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A fuzzy AHP-based methodology for project prioritization and selection

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Abstract

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.

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Acknowledgements

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

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Hacettepe University, 06800, Ankara, Turkey

    Amir Shaygan & Özlem Müge Testik

Authors
  1. Amir Shaygan
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  2. Özlem Müge Testik
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Corresponding author

Correspondence to Özlem Müge Testik.

Ethics declarations

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.

Additional information

Communicated by V. Loia.

Appendix. Resulting fuzzy comparison matrices

Appendix. Resulting fuzzy comparison matrices

1. Capacity

 

1

2

1

1.00

1.00

1.00

1.25

1.75

2.25

2

0.44

0.57

0.80

1.00

1.00

1.00

1.1. Improving the appointment schedule

 

1

2

3

4

1

1.00

1.00

1.00

0.50

0.75

1.00

0.40

0.50

0.67

0.29

0.33

0.40

2

1.00

1.33

2.00

1.00

1.00

1.00

0.40

0.50

0.67

0.40

0.50

0.67

3

1.50

2.00

2.50

1.50

2.00

2.50

1.00

1.00

1.00

1.00

1.33

2.00

4

2.50

3.50

4.00

1.50

2.00

2.50

0.50

0.75

1.00

1.00

1.00

1.00

2. System and connection related

 

1

2

3

4

1

1.00

1.00

1.00

1.50

2.00

2.50

3.50

4.00

4.50

0.50

0.75

1.00

2

0.40

0.50

0.67

1.00

1.00

1.00

1.50

2.00

2.50

3.00

1.33

2.00

3

0.22

0.25

0.29

0.40

0.50

0.67

1.00

1.00

1.00

0.40

0.50

0.67

4

3.00

1.33

2.00

0.50

0.75

1.00

1.50

2.00

2.50

1.00

1.00

1.00

2.1. Improving the telecommunication system

 

1

2

3

4

1

1.00

1.00

1.00

0.40

0.50

0.67

0.50

0.75

1.00

1.50

2.00

2.50

2

1.50

2.00

2.50

1.00

1.00

1.00

1.50

2.00

2.50

1.50

2.00

2.50

3

1.00

1.33

2.00

0.40

0.50

0.67

1.00

1.00

1.00

0.50

0.75

1.00

4

0.40

0.50

0.67

0.40

0.50

0.67

1.00

1.33

2.00

1.00

1.00

1.00

2.2. Improving the IT system for internet appointments

 

1

2

3

1

1.00

1.00

1.00

1.50

2.00

2.50

1.00

1.00

1.00

2

0.40

0.50

0.67

1.00

1.00

1.00

0.40

0.50

0.67

3

1.00

1.00

1.00

1.50

2.00

2.50

1.00

1.00

1.00

3. Hardware and software related

 

1

2

3

1

1.00

1.00

1.00

0.50

0.75

1.00

1.50

2.00

2.50

2

1.00

1.33

2.00

1.00

1.00

1.00

0.67

1.00

1.50

3

0.40

0.50

0.67

0.67

1.00

1.50

1.00

1.00

1.00

3.1. Improving the software performance

 

1

2

1

1.00

1.00

1.00

0.50

0.67

1.00

2

1.00

1.50

2.00

1.00

1.00

1.00

3.2. Improving the capabilities of IT department

 

1

2

1

1.00

1.00

1.00

0.50

0.67

1.00

2

1.00

1.50

2.00

1.00

1.00

1.00

4. Staff related

 

1

2

1

1.00

1.00

1.00

3.50

4.00

4.50

2

0.22

0.25

0.29

1.00

1.00

1.00

4.1. Reducing inefficiencies caused by doctors

 

1

2

1

1.00

1.00

1.00

2.50

3.00

3.50

2

0.29

0.33

0.40

1.00

1.00

1.00

4.2. Reducing inefficiencies caused by secretaries

 

1

2

3

1

1.00

1.00

1.00

0.40

0.50

0.67

0.40

0.50

0.67

2

1.50

2.00

2.50

1.00

1.00

1.00

0.50

0.67

1.00

3

1.50

2.00

2.50

1.00

1.50

2.00

1.00

1.00

1.00

6. During appointments problems

 

1

2

3

1

1.00

1.00

1.00

0.67

1.00

1.50

0.29

0.33

0.40

2

0.67

1.00

1.50

1.00

1.00

1.00

0.22

0.25

0.57

3

2.50

3.00

3.50

3.50

4.00

4.50

1.00

1.00

1.00

6.1. Reducing excessive questions asked by patients

 

1

2

1

1.00

1.00

1.00

1.25

1.75

2.25

2

0.44

0.57

0.80

1.00

1.00

1.00

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Shaygan, A., Testik, Ö.M. A fuzzy AHP-based methodology for project prioritization and selection. Soft Comput 23, 1309–1319 (2019). https://doi.org/10.1007/s00500-017-2851-9

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