Soft Computing

, Volume 23, Issue 4, pp 1309–1319 | Cite as

A fuzzy AHP-based methodology for project prioritization and selection

  • Amir Shaygan
  • Özlem Müge TestikEmail author
Methodologies and Application


To improve today’s complex systems composed of personnel, hardware, software and methods, it is necessary to identify, prioritize and select projects to eliminate the causes of underperformance. In this study, a methodology based on fuzzy analytical hierarchy process (FAHP) for decision-making, integrated with cause-and-effect diagrams used in quality improvement studies, is proposed for this purpose. It is assumed that the resources for improvement are scarce and the best use of these is needed. To guide practitioners, the methodology is illustrated with a real-world implementation for identification, prioritization and selection of improvement projects for a poor performing appointment system at a hospital. The cause-and-effect diagram is used to identify and organize the causes and their sub-causes leading to the poor performance, and to create a hierarchy of these by using the information obtained from experts, staff and patients. In order to prioritize the major and sub-causes as potential improvement project topics, FAHP methodology, which utilize human cognition and judgment power based on knowledge and experience, is applied and used for decision-making. The priorities corresponding to each major cause and sub-cause can be used to make a decision on improvement projects and their order due to scarce resources. The methodology is general and can be used in various application domains.


Project prioritization Project selection Fuzzy AHP Cause-and-effect diagram Process improvement Service quality Appointment system 



We would like to thank the reviewers and the associate editor for constructive comments, which helped improving the paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Abbod MF, Keyserlingk DG, Linkens DA, Mahfouf M (2001) Survey of utilisation of fuzzy technology in medicine and healthcare. Fuzzy Sets Syst 120(2):331–349MathSciNetCrossRefGoogle Scholar
  2. Bhattacharjee P, Ray P (2015) Simulation modelling and analysis of appointment system performance for multiple classes of patients in a hospital: a case study. Oper Res Health Care 8:71–84CrossRefGoogle Scholar
  3. Chang D-Y (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95(3):649–655CrossRefzbMATHGoogle Scholar
  4. Chang T-H (2014) Fuzzy VIKOR method: a case study of the hospital service evaluation in Taiwan. Inf Sci 271:196–212CrossRefGoogle Scholar
  5. Chauhan A, Singh A (2016) A hybrid multi-criteria decision making method approach for selecting a sustainable location of healthcare waste disposal facility. J Clean Prod 139:1001–1010CrossRefGoogle Scholar
  6. Deng H (1999) Multicriteria analysis with fuzzy pairwise comparisons. Int J Approx Reason 21:215–231CrossRefGoogle Scholar
  7. Dursun M, Karsak EE, Karadayi MA (2011) A fuzzy multi-criteria group decision making framework for evaluating health-care waste disposal alternatives. Expert Syst Appl 38(9):11453–11462CrossRefGoogle Scholar
  8. Flood A, Latino RJ (2004) Optimizing FMEA and RCA efforts in health care. J Healthcare Risk Manag 24(3):21–28CrossRefGoogle Scholar
  9. Harding KE, Bottrell J (2016) Specific timely appointments for triage reduced waiting lists in an outpatient physiotherapy service. Physiotherapy 102:345–350CrossRefGoogle Scholar
  10. Hasin M, Seeluangsawat R, Shareef M (2001) Statistical measures of customer satisfaction for health care quality assurance: a case study. Int J Health Care Qual Assur 14(1):6–14CrossRefGoogle Scholar
  11. Hatcher M (1994) Voting and priorities in health care decision making, portrayed through a group decision support system, using analytic hierarchy process. J Med Syst 18(5):267–288CrossRefGoogle Scholar
  12. Ishizaka A, Labib A (2009) Analytic hierarchy process and expert choice: benefits and limitations. OR Insight 22(4):201–220. doi: 10.1057/ori.2009.10 CrossRefGoogle Scholar
  13. Javid AA, Jalali Z, Klassen KJ (2016) Outpatient appointment systems in healthcare: a review of optimization studies. Eur J Oper Res 258:3–34. doi: 10.1016/j.ejor.2016.06.064 MathSciNetCrossRefzbMATHGoogle Scholar
  14. Kuo RJ, Wu YH, Hsu TS (2012) Integration of fuzzy set theory and TOPSIS into HFMEA to improve outpatient service for elderly patients in Taiwan. J Chin Med Assoc 75(7):341–348CrossRefGoogle Scholar
  15. Lee A, Chen WC, Chang CJ (2008) A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Syst Appl 34(1):96–107CrossRefGoogle Scholar
  16. Liberatore MJ, Nydick RL (2008) The analytic hierarchy process in medical and health care decision making: a literature review. Eur J Oper Res 189:194–207CrossRefzbMATHGoogle Scholar
  17. Low C, Chen YH (2012) Criteria for the evaluation of a cloud-based hospital information system outsourcing provider. J Med Syst 36(6):3543–3553CrossRefGoogle Scholar
  18. Mahfouf MM, Abbod MF, Linkens DA (2001) A survey of fuzzy logic monitoring and control utilisation in medicine. Artif Intell Med 21(1):27–42CrossRefGoogle Scholar
  19. Montgomery DC (2013) Statistical quality control: a modern introduction, 7th edn. Wiley, SingaporeGoogle Scholar
  20. Moreno JM (2010) A quality evaluation methodology for health-related websites based on a 2-tuple fuzzy linguistic approach. Soft Comput 14(8):887–897CrossRefGoogle Scholar
  21. Saaty TL (1980) The analytic hierarchy process, planning, priority setting, resource allocation. McGraw-Hill, New YorkzbMATHGoogle Scholar
  22. Singh A, Prasher A (2017) Measuring healthcare service quality from patients’ perspective: using Fuzzy AHP application. Total Qual Manag Bus Excell. doi: 10.1080/14783363.2017.1302794
  23. Testik ÖM, Shaygan A, Dasdemir E, Soydan G (2017) Selecting health care improvement projects: a methodology integrating cause-and-effect diagram and analytical hierarchy process. Qual Manag Health Care 26(1):40–48Google Scholar
  24. Tsai H-Y, Chang C-W, Lin H-L (2010) Fuzzy hierarchy sensitive with Delphi method to evaluate hospital organization performance. Expert Syst Appl 37:5535–5541Google Scholar
  25. Vahidnia MH, Alesheikh AA, Alimohammadi A (2009) Hospital site selection using fuzzy AHP and its derivatives. J Environ Manag 90(10):3048–3056CrossRefGoogle Scholar
  26. White AA (2004) Cause-and-effect analysis of risk management files to assess patient care in the emergency department. Acad Emerg Med 11(10):1035–1041CrossRefGoogle Scholar
  27. Wong K (2011) Using an Ishikawa diagram as a tool to assist memory and retrieval of relevant medical cases from the medical literature. J Med Case Rep 5(1):120CrossRefGoogle Scholar
  28. Wu WY, Hsiao SW, Kuo HP (2004) Fuzzy set theory based decision model for determining market position and developing strategy for hospital service quality. Total Qual Manag Bus Excell 15(4):439–456CrossRefGoogle Scholar
  29. Wu C-R, Chang C-W, Lin H-L (2008) FAHP sensitivity analysis for measurement nonprofit organizational performance. Qual Quant 42(3):283–302CrossRefGoogle Scholar
  30. Yu SC, Lin YH (2008) Applications of fuzzy theory on health care: an example of depression disorder classification based on FCM. WSEAS Trans Inf Sci Appl 5(1):31–36Google Scholar
  31. Zadeh L (1965) Fuzzy sets. Inf Control 8:338–353CrossRefzbMATHGoogle Scholar
  32. Zadeh LA (1968) Fuzzy algorithms. Inf Control 12(2):94–102MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Industrial Engineering, Faculty of EngineeringHacettepe UniversityAnkaraTurkey

Personalised recommendations