Soft consensus model for the group fuzzy AHP decision making

The fuzzy analytic hierarchy process (AHP) is an extension to the classical AHP that enables dealing with the impreciseness and vagueness of judgments. It has been frequently used for handling complex decision making problems that demand a group rather than a single decision maker. Group decision making aggregates the judgments of individuals into a joint decision. Although consensus is the desired result in group decision making, it is difficult to achieve due to the diversity of opinions, knowledge and experiences of the decision makers. Therefore, the concept of soft consensus can be applied. We propose a new soft consensus based model for fuzzy AHP group decision making. The judgments in the model are presented as triangular fuzzy numbers. The closeness between judgments of two decision makers is measured by the individual fuzzy consensus index which in turn is based on the compatibility index from classical AHP. In each iteration, two decision makers with the most dissimilar opinions are identified and their judgments are adapted. The process is repeated until the desired consensus level is reached. The model can also take into account the weights of importance of individual decision makers. A fuzzy extension of the geometric mean method is employed for deriving fuzzy weights from a group fuzzy pairwise comparison matrix. The application of the model is provided in an example from the literature

A Survey on Data Security in Cloud Computing Using Blockchain: Challenges, Existing-State-Of-The-Art Methods, And Future Directions

Cloud computing is one of the ruling storage solutions. However, the cloud computing centralized storage method is not stable. Blockchain, on the other hand, is a decentralized cloud storage system that ensures data security. Cloud environments are vulnerable to several attacks which compromise the basic confidentiality, integrity, availability, and security of the network. This research focus on decentralized, safe data storage, high data availability, and effective use of storage resources. To properly respond to the situation of the blockchain method, we have conducted a comprehensive survey of the most recent and promising blockchain state-of-the-art methods, the P2P network for data dissemination, hash functions for data authentication, and IPFS (InterPlanetary File System) protocol for data integrity. Furthermore, we have discussed a detailed comparison of consensus algorithms of Blockchain concerning security. Also, we have discussed the future of blockchain and cloud computing. The major focus of this study is to secure the data in Cloud computing using blockchain and ease for researchers for further research work

Decision-Making Framework for Medical Equipment Maintenance and Replacement in Private Hospitals

The process for medical equipment maintenance and replacement in hospitals is a challenging and demanding procedure. Further, the topic of making decisions to maintain or replace or upgrade medical equipment has been debated for a long time since errors equipment maintenance will increase equipment failure at undesirable times; or if early equipment replacement will result in high investment costs and premature disposal. Therefore, standard operating procedures or guidelines need to be in place to help healthcare facilities conduct a more organized and planned maintenance and replacement process. Many hospitals may already have established replacement guidelines or have implemented asset monitoring systems for this purpose. However, the effectiveness of this system has not yet been systematically evaluated. Several studies have been conducted on the same research topic, but most of the findings emphasize the replacement method rather than the criteria that contributed to the decision. Criteria for replacing medical equipment play an important role in ensuring that the equipment can be used cost-effectively. Thus, this research aims to identify important criteria that need to be considered for medical equipment maintenance and replacement focusing on private hospitals. This research was conducted in three phases: (1) a structured literature review; (2) semi-structured interviews with eleven (11) healthcare experts; and (3) a pairwise comparison survey with 50 biomedical engineers. A decision-making framework was developed based on the findings of these three research phases. The framework developed will provide guidelines for practitioners and academics to understand and make better decisions for medical equipment maintenance and replacement in the context of private hospitals

Distance-based consensus models for fuzzy and multiplicative 3 preference relations

This paper proposes a distance-based consensus model for fuzzy preference relations where the weights of fuzzy preference relations are automatically determined. Two indices, an individual to group consensus index (ICI) and a group consensus index (GCI), are introduced. An iterative consensus reaching algorithm is presented and the process terminates until both the ICI and GCI are controlled within predefined thresholds. The model and algorithm are then extended to handle multiplicative preference relations. Finally, two examples are illustrated and comparative analyses demonstrate the effectiveness of the proposed methods

Combining user preferences and expert opinions: a criteria synergy-based model for decision making on the Web

Customers strongly base their e-commerce decisions on the opinions of others by checking reviews and ratings provided by other users. These assessments are overall opinions about the product or service, and it is not possible to establish why they perceive it as good or bad. To understand this “why”, it is necessary an expert’s analysis concerning the relevant factors of the product or service. Frequently, these two visions are not coincident and the best product for experts may not be the best one for users. For this reason, trustworthy decision-making methods that integrate the mentioned views are highly desirable. This article proposes a multi-criteria decision analysis model based on the integration of users’ preferences and experts’ opinions. It combines the majority’s opinion and criteria synergy to provide a unified perspective in order to support consumers’ ranking-based decisions in social media environments. At the same time, the model supplies useful information for managers about strengths and weaknesses of their product or service according to users’ experience and experts’ judgment. The aggregation processes and synergy criteria are modeled in order to obtain an adequate consensus mechanism. Finally, in order to test the proposed model, several simulations using hotel valuations are performed.Project UTN4058 of National Technological University (Argentine) Fellowship for Short Term Postdoctoral Stays at University of Malaga – International Campus of Excellence Andalucía Tec

Ordering based decision making: a survey

Decision making is the crucial step in many real applications such as organization management, financial planning, products evaluation and recommendation. Rational decision making is to select an alternative from a set of different ones which has the best utility (i.e., maximally satisfies given criteria, objectives, or preferences). In many cases, decision making is to order alternatives and select one or a few among the top of the ranking. Orderings provide a natural and effective way for representing indeterminate situations which are pervasive in commonsense reasoning. Ordering based decision making is then to find the suitable method for evaluating candidates or ranking alternatives based on provided ordinal information and criteria, and this in many cases is to rank alternatives based on qualitative ordering information. In this paper, we discuss the importance and research aspects of ordering based decision making, and review the existing ordering based decision making theories and methods along with some future research directions

A Similarity Measure-based Optimization Model for Group Decision Making with Multiplicative and Fuzzy Preference Relations

Group decision making (GDM) problem based on different preference relations aims to obtain a collective opinion based on various preference structures provided by a group of decision makers (DMs) or experts, those who have varying backgrounds and interests in real world. The decision process in proposed question includes three steps: integrating varying preference structures, reaching consensus opinion, selecting the best alternative. Two major approaches: preference transformation and optimization methods have been developed to deal with the issue in first step. However, the transformation processes causes information lose and existing optimization methods are so computationally complex that it is not easy to be used by management practice. This study proposes a new consistency-based method to integrate multiplicative and fuzzy preference relations, which is based on a cosine similarity measure to derive a collective priority vector. The basic idea is that a collective priority vector should be as similar per column as possible to a pairwise comparative matrix (PCM) in order to assure the group preference has highest consistency for each decision makers. The model is computationally simple, because it can be solved using a Lagrangian approach and obtain a collective priority vector following four simple steps. The proposed method can further used to derive priority vector of fuzzy AHP. Using three illustrative examples, the effectiveness and simpleness of the proposed model is demonstrated by comparison with other methods. The results show that the proposed model achieves the largest cosine values in all three examples, indicating the solution is the nearest theoretical perfectly consistent opinion for each decision makers

Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors

This work was supported in part by the NSF of China under grants 71171160 and 71571124, in part by the SSEM Key Research Center at Sichuan Province under grant xq15b01, in part by the FEDER funds under grant TIN2013-40658-P, and in part by Andalusian Excellence Project under grant TIC-5991.The consensus reaching process (CRP) is a dynamic and iterative process for improving the consensus level among experts in group decision making. A large number of non-cooperative behaviors exist in the CRP. For example, some experts will express their opinions dishonestly or refuse to change their opinions to further their own interests. In this study, we propose a novel consensus framework for managing non-cooperative behaviors. In the proposed framework, a self-management mechanism to generate experts' weights dynamically is presented and then integrated into the CRP. This self-management mechanism is based on multi-attribute mutual evaluation matrices (MMEMs). During the CRP, the experts can provide and update their MMEMs regarding the experts' performances (e.g., professional skill, cooperation, and fairness), and the experts' weights are dynamically derived from the MMEMs. Detailed simulation experiments and comparison analysis are presented to justify the validity of the proposed consensus framework in managing the non-cooperative behaviors.National Natural Science Foundation of China 71171160 71571124SSEM Key Research Center at Sichuan Province xq15b01European Union (EU) TIN2013-40658-PAndalusian Excellence Project TIC-599