Fuzzy-based Multi-criteria Decision-making Research Articles

Selection of most significant risk factors for Indian tunnel projects: an integrated fuzzy-based MCDM approach

The purpose of this study is to develop a comprehensive fuzzy-based multi-criteria decision-making (MCDM) methodology to generate ranking of risk factors on Indian tunnel projects. The complexities and high costs involved in tunnelling projects create a strong necessity to look into quantitative risk assessment so as to prevent project underperformance. The analysis of large number of risk factors makes the task difficult. Thus, the present study attempts to address this challenge by developing a comprehensive framework for risk prioritization. In the previous work, critical risk factors (CRFs) were identified using Relative importance Index from the factor pool. The present study utilizes the Analytic Hierarchy Process approach for the identification of CRFs. The list of 25 critical factors, obtained by integrating both RII and AHP results, is further analyzed using the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS), based on three criteria, probability of occurrence, impact and cost. The methodology developed in this work will help in selecting the most significant risk factors, thus substantially improving the risk analysis process on Indian tunnel projects. This study contributes towards the development of framework for risk prioritization which will be useful for researchers and practitioners.

A New Algorithm on Pythagorean Fuzzy-Based Multi-Criteria Decision-Making and Its Application

The rapid growth in information technology (IT) and the implementation of IT tools has aided hospitals in delivering healthcare at an unprecedented pace. However, without the proper knowledge and understanding of software tools, healthcare establishments may find it difficult to select the best hospital management system software solution for their business. In this paper, a problem based on multi-criteria decision-making (MCDM) is modelled to identify the best hospital management software on the basis of a thorough analysis of every aspect of the healthcare needs and required software competencies, so that hospitals can select a robust hospital management software system. This article presents an extended MCDM method for estimating and selecting robust hospital management system alternatives using the Pythagorean fuzzy method based on the removal effects of criteria (MEREC) and stepwise weight assessment ratio analysis (SWARA). To do this, the objective weights and subjective weights are assessed by the MEREC and SWARA models, and the measurement alternatives and ranking according to compromise solution (MARCOS) method determines the order of preference of the alternatives on the basis of the Pythagorean fuzzy set. Moreover, after in-depth analysis of every aspect of healthcare needs and required software competencies in India, the proposed method is designed to identify the best hospital management software tool, so that the hospital can choose a robust hospital management system. Comparative discussion is presented to exhibit the rationality and sustainability of the established approach to prioritize hospital management software options.

Customs brokerage company selection problem with hybrid method

Purpose- Customs are the main transit points in cross-border trade activities. Customs logistics activities are carried out by authorized customs brokerage companies (CBC). Export/import companies that execute customs clearance with the right CBC partners gain competitive advantage. Therefore, selecting the right CBC is an important decision-making problem. In this research, CBC selection problem is handled with fuzzy-based multi criteria decision-making (MCDM) methods. Methodology- The research application covers the CBC selection process of an export firm. The criteria for the problem are obtained as a result of the literature review. The opinions of the decision makers are also taken. Seven criteria have been identified. These criteria are cost/price, service quality, information system and technology, flexibility, relationship, professionalism, reputation. Fuzzy-based stepwise weight assessment ratio analysis (F-SWARA) method is used for criterion weighting. In order of alternatives, ranking of alternatives through functional mapping of criterion sub-intervals into single interval (F-RAFSI) method is applied. Four decision makers are used to compare the criteria. Findings- Four CBC alternative rankings based on criteria are made. According to the research findings, the highest criterion weight is determined as service quality. The first alternative is chosen as the best alternative. Conclusion- CBC alternative sequencing has been made for the export company by applying fuzzy-based MCDM methods. Thus, the applicability of MCDM methods is supported in CBC company selection. In addition, the CBC selection criteria are determined, and the CBC selection problem are shed light on. Suggestions are also made to export companies and researchers based on the results of the research.

Analysing the impact of COVID-19 pandemic on the psychological health of people using fuzzy MCDM methods

Recently, a large portion of the world's population has experienced an unprecedented devastating effect of the COVID-19 pandemic. At the time of its outbreak, not much was known about this disease and therefore, quarantine and social distancing were the only ways suggested to prevent its spread among humans. Although the current situation is much better than before however, strict social distancing norms as well as frequent long-lasting lockdowns with stringent guidelines and actions to control the spread in the early days have affected the physical and psychological health of the people. Consequently, this study was carried out to attain the following major objectives: (i) to identify the potential psychological problems/factors that might have been caused due to COVID-19 led social distancing and lockdowns, and (ii) to determine the ranks of the identified psychological factors to reflect their degree of criticality. The first objective was achieved by gathering information about the potential psychological factors from the experts. Data, in terms of linguistic variables, was collected from the experts and analyzed using two fuzzy-based multi-criteria decision-making (MCDM) methods i.e. Fuzzy Best Worst Method (F-BWM) and Fuzzy TOPSIS (F-TOPSIS) which led to the accomplishment of the second objective. The results of this study revealed that anxiety, stress, panic attacks, frustration, and insomnia were the top five critical psychological factors that might have affected people due to this pandemic. Consistency of the results was ensured by comparing the obtained ranks with the ranks found using the Fuzzy WSM and Fuzzy MABAC methods. In addition, the robustness of the results was ascertained by conducting the sensitivity analysis. Based on the findings of the study, the identified factors were categorized into most, average, and least critical psychological factors. This research might help the relevant authorities to understand the extent of the seriousness of the various psychological factors caused by this pandemic, so that an effective strategy may be developed for better management, control, and safety.

The attitude of MCDM approaches versus the optimization model in finding the safest shortest path on a fuzzy network

This paper examines the performances of the multi-criteria decision-making (MCDM) methods and optimization model in solving multi-attribute shortest path problems such as the safest shortest path under a fuzzy environment. To the best of the knowledge of the authors, this is the first study performing comparative analysis on finding the multi-attribute shortest path by employing well-known techniques in terms of computational effort and results in a fuzzy environment. To this end, the safest shortest path problem, where the risk and distance values concerning arcs on a directed network are defined as triangular fuzzy numbers, is handled. The solution process is carried out under two main headings: (i) To start the solution with MCDM methods, an auxiliary algorithm that constructs a fuzzy decision matrix is proposed. Then, Fuzzy-Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS), Fuzzy Simple Additive Weighting (F-SAW), and Fuzzy Evaluation Based on Distance from Average Solution (F-EDAS), that are fuzzy-based MCDM methods, are employed to rank the alternative paths. (ii) A multi-objective fuzzy optimization model is formulated, and the most reasonable paths are obtained considering different α-cut levels. Following that, comparative analysis is performed through a set of scenarios considering the different weights of the criteria to see the variability in the rankings. Besides, the addressed fuzzy-based MCDM methods are compared in terms of computational complexity. Overall, the main findings and managerial insights regarding the effectiveness and performance of the methods discussed in the solution process are provided.

Selection of electric vehicle using integrated Fuzzy-MCDM approach with analysis on challenges faced in hilly terrain

ABSTRACT Electric Vehicles (EVs) are still not a preferable choice among the consumers of vehicular world. It has now become necessary to showcase the merits of EVs to the consumers. If compared to the nonhilly areas, people in hills are still reluctant toward adoption of EVs due to various constraints as mentioned in this article. Several factors affect the desirability of EVs, including technical and customer-related factors. This work also proposes a model to help customers choose the most suitable EV by adjudging vehicles concerning multiple factors. During the judging process, extra care is taken to include factors defining the viewpoint of the general customer while purchasing the EV. Distinctly, two sets of criteria, one highlighting the technical specifications of the EV and the other exclusively defining the customer’s perspective of it, including features like cost, warranty, charging facility, etc., are taken up to rank the six most popular EV models in India using a Fuzzy-based Multi-Criteria Decision-Making (MCDM) model. Here, the Quality Function Deployment (QFD) model is used to tackle the weightage of the criteria based on customer’s feedback. The outcome of this work and the evaluation process can act as a quick guideline for any arbitrary customer who plans to make a new purchase of an EV. Hyundai Kona electric scored 0.223 the highest, making it the most suitable choice, and Mahindra E20 plus scored 0.121, making it the least suitable choice among the list of EVs chosen in the study for the Indian market. An additional case study segment in this article focuses on the market penetration issues that the EVs may likely face on a hilly terrain in the perspective of energy consumption per trip and the customer woes that may arise due to terrain.

Recommended System for Cluster Head Selection in a Remote Sensor Cloud Environment Using the Fuzzy-Based Multi-Criteria Decision-Making Technique

Clustering is an energy-efficient routing algorithm in a sensor cloud environment (SCE). The clustering sensor nodes communicate with the base station via a cluster head (CH), which can be selected based on the remaining energy, the base station distance, or the distance from the neighboring nodes. If the CH is selected based on the remaining energy and the base station is far away from the cluster head, then it is not an energy-efficient selection technique. The same applies to other criteria. For CH selection, a single criterion is not sufficient. Moreover, the traditional clustering algorithm head nodes keep changing in every round. Therefore, the traditional algorithm energy consumption is less, and nodes die faster. In this paper, the fuzzy multi-criteria decision-making (F-MCDM) technique is used for CH selection and a threshold value is fixed for the CH selection. The fuzzy analytical hierarchy process (AHP) and the fuzzy analytical network process (ANP) are used for CH selection. The performance evaluation results exhibit a 5% improvement compared to the fuzzy AHP clustering method and 10% improvement compared to the traditional method in terms of stability, energy consumption, throughput, and control overhead.

Sustainability Evaluation of Rainwater Harvesting-Based Flood Risk Management Strategies: A Multilevel Decision-Making Framework for Arid Environments

Arid environmental regions have faced disastrous floods as a result of global climate change in the recent past, particularly the Gulf countries. For the municipalities in urban areas, flood risk management (FRM) by means of rainwater harvesting (RWH) methods is a daunting task due to multiple influencing variables, such as hydraulic capacity of drainage infrastructure, effectiveness of RWH methods, flood frequencies, and associated consequences in different land uses. The solution of this integration problem may lead to development of several possible scenarios, e.g. improved drainage infrastructure for 10-year flood, existing drainage infrastructure with green roofs for 25-year flood, etc., which have their exclusive techno-economic constraints. A multilevel decision-making framework is developed to prioritize sustainable FRM scenarios for different land uses. Firstly, screening was carried out using checklist matrix to identify potential scenarios out of 105 possible combinations. Subsequently, risk analysis is performed to identify the scenarios with an acceptable risk using fuzzy failure mode and effect analysis (fuzzy-FMEA). Finally, shortlisted scenarios are evaluated for their overall environmental, technical, and economic sustainability by means of fuzzy-based multicriteria decision-making. The implementation of the framework on Buraydah (Qassim, Saudi Arabia) revealed that ‘existing drainage system supported with green roofs and retention ponds’ is the most sustainable FRM scenario (to safely route 100-year flood) for mixed development and residential neighbourhoods, while ‘improved drainage system with retention ponds’ comes out to be the most sustainable choice for central districts.

A novel extension to VIKOR method under intuitionistic fuzzy context for solving personnel selection problem

Personnel selection is a challenging problem for any organization. The success of a project is determined by the human resources that handle the project. To make better personnel selections, researchers have adopted multi-criteria decision-making (MCDM) approaches. Among these, fuzzy-based MCDM methods are most frequently used, as they handle vagueness and imprecision better. Intuitionistic fuzzy set (IFS) is a popular MCDM context which provides degree of membership and non-membership for preference elicitation. In this work, we propose a novel decision-making framework that consists of two stages. In the first stage, a new extension to the popular VIKOR method is presented under IFS context. The positive and negative ideal solutions are determined, and VIKOR parameters are calculated using transformation procedure. The proposed method combines the strength of both interval-valued fuzzy set and IFS that is more effective in handling vagueness with a simple formulation setup. In the second stage, a personnel selection problem is used to validate the proposed framework. Finally, the superiority and weakness of the proposed framework are discussed by comparison with other methods.

An integrated fuzzy-based multi-criteria decision-making approach for the selection of an effective manufacturing system

PurposeThe research paper presents analysis and prioritization of barriers influencing the improvement in the effectiveness of manufacturing system. The purpose of this paper is to develop an integrated fuzzy-based multi-criteria decision-making (F-MCDM) framework to assist management of the case company in the selection of most effective manufacturing system. The framework helps in prioritizing the manufacturing systems on the basis of their effectiveness affected by the barriers.Design/methodology/approachIn this paper, on the basis of experts’ opinion, five barriers have been identified in a brain-storming session. The problem of prioritization of manufacturing system is a multi-criteria decision-making (MCDM) problem and hence is solved by using the F-MCDM approach using dominance matrix.FindingsManufacturing systems’ effectiveness for Indian industries is influenced by barriers. The prioritization of manufacturing systems depends on qualitative factor decision-making criteria. Among the manufacturing systems, leagile manufacturing system is given the highest priority followed by lean manufacturing system, agile manufacturing system, flexible manufacturing system and cellular manufacturing system.Research limitations/implicationsThe selection of an appropriate manufacturing system plays a vital role for sustainable growth of the manufacturing company. In the present work, barriers which influence the effectiveness of manufacturing system have been identified. On the basis of degree of influence of barriers on the effectiveness of the manufacturing system, five alternative manufacturing systems are prioritized. The framework will help the management of the case company to take reasonable decision for the adoption of the appropriate manufacturing system.Practical implicationsThe results of the research work are very useful for the manufacturing companies interested in analyzing the alternative manufacturing systems on the basis of their effectiveness and their sensitivity toward various barriers. The management of Indian manufacturing company will take decision to adopt a manufacturing system whose effectiveness is least sensitive toward barriers. Effectiveness of such manufacturing system will improve with time without having retardation due to barriers. With improved effectiveness of the manufacturing system, the manufacturing company would be able to survive with global competition. The result of the present work is based on the inputs from the case company and may vary for the other manufacturing company. In the present work, only five alternative manufacturing systems and five barriers have been considered. To obtain the better result, MCDM approach with more number of alternative manufacturing systems and barriers might be considered.Originality/valueThe research work is based on the fuzzy analytic hierarchy process framework and on the case study conducted by the authors. The work carried out is original in nature and based on the real-life case study.

A fuzzy-based decision making procedure for machine selection problem

The selection of appropriate machines is one of the most critical decisions in the design and development of an efficient production environment. It is the fact that improper machine selection can result in quality, flexibility, productivity, etc., problems and negatively affect the overall performance and productivity of a manufacturing system. On the other hand, selecting the best machine among many alternatives is a multi-criteria decision making (MCDM) problem. In this paper, a fuzzy-based MCDM approach is used. For this aim, the fuzzy analytic network process (FANP) is used to determine weights of the criteria and preference ranking organization method for enrichment evaluations (PROMETHEE) is used to obtain final ranking of alternative machines. The proposed approach is applied for the selection of a CNC router machine (RM) to be purchased in an international company. In the problem addressed, there are four main criteria, namely cost, quality, flexibility and performance with the corresponding fourteen sub-criteria. The results for the case study indicate the best machine among six potential alternatives and provide different managerial insights for the decision makers.

Lean Six Sigma project selection using hybrid approach based on fuzzy DEMATEL–ANP–TOPSIS

Purpose – The purpose of this paper is to select the optimal Lean Six Sigma (LSS) project using hybrid fuzzy-based Multi-Criteria Decision-Making (MCDM) approach for an automotive component manufacturing organization. Design/methodology/approach – The LSS project selection has been formulated as the MCDM problem. Hybrid MCDM method based on Decision-Making Trial and Evaluation Laboratory Model (DEMATEL), Analytical Network Process (ANP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been used to select the optimal LSS project. The methodology enabled the practitioners to systematically prioritize LSS projects. Findings – The finding of this study is that, out of five LSS projects, project P3 is the best LSS project. P3 is the optimal LSS project with reduced failure risk, and efforts are being taken to implement the selected project. Research limitations/implications – The problem formulation and methodology has been tested for a single study. In future, more number of studies could be conducted using the hybrid approach. This method is presently applied for an automotive component manufacturing organization; in future, the approach could be applied in different industrial sectors for improving its effectiveness. Practical implications – The case study has been conducted in a real-time industrial problem. The practitioners expressed the usefulness of the methodology for prioritizing LSS projects Hence, the inferences derived are found to possess practical relevance. Originality/value – The original contribution of the study is the selection of optimal LSS project using hybrid MCDM technique.

Prioritizing the best sites for treated wastewater instream use in an urban watershed using fuzzy TOPSIS

This study developed a new framework that prioritized the best sites for treated wastewater (TWW) instream use using fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS), a fuzzy-based multi-criteria decision-making (MCDM) technique. We identified key criteria for TWW use based on the Driver-Pressure-State-Impact-Response (DPSIR) framework that considered technical, social, economic and environmental aspects. We also introduced triangular fuzzy numbers and conducted individual interviews to consider the uncertainty of weighting values and input data. This procedure was applied at ten sites in a South Korean urban watershed, where hydrologic modeling exercises were performed. Our simulation results for water quantity (i.e., drought flow, low flow and the days to satisfy instream flow) and water quality (i.e., BOD concentration and the days to satisfy target water quality) showed significant inter-annual variability that could be better represented with fuzzy numbers. Furthermore, the use of fuzzy TOPSIS gave different rankings of the best sites for TWW use compared to those obtained from a weighting sum method, a traditional MCDM technique. Such varied rankings with different MCDM techniques indicate the need for fuzzy-based techniques, considering various uncertainties and thus being less controversial.