Abstract
The sustainable development of cloud service providers (CSPs) is a significant multiple criteria decision making (MCDM) problem, involving the intrinsic relations among multiple alternatives, (quantitative and qualitative) decision criteria and decision-experts for the selection of trustworthy CSPs. Most existing MCDM methods for CSP selection incorporated only one normalization technique in benefit and cost criteria, which would mislead the decision results and limit the applications of these methods. In addition, these methods did not consider the reliability of information given by decision-makers. Given these research gaps, this study introduces a Z-number-based double normalization-based multiple aggregation (DNMA) method to tackle quantitative and qualitative criteria in forms of benefit, cost, and target types for sustainable CSP development. We extend the original DNMA method to the Z-number environment to handle the uncertain and unreliability information of decision-makers. To make trade-offs between normalized criteria values, we develop a Gini-coefficient based weighting method to replace the mean-square-based weighting method used in the original DNMA method to enhance the applicability and isotonicity of the DNMA method. A case study is conducted to demonstrate the effectiveness of the proposed method. Furthermore, comparative analysis and sensitivity analysis are implemented to test the stability and applicability of the proposed method.
Highlights
Today’s organizations, regardless of their size and business scope, pay more and more attention to the maintenance of competitiveness and the establishment of a sustainable environment [1]
To enhance the applicability and isotonicity and the double normalization-based multiple aggregation (DNMA) method, we make use of the Gini-coefficient-based weighting method to replace the mean-square-based weighting method used in the original DNMA method, and extend this approach to the Z-number environment for the trade-offs between criteria after normalization
It has been combined with many multiple criteria decision making (MCDM) methods such as TOPSIS [36,37], VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) [38], Multi-Objective Optimization by Ratio Analysis (MOORA) [39], COmbinative Distance-based Assessment (CODAS) [40], PROMETHEE [41], TODIM [37], Analytic Hierarchical Process (AHP) [42], Best Worst Method (BWM) [43] and Data Envelopment Analysis (DEA) [44]
Summary
Today’s organizations, regardless of their size and business scope, pay more and more attention to the maintenance of competitiveness and the establishment of a sustainable environment [1]. A recently proposed MCDM method, the double normalization-based multiple aggregation (DNMA) method, [25] takes advantages of two normalization techniques and three aggregation functions to tackle quantitative and qualitative criteria in the forms of benefit, cost, and target types It can flexibly and reliably solve MCDM problems compared with the TOPSIS, VIKOR and MULTIMOORA methods [26]. 2. We extend the original DNMA method to the Z-number environment and propose the Z-DNMA method to tackle quantitative and qualitative criteria in forms of benefit, cost, and target types for CSP selection. We extend the original DNMA method to the Z-number environment and propose the Z-DNMA method to tackle quantitative and qualitative criteria in forms of benefit, cost, and target types for CSP selection In this regard, the uncertain and unreliability decision information of decision-makers (DMs) is considered in the process of CSP selection.
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