Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

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.Sustainability 2020, 12, 3410 2 of 17 future generations to meet their own needs" [2]. Sustainability requires sustainable business practices. For doing so, information systems play an important role in the organization transition towards sustainability initiatives. The sustainable information systems have been considered as an opportunity for organizations to improve productivity, reduce costs, and increase profitability [3]. The flexible, elastic and agile nature of cloud computing provides an opportunity for governmental, academic and business organizations to consider migrating their existing applications to cloud environments to make their business processes agile with minimal cost and management effort [4]. Due to the increase in the number of cloud service providers (CSPs) offering functionally similar cloud services with varying cost, features and quality, it becomes extremely complex and burdensome to select a dependable, scalable, sustainable and high cost-effective cloud service for consumers to fulfil and satisfy their requirements and business strategies. There is a pressing need of comprehensive techniques to help them for the sustainable CSP development [5,6].Sustainable CSP development can be formulated as a multicriteria decision-making (MCDM) problem [5]. It involves the intrinsic relations among multiple alternatives, (quantitative and qualitative) decision criteria and experts for selecting the trustworthy CSPs. Moreover, the CSP evaluation data often involves uncertain, unreliability, multi-scale and imprecise weights of quality of service (QoS)...

Multiple Criteria Decision Making for the Achievement of the UN Sustainable Development Goals: A Systematic Literature Review and a Research Agenda

“…A summary of the reviewed papers related to SDG 9 issues [79][80][81][82][83][84][85][86][87][88][89][90][91][92][93] is presented in Table 8. In most articles concerning SDG 9, MCDM approaches were applied to evaluate the industry's sustainability performance in national contexts to overcome barriers to achieve this SDG [80,81,84,85,87,89].…”

“…Their evaluation showed that the proposed mixed strategy portfolio for green aviation fleet management could be determined using limited resources. Some authors concentrated their efforts on selecting or ranking strategies to make the infrastructure sector more sustainable [81,87,[89][90][91][92][93][94].…”

“…Aligned with the MCDM taxonomy chosen for this review, eight studies employed compromise methods, namely VIKOR, CODAS, TOPSIS, BWM, and SAW studies [82,84,85,[87][88][89]92,93]. Two utility-based methods were applied in seven studies, as follows: AHP [79,84,89,90], and ANP [81,82,91].…”

“…Thus, 18 scenarios were built and ranked as alternatives, based on relevant data from the literature and taking into account the principles of prevention, planning, and design. Finally, it is important to mention that six studies [84,[88][89][90]92,93] combined fuzzy logic with MCDM methods, and one [88] used artificial intelligence (i.e., GRA) to select and rank the best sustainable reverse logistics recovery options, focusing on the case of China Pakistan Economic Corridor (CPEC).…”

Assessment of cloud vendors using interval‐valued probabilistic linguistic information and unknown weights

“…Recently, Al‐Faifi et al 45 presented a hybrid model with clustering and MCDM for the appropriate selection of CVs based on smart data from a simulation in MATLAB. Lai et al 46 proposed an integrated model with Z ‐numbers to select suitable sustainable CVs with Gini index and double normalized‐based multiple aggregation methods. Hussain et al 47 presented a novel framework for CV selection under the SaaS category by extending the best–worst (BW) method to fuzzy context.…”