Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making

A systematic review on multi-criteria group decision-making methods based on weights: analysis and classification scheme

Interest in group decision-making (GDM) has been increasing prominently over the last decade. Access to global databases, sophisticated sensors which can obtain multiple inputs or complex problems requiring opinions from several experts have driven interest in data aggregation. Consequently, the field has been widely studied from several viewpoints and multiple approaches have been proposed. Nevertheless, there is a lack of general framework. Moreover, this problem is exacerbated in the case of experts’ weighting methods, one of the most widely-used techniques to deal with multiple source aggregation. This lack of general classification scheme, or a guide to assist expert knowledge, leads to ambiguity or misreading for readers, who may be overwhelmed by the large amount of unclassified information currently available. To invert this situation, a general GDM framework is presented which divides and classifies all data aggregation techniques, focusing on and expanding the classification of experts’ weighting methods in terms of analysis type by carrying out an in-depth literature review. Results are not only classified but analysed and discussed regarding multiple characteristics, such as MCDMs in which they are applied, type of data used, ideal solutions considered or when they are applied. Furthermore, general requirements supplement this analysis such as initial influence, or component division considerations. As a result, this paper provides not only a general classification scheme and a detailed analysis of experts’ weighting methods but also a road map for researchers working on GDM topics or a guide for experts who use these methods. Furthermore, six significant contributions for future research pathways are provided in the conclusions.The first author acknowledges support from the Spanish Ministry of Universities [grant number FPU18/01471]. The second and third author wish to recognize their support from the Serra Hunter program. Finally, this work was supported by the Catalan agency AGAUR through its research group support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/ 501100011033.Peer ReviewedPostprint (published version

A novel approach for assessing the reliability of data contained in a single valued neutrosophic number and its application in multiple criteria decision making

Multiple criteria decision making is one of the many areas where neutrosophic sets have been applied to solve various problems so far

Performance Evaluation of 198 Village Governments using Fuzzy TOPSIS and Intuitionistic Fuzzy TOPSIS

Currently, volatility, uncertainty, complexity, and ambiguity (VUCA) have become unavoidable problems. In addition, knowledge or information that is not managed properly can result in inappropriate decision-making processes within an organization. Business Intelligence conception is then becoming an essential view for converting unstructured data and information into a more actionable strategic plan that allows organizations to make competitive decisions. Village Government (VG) is the smallest organization in the Indonesian government system because VG implemented regulation and development programs in all areas of a national government. VG executes a series of tasks every year starting from planning, budgeting, administrating, executing, and reporting. However, the important role of VG in the development of a country brings also some drawbacks such as corruption and other domino effects. Several factors have been identified that cause those problems such as lack of capabilities in managing village organization and human resources quality. Monitoring and evaluation regarding those VG performances normally have been done each year. However, measurable evaluation standard for VG performance until recently has not been determined nationally. This study is intended to make a comprehensive standard of village government performance assessment through a Good Governance Framework approach. This study involved 198 villages from Madiun Regency as a case study. Seventy-four measured parameters were proposed to evaluate VG performance mapping. Fuzzy TOPSIS is implemented to rank those 198 villages into 4 groups of VG performance levels. The fuzzy TOPSIS classification result has been validated by using manual scoring and the accuracy reached 86,4%

On improved RCM model to theart evaluation for radiation sesource

Focusing on the deficiency of intuition, real-time and complexity of threat evaluation of radiation resource, an algorithm based on improved radar chart method (RCM) is proposed in this paper. In the algorithm proposed, coarse sorting is integrated with fine sorting to obtain a more accurate and reliable result of threat evaluation. Coarse sorting is applied to sequence all the radiation resource roughly according to radar operation mode, and reduce the task priority of low-threat radiation resource. Then, on the basis of improved RCM, fine sorting is applied to sequence the radiation resource with same radar operation mode. Finally, obtain the results of threat evaluation which combined coarse sorting with fine sorting. Simulation analysis shows the correctness and effectiveness of this algorithm. Comparing with classical method of threat evaluation of radiation resource based on RCM, the algorithm proposed in this paper is more visual in image and can work quickly with lower complexity

An Intuitionistic Fuzzy Group Decision-Making to Measure the Performance of Green Supply Chain Management with TOPSIS Method

Green supply chain management (GSCM) integrates environmental regulations into supply chain management to diminish the negative effects of supply chain processes on the environment. The environmental problems appeared by an enterprise arise from designing the product and last until the recycling process. GSCM activities include five drivers such as green design, green purchasing, green transformation, green logistics and reverse logistics. In this chapter, the main aim is to explain these drivers and to show how to measure the GSCM success of companies, which operate as tire manufacturers by using an evaluation of a group of experts in their field. The proposed method, intuitionistic fuzzy technique for order preference by similarity to an ideal solution as an effective group decision-making method, helped to identify the alternative company 1 with the best GSCM performance among six different alternative tire companies under the consideration of five important GSCM drivers. The ranking result was as follows: A1 > A3 > A4 > A2 > A6 > A5