2018: 1-50.
  8. Hamdan  B, Diabat A. A two-stage multi-echelon stochastic blood supply chain problem. Computers & Operations Research. 2019: 101: 130-143.
  9. Ramezanian R, Behboodi Z. Blood supply chain network design under uncertainties in supply and demand considering social aspects. Transportation Research Part E: Logistics and Transportation Review. 2017; 104: 69-82.
  10. Zahiri B, Pishvaee MS. Blood supply chain network design considering blood group compatibility under uncertainty. International Journal of Production Research. 2017; 55(7): 2013-2033.
  11. Samani MRG, Torabi SA, Hosseini-Motlagh SM. Integrated blood supply chain planning for disaster relief. International journal of disaster risk reduction. 2018; 27: 168-188.
  12. Ho W, Xu X, Dey PK. Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of operational research. 2010; 202(1): 16-24.
  13. Junior FRL, Osiro L, Carpinetti LCR. A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied Soft Computing. 2014; 21: 194-209.
  14. Bai C, Sarkis J. Supply-chain performance-measurement system management using neighbourhood rough sets. International Journal of Production Research. 2012; 50(9): 2484-2500.
  15. Deng JL. Control problems of grey systems. Sys. & Contr. Lett.. 1982; 1(5): 288-294.
  16. Deng J. Introduction to grey system theory. Journal of Grey system. 1989; 1(1): 1-24.
  17. Lin YH, Lee PC, Ting HI. Dynamic multi-attribute decision making model with grey number evaluations. Expert Systems with Applications. 2008; 35(4): 1638-1644.
  18. Noorul Haq A, Kannan G. An integrated approach for selecting a vendor using grey relational analysis. International Journal of Information Technology & Decision Making. 2006; 5(02): 277-295.
  19. Kuo Y, Yang T, Huang GW. The use of grey relational analysis in solving multiple attribute decision-making problems. Computers & industrial engineering. 2008; 55(1): 80-93.
  20. Tseng ML. A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach. Expert systems with applications. 2009; 36(4): 7738-7748.
  21. Zavadskas EK, Turskis Z, Tamošaitiene J. Risk assessment of construction projects. Journal of civil engineering and management. 2010; 16(1): 33-46.
  22. Zavadskas EK, Vilutiene T, Turskis Z, Tamosaitiene J. Contractor selection for construction works by applying SAW‐G and TOPSIS grey techniques. Journal of Business Economics and Management. 2010; 11(1): 34-55.
  23. Torkzad A, Beheshtinia, MA. Evaluating and prioritizing hospital service quality. International journal of health care quality assurance. 2019; 32(2): 332-346.
  24. Sedady F, Beheshtinia MA. A novel MCDM model for prioritizing the renewable power plants’ construction. Management of Environmental Quality: An International Journal. 2019; 30(2): 383-399.
  25. Beheshtinia MA, Omidi S. A hybrid MCDM approach for performance evaluation in the banking industry. Kybernetes. 2017; 46(8): 1386-1407.
  26. Beheshtinia MA, Nemati-Abozar V. A novel hybrid fuzzy multi-criteria decision-making model for supplier selection problem (A case study in advertising industry). Journal of Industrial and Systems Engineering. 2017; 9(4): 65-79.
  27. Beheshtinia MA, Ahangareian Abhari M. A New Hybrid Decision Making Method for Selecting Roller Concrete Road Pavement Technology Transfer Method. International Journal of Transportation Engineering. 2018; 5(3): 229-242.
  28. Bai C, Sarkis J. Integrating sustainability into supplier selection: a grey-based TOPSIS analysis. Technological and Economic Development of Economy. 2018; 24(6): 2202-2224.
  29. Oztaysi B. A decision model for information technology selection using AHP integrated TOPSIS-Grey: The case of content management systems. Knowledge-Based Systems. 2014; 70: 44-54.
  30. Peykani P, Mohammadi E, Rostamy-Malkhalifeh M, Hosseinzadeh Lotfi F. Fuzzy Data Envelopment Analysis Approach for Ranking of Stocks with an Application to Tehran Stock Exchange. Advances in Mathematical Finance and Applications. 2019; 4(1): 31-43.
  31. Peykani P, Mohammadi E, Emrouznejad A, Pishvaee MS, Rostamy-Malkhalifeh M. Fuzzy Data Envelopment Analysis: An Adjustable Approach. Expert Systems with Applications. 2019.
  32. Junior FRL, Osiro L, Carpinetti LCR. A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied Soft Computing. 2014; 21: 194-209.
  33. Samani MRG, Hosseini-Motlagh SM, Ghannadpour SF. A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation. Computers & Industrial Engineering. 2019; 130: 450-471.
  34. Hosseini-Motlagh SM, Samani MRG, Homaei S. Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). Journal of Ambient Intelligence and Humanized Computing. 2019: 1-20.
  35. Hosseini-Motlagh SM, Cheraghi S, Ghatreh Samani M. A robust optimization model for blood supply chain network design. International Journal of Industrial Engineering & Production Research. 2016; 27(4): 425-444.
  36. Ghatreh Samani M, Hosseini-Motlagh SM. A hybrid algorithm for a two-echelon location-routing problem with simultaneous pickup and delivery under fuzzy demand. International Journal of Transportation Engineering. 2017; 5(1): 59-85.
  37. Ensafian H, Yaghoubi S. Robust optimization model for integrated procurement, production and distribution in platelet supply chain. Transportation Research Part E: Logistics and Transportation Review. 2017; 103: 32-55.
  38. Cheraghi S, Hosseini-Motlagh SM, Ghatreh Samani M. Integrated planning for blood platelet production: a robust optimization approach. Journal of Industrial and Systems Engineering. 2017; 10(special issue on healthcare): 55-80.
  39. Hosseini-Motlagh SM, Samani MRG, Cheraghi S. Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences. 2019.
  40. Khalilpourazari S, Khamseh AA. Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: a comprehensive study with real world application. Annals of Operations Research. 2017: 1-39.
International Journal of Hospital Research

Volume 7, Issue 2
Spring 2018
Pages 81-101

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