Cloud vendor selection for the healthcare industry using a big data-driven decision model with probabilistic linguistic information

Applied Intelligence - The role of cloud services in the data-intensive industry is indispensable. Cision recently reported that the cloud market would grow to 55 billion USD, with an active...     

Double-hierarchy hesitant fuzzy linguistic information-based framework for green supplier selection with partial weight information

Neural Computing and Applications - Green supplier selection (GSS) is a crucial issue in green supply chain management. CAPS indicate that industries spend yearly USD 25 million per procurement,...     

Solving cloud vendor selection problem using intuitionistic fuzzy decision framework

This paper presents a new decision-making framework called cloud vendor selector (CVS) for effective selection of cloud vendors by mitigating the challenge of unreasonable criteria weight assignment and improper management of uncertainty. The CVS comprises of two stages where, in the first stage, decision-makers’ intuitionistic fuzzy-valued preferences are aggregated using newly proposed extended simple Atanassov’s intuitionistic weighted geometry operator. Further, in the second stage, criteria weights are estimated by using newly proposed intuitionistic fuzzy statistical variance method and finally, ranking of cloud vendor (CV) is done using newly proposed three-way VIKOR method under intuitionistic fuzzy environment which introduces neutral category along with cost and benefit for better understanding the nature of criteria. An illustrative example of CV selection is demonstrated to show the practicality and usefulness of the proposed framework. Finally, the strength and weakness of the proposal are realized from both theoretic and numeric context by comparison with other methods.

     

Generalized orthopair fuzzy weighted distance-based approximation (WDBA) algorithm in emergency decision-making

With the intensification of global warming trends, the frequent occurrence of natural disasters has brought severe challenges to the sustainable development of society. Emergency decision-making (EDM) in natural disasters is playing an increasingly important role in improving disaster response capacity. In the case of EDM evaluation, the essential problem arises serious incompleteness, impreciseness, subjectivity, and incertitude. The q-rung orthopair fuzzy set (q-ROFS), disposing the indeterminacy portrayed by membership and nonmembership with the sum of $q$th power of them, is a more viable and effective means to seize indeterminacy. The aim of paper is to present a new score function of q-rung orthopair fuzzy number (q-ROFN) for solving the failure problems when comparing two q-ROFNs. Firstly, we introduce some basic set operations for q-ROFS. The properties of these operations are also discussed in detail. Later, we propose a q-rung orthopair fuzzy decision-making method based on weighted distance-based approximation (WDBA), in which the weights of decision-makers are obtained from a nonliner optimization model according to the deviation-based method. Finally, some examples are investigated to illustrate the feasibility and validity of the proposed approach. The salient features of the proposed method, compared to the existing q-rung orthopair fuzzy decision-making methods, are as follows: (a) it can obtain the optimal alternative without counterintuitive phenomena and (b) it has a great power in distinguishing the optimal alternative.     

A decision framework under probabilistic hesitant fuzzy environment with probability estimation for multi-criteria decision making

Neural Computing and Applications - With growing hesitation in human perception, hesitant fuzzy set, an important extension of fuzzy set, has gained much attention from the research community. The...