Skip to main content

A Combined Method for Deriving Decision Makers’ Weights in Group Decision Making Environment: An Application in Medical Decision Making

  • Conference paper
  • First Online:
Industrial Engineering in the Big Data Era

Abstract

The complexity of the problem grows as multiple individuals involved in the decision making process. Since each individual may have a different experience, attitudes, and knowledge, their approaches might be different from each other on the same problem. Therefore, more comprehensive techniques are needed in group decision making methods in order to determine how much a decision maker’s contribution is considered in the final solution (i.e., the weight of each decision maker). The purpose of this study is to determine the combined weights of decision makers based on both the objective weights, using the geometric cardinal consensus index, and the subjective weights provided by a supervisor. In order to represent the implementation of the method, the study includes a case study in a medical decision making. There are several anesthesia method alternatives to apply; specifically, general anesthesia, local anesthesia, and sedation, which are considered by surgeons. In the case study, the combined relative weights of the medical doctors are derived regarding this issue.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aguaron, J., & Moreno-Jiménez, J. M. (2003). The geometric consistency index: Approximated thresholds. European Journal of Operational Research, 147(1), 137–145.

    Article  MATH  Google Scholar 

  • Barzilai, J., & Golany, B. (1994). AHP rank reversal, normalization and aggregation rules. INFOR: Information Systems and Operational Research, 32(2), 57–64.

    MATH  Google Scholar 

  • Blagojevic, B., Srdjevic, B., Srdjevic, Z., & Zoranovic, T. (2016). Deriving weights of the decision makers using AHP group consistency measures. Fundamenta Informaticae, 144(3–4), 383–395.

    Article  MathSciNet  MATH  Google Scholar 

  • Cabrerizo, F. J., Herrera-Viedma, E., & Pedrycz, W. (2013). A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts. European Journal of Operational Research, 230(3), 624–633.

    Article  MathSciNet  MATH  Google Scholar 

  • Crawford, G., & Williams, C. (1985). A note on the analysis of subjective judgment matrices. Journal of Mathematical Psychology, 29(4), 387–405.

    Article  MATH  Google Scholar 

  • Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2017). Group heterogeneity in multi member decision making model with an application to warehouse location selection in a supply chain. Computers & Industrial Engineering, 105, 101–122.

    Article  Google Scholar 

  • Dolan, J. G., Isselhardt, B. J., & Cappuccio, J. D. (1989). The analytic hierarchy process in medical decision making: A tutorial. Medical Decision Making, 9(1), 40–50.

    Article  Google Scholar 

  • Dolan, J. C., Bordley, D. R., & Miller, H. (1993). Diagnostic strategies in the management of acute upper gastrointestinal bleeding. Journal of General Internal Medicine, 8(10), 525–529.

    Article  Google Scholar 

  • Dong, Y., Zhang, G., Hong, W. C., & Xu, Yinfeng. (2010). Consensus models for AHP group decision making under row geometric mean prioritization method. Decision Support Systems, 49(3), 281–289.

    Article  Google Scholar 

  • Dragincic, J., Korac, N., & Blagojevic, B. (2015). Group multi-criteria decision making (GMCDM) approach for selecting the most suitable table grape variety intended for organic viticulture. Computers and Electronics in Agriculture, 111, 194–202.

    Article  Google Scholar 

  • Hancerliogullari, G., Hancerliogullari, K. O., & Koksalmis, E. (2017). The use of multi-criteria decision making models in evaluating anesthesia method options in circumcision surgery. BMC Medical Informatics and Decision Making, 17(1), 14.

    Article  Google Scholar 

  • Kabak, O., & Ervural, B. (2017). Multiple attribute group decision making: A generic conceptual framework and a classification scheme. Knowledge-Based Systems, 123, 13–30.

    Article  Google Scholar 

  • Liberatore, M. J., & Nydick, R. L. (2008). The analytic hierarchy process in medical and health care decision making: A literature review. European Journal of Operational Research, 189(1), 194–207.

    Article  MATH  Google Scholar 

  • Liu, B., Shen, Y., Chen, Y., Chen, X., & Wang, Y. (2015). A two-layer weight determination method for complex multi-attribute large-group decision-making experts in a linguistic environment. Information Fusion, 23, 156–165.

    Article  Google Scholar 

  • Lu, J., & Ruan, D. (2007). Multi-objective group decision making: Methods, software and applications with fuzzy set techniques (Vol. 6). Imperial College Press.

    Google Scholar 

  • Mianabadi, H., & Afshar, A. (2008). A new method to evaluate weights of decision makers and its application in water resource management. In 13th IWRA World Water Congress, Montpellier, France.

    Google Scholar 

  • Ölçer, A. I., & Odabaşi, A. Y. (2005). A new fuzzy multiple attributive group decision making methodology and its application to propulsion/manoeuvring system selection problem. European Journal of Operational Research, 166(1), 93–114.

    Article  MATH  Google Scholar 

  • Pang, J., Liang, J., & Song, P. (2017). An adaptive consensus method for multi-attribute group decision making under uncertain linguistic environment. Applied Soft Computing, 58, 339–353.

    Article  Google Scholar 

  • Ramanathan, R., & Ganesh, L. S. (1994). Group preference aggregation methods employed in AHP: An evaluation and an intrinsic process for deriving members’ weightages. European Journal of Operational Research, 79(2), 249–265.

    Article  MATH  Google Scholar 

  • Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resources allocation (p. 281). McGraw: New York.

    Google Scholar 

  • Srdjevic, B., Pipan, M., Srdjevic, Z., Blagojevic, B., & Zoranovic, T. (2015). Virtually combining the analytical hierarchy process and voting methods in order to make group decisions. Universal Access in the Information Society, 14(2), 231–245.

    Article  Google Scholar 

  • Stang, H. J., Gunnar, M. R., Snellman, L., Condon, L. M., & Kestenbaum, R. (1988). Local anesthesia for neonatal circumcision. JAMA, 259, 1507–1511.

    Article  Google Scholar 

  • Wang, Baoli, Liang, Jiye, & Qian, Yuhua. (2015). Determining decision makers’ weights in group ranking: A granular computing method. International Journal of Machine Learning and Cybernetics, 6(3), 511–521.

    Article  Google Scholar 

  • Yoon, K. P., & Hwang, C. L. (1995). Multiple attribute decision making: An introduction (Vol. 104). Sage publications.

    Google Scholar 

  • Yue, Z. (2011). Deriving decision maker’s weights based on distance measure for interval-valued intuitionistic fuzzy group decision making. Expert Systems with Applications, 38(9), 11665–11670.

    Article  Google Scholar 

  • Zhang, X., & Xu, Z. (2014). Deriving experts’ weights based on consistency maximization in intuitionistic fuzzy group decision making. Journal of Intelligent & Fuzzy Systems, 27(1), 221–233.

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Emrah Koksalmis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Koksalmis, E., Hancerliogullari Koksalmis, G., Kabak, O. (2019). A Combined Method for Deriving Decision Makers’ Weights in Group Decision Making Environment: An Application in Medical Decision Making. In: Calisir, F., Cevikcan, E., Camgoz Akdag, H. (eds) Industrial Engineering in the Big Data Era. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-03317-0_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03317-0_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03316-3

  • Online ISBN: 978-3-030-03317-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics