Sensitivity Analysis by SIMUS: The IOSA Procedure
This chapter addresses a fundamental subject: sensitivity analysis. It is not only a checking procedure but also a tool that allows the DM to answer questions from stakeholders as well as a way to study different attitudes; without it a MCDM process is incomplete. The main reason for its use lies in the uncertainty of data, and therefore, a test must be performed to verify how a solution holds when certain parameters change.
In this book, sensitivity analysis is performed in a different and novel way, compared as it is done nowadays because there are nonsubjective assumptions, and it is based on what is believed to be a more rational approach, by using economics principles such as criteria marginal values instead of subjective criteria weights. The advantage over the lack of effectiveness and transparency of the actual procedure is that it permits not only determining when a ranking changes – as is the information given by all methods – but also getting a quantitative measure on the effect of these changes. It also provides a graphic display of how the objective value increases or decreases according to increments or decrements in some criteria.
Another advantage is that it allows analysis of how exogenous factors, related somehow with criteria, may affect the best selection in the future, during operation of the alternative selected. Due to this, it is possible to compute the value of diverse risks related to the potential changes of these exogenous factors.
- *Cox M (2015) Expected utility theory. Accessed: 23 Sept 2017Google Scholar
- *Dantzig G (1948) Linear programming and extensions. United States Air Force, Washington, DCGoogle Scholar
- *Ishizaka A, Nemery P (2013) Multicriteria decision software: methods and analysis. Wiley, ChichesterGoogle Scholar
- *Kantorovich L (1939) The best uses of economic resources. Pergamon Press (1965)Google Scholar
- *Kothari C (2009) An introduction to operational research, 3rd edn. Vikas Publishing House PVT LTD Noida, IndiaGoogle Scholar
- *Mareschal B, De Smet Y, Nemery N (2008) Rank reversal in Promethee II method. Some new results. In: International Conference on Industrial Engineering and Engineering Management (2008). p. 2008Google Scholar
- PMI (Project Management Institute) (2017) A guide to the project management body of knowledge. https://www.bing.com/search?q=project+management+institute+a+guide+to+the+project+management&qs=n&form=QBRE&sp=1&pq=project+management+institute+a+guide+to+the+project+management&sc=0-62&sk=&cvid=0AE19844C0524098AFDD4D4E88CE1C57Google Scholar
- *Shing Y, Lee L, Chun S, Chung D (2013) A critical view of multi-criteria decision-making methodologies. Issues Inf Syst 14(1):358–365Google Scholar
- *Solver – FrontLine Systems. http://www.solver.com/. Accessed 08 Dec 2017
- *Triantaphyllou E (2001) Two cases of rank reversal when the AHP and some of its additive variants are used that do not occur with the multiplicative AHP. J Multicrit Decis Anal 10(1):11–25. John Wiley and SonsGoogle Scholar
- *Verly C, De Smet Y (2013) Some considerations about rank reversal occurrences in the PROMETHEE methods. Accepted for publication in the Int J Multicrit Decis Mak 71, 3(4):325–345Google Scholar
- *Wan J-M, Luo Y (2009) On rank reversal in decision analysis. Math Comput Model 49(5–6):1221–1229Google Scholar
- *Zavadskas E, Antuvichevicene J, Saparauskas J, Turskis Z (2012) MCDM methods Waspas and Multimoora: verification of robustness of methods when assessing alternative solutions. http://www.ecocyb.ase.ro/20132/Zavadskas%20(T).pdf. Accessed 05 Dec 2015Google Scholar