Stochastic KEMIRA-M Approach with Consistent Weightings

Pelin Toktaş () and Gülin Feryal Can ()
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Pelin Toktaş: Industrial Engineering Department, Başkent University, Baǧlica Kampüsü Fatih Sultan Mahallesi, Eskişehir Yolu 18.km, 06970 Ankara, Turkey
Gülin Feryal Can: Industrial Engineering Department, Başkent University, Baǧlica Kampüsü Fatih Sultan Mahallesi, Eskişehir Yolu 18.km, 06970 Ankara, Turkey

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 03, 793-831

Abstract: This study proposes an advanced Modified KEmeny Median Indicator Rank Accordance (KEMIRA-M) approach based on stochastic evaluation process considering consistent weights to improve effective usage of KEMIRA-M. In the proposed approach, tasks related to the decision issue are performed by decision makers to ensure the understanding sufficiency of alternatives in terms of criteria more clearly. The weighting procedure of Analytic Hierarchy Process (AHP) is implemented in a stochastic manner benefited from discrete uniform distribution to provide obtaining consistent criteria weights considering median priority components. Therefore, different trials including different number of replications that shows the number of decision makers are performed and the most consistent weightings are determined for each trial in the stochastic process. In this way, the dependency to the limited numbers of decision makers and to determine criteria weights in a heuristic manner in KEMIRA-M is prevented. Additionally, the effect of the number of decision makers on criteria weightings and alternatives’ ranking process is shown. To obtain the most consistent weighting results, this stochastic process is utilized until acquiring approximate consistency ratios. The proposed stochastic KEMIRA-M approach is utilized to rank nine shopping malls (SMs) in Ankara in terms of technical criteria (TC) and universal design criteria (UDC). It was seen from the ranking results that the first SM (SM1) is the best one.

Keywords: KEMIRA-M; AHP; task implementation; MCDM; stochastic approach; criteria weights (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0219622019500123

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