Fuzzy TOPSIS Method Research Articles

Ranking of Criteria Affecting the Implementation Readiness of Internet of Things in industries Using TISM and Fuzzy TOPSIS Analysis

The Internet of Things (IoT) technology has emerged as a vital driver across various fields, engaging businesses, platforms, and industries. IoT involves a holistic ecosystem and a value chain that necessitates the evaluation of impactful dimensions for successful implementation. This research employs the TISM method to identify driver and dependent criteria regarding IoT implementation readiness and uses the fuzzy TOPSIS method to rank these criteria. In the initial step, 15 criteria were identified through a review of previous studies. The TISM results reveal five levels reflecting the driver power and dependence of the criteria. Based on these results, “Implementation Knowledge and Expertise (C2)”, “Technical and Infrastructural Readiness (C9)” and “Financial and Investment Readiness (C12)” were placed at level 5, marking them as the most driver criteria. Additionally, “Implementation Roadmap (C8)” was identified as the most dependent criterion at level one. According to the fuzzy TOPSIS results, “Senior Management Support (C6)”, “IoT Usage Culture (C1)”, “Business Model Development Capability (C15)”, “Financial and Investment Readiness (C12)” and “Technical and Infrastructural Readiness (C9)” ranked first to fifth, respectively. The combined results provide valuable insights for decision-makers and stakeholders involved in IoT implementation. By determining driver and dependent levels and ranking the criteria, industries can effectively prepare for the successful implementation of IoT.

Evaluating sustainable water management strategies using TOPSIS and fuzzy TOPSIS methods

This study evaluates sustainable water management strategies using TOPSIS and Fuzzy TOPSIS (FTOPSIS) to address global water scarcity by comparing rainwater harvesting, water recycling, and desalination across five criteria: water efficiency, cost-effectiveness, environmental impact, social equity, and technological feasibility. The results show Rainwater Harvesting as the most balanced option with a relative closeness value of Ci+=0.640, excelling in social equity and environmental sustainability. Water Recycling ranks closely behind (Ci+=0.608), highlighting its adaptability and technological feasibility, while Desalination, though highly efficient, is hindered by lower cost-effectiveness (Ci+=0.578). By integrating TOPSIS and FTOPSIS, the study addresses uncertainties and subjective criteria, providing a robust multi-dimensional assessment framework for resource management. This methodology aids decision-makers in identifying strategies that align with sustainable development goals and adapt to regional priorities. Future work can expand this framework to include stakeholder engagement and policy factors, enhancing water management strategies for resilient, long-term solutions.

Overcoming Barriers to Sustainable Green Transportation in Ho Chi Minh City: A Pathway Toward Achieving SDGs 11 and 13

Ho Chi Minh City (HCMC), Vietnam’s largest urban center, is home to over 9 million people and faces significant challenges due to rapid urbanization and the heavy reliance on personal vehicles. Over 95% of urban passenger transport in HCMC relies on high-emission cars, contributing to severe air pollution and slowing progress toward the United Nations Sustainable Development Goals (SDGs). Greening HCMC’s transport system is essential to reducing emissions and achieving SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). Despite ongoing efforts, many barriers continue to hinder this transition. This paper aims to identify and prioritize these barriers using the fuzzy TOPSIS method. The results highlight limited infrastructure investment, a lack of charging stations, and unclear policies as the top obstacles. Other challenges include poor service quality, dependence on private vehicles, low community awareness, high fuel prices, and the cost of green vehicles. These findings offer a foundation for proposing solutions and prioritizing actions to build an eco-friendly transport system. This would directly support the SDGs related to sustainable cities, climate action, and public health. A thorough analysis of these barriers and their impact is vital to expedite and enhance the “greening” process by focusing on factors with significant and decisive influence.

Evaluation of low-and middle-income countries according to cardiovascular disease risk factors by using pythagorean fuzzy AHP and TOPSIS methods

BackgroundCardiovascular disease risk factors play a crucial role in determining individuals’ future health status and significantly affect health. This paper aimed to address cardiovascular disease risk factors in low- and middle-income countries using multi-criteria decision-making methods.MethodsIn line with this objective, 22 evaluation criteria were identified. Due to the unequal importance levels of the criteria, the interval-valued Pythagorean Fuzzy AHP (PF-AHP) method was employed for weighting. The TOPSIS method was utilized to rank the countries.ResultsThe application of interval-valued PF-AHP revealed that metabolic, behavioral, and economic factors are more important in contributing to disease risk. Among adults, tobacco use prevalence was identified as the most significant risk factor. According to the TOPSIS method, Lebanon, Jordan, Solomon Islands, Serbia, and Bulgaria ranked highest, while Timor Leste, Benin, Ghana, Niger, and Ethiopia ranked lowest.ConclusionsIdentifying disease risk factors and preventing or reducing risks are crucial in combating cardiovascular diseases. Therefore, it is recommended that countries ranking higher take remedial actions to reduce disease risk.

Fermatean Fuzzy TOPSIS Method and Its Application in Ranking Business Intelligence-Based Strategies in Smart City Context

With the expansion of smart cities, the use of business intelligence (BI) has emerged as a crucial tool for resource optimization, increasing efficiency, and improving the citizens’ quality of life. BI enables companies to make better strategic decisions by analyzing vast amounts of urban data, helping them remain competitive in the dynamic smart city environment. This study utilizes content analysis and the Fermatean Fuzzy TOPSIS (FF-TOPSIS) method to rank the strategies based on business intelligence in the context of smart city. Initially, relevant criteria were identified through content analysis, and subsequently, five strategies were developed and ranked based on these criteria. The results revealed that the "Development of IOT-enabled smart networks (S2)" ranked highest, as it plays a significant role in optimizing resource management and enhancing urban service performance, thereby contributing greatly to the advancement of smart cities. "Process automation and the deployment of robotic systems (S5)" ranked second, as it enhances efficiency and reduces human errors. "Cloud platform integration for seamless access to data and services (S3)" also proved to be of considerable importance, ranking third, as it provides seamless access to data and services. " Artificial intelligence deployment for predictive analytics and process optimization (S4)" ranked fourth and was vital for predictive analytics and process optimization, while " Big data analytics for smart decision-making (S1)"—despite its importance—ranked fifth. Urban managers should prioritize the development of IOT networks to fully leverage their potential for resource management and efficiency gains. Following this, attention to process automation and AI integration can significantly enhance the quality of life for citizens and reduce urban costs.

A three-way decision combining multi-granularity variable precision fuzzy rough set and TOPSIS method

This study proposed an innovative fuzzy rough set model to address multi-attribute decision-making problems. Initially, we introduced a novel model of multi-granularity variable precision fuzzy rough sets, which included three foundational models. This model was demonstrated to possess favorable algebraic and topological properties, and particularly noteworthy the comparable property. Subsequently, by integrating the novel model with the TOPSIS method, a novel three-way decision model was proposed. Within this framework, three fundamental models of multi-granularity variable precision fuzzy rough sets were applied in three methods to construct relative loss functions. This resulted in a three-way decision model with three distinct strategies. Finally, we implemented the proposed three-way decision model for risk detection in maternal women. Several experiments and comparisons were conducted to validate the effectiveness, stability, and reliability of our proposed approach. The experimental results indicated that the proposed method accurately classified and ranked maternal women. Overall, our approach offered multiple strategies and fault tolerance and was found to be effective for a large amount of data.

Enhancing gas drainage and ventilation efficiency in underground coal mines: A hybrid expert decision approach for booster fan prioritization

Expanding mining operations in goaf zones heightens gas production potential, posing challenges in maintaining adequate ventilation within development panels, consequently impacting coal production. Various strategies have been explored to enhance mine ventilation and gas drainage effectiveness. However, deficiencies persist in the proposed ventilation system for the Okaba underground coal mine, prompting this study’s necessity. Addressing these concerns, the study evaluates the feasibility of employing booster fans to mitigate the identified drawbacks. Prioritizing booster fans for airflow distribution in underground mines is a complex decision-making process, requiring an advanced expert system approach. To address this, the study proposes an intuitionistic-based fuzzy TOPSIS (IFT) method for booster fan prioritization in the Okaba mine. Results indicate that booster fan 4 (BF4) ranks highest, followed by booster fan 3 (BF3), consistent with fuzzy TOPSIS findings. Sensitivity analysis supports the predicted importance order, affirming the efficacy of the hybrid expert decision method in selecting a booster fan capable of enhancing the overall efficiency of gas drainage and ventilation systems in underground mines. This study introduces a Hybrid Expert Decision Approach that integrates IFT and traditional fuzzy TOPSIS methodologies. This hybrid approach is particularly novel because it combines the strengths of both methods to prioritize booster fans in underground coal mines.

Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method

This research explores multi-objective optimization in injection molding with a focus on identifying the optimal configuration for the moldability index in aviation propeller manufacturing. The study employs the Taguchi method and fuzzy analytic hierarchy process (FAHP) combined with the Technique for the Order Performance by Similarity to the Ideal Solution (TOPSIS) to systematically evaluate diverse objectives. The investigation specifically addresses two prevalent defects—shrinkage rate and sink mark—that impact the final quality of injection-molded components. Polypropylene is chosen as the injection material, and critical process parameters encompass melt temperature, mold temperature, filling time, cooling time, and pressure holding time. The Taguchi L25 orthogonal array is selected, considering the number of levels and parameters, and Finite Element Analysis (FEA) is applied to enhance precision in results. To validate both simulation outcomes and the proposed optimization methodology, Artificial Neural Network (ANN) analysis is conducted for the chosen component. The Fuzzy-TOPSIS method, in conjunction with ANN, is employed to ascertain the optimal levels of the selected parameters. The margin of error between the chosen optimization methods is found to be less than one percent, underscoring their suitability for injection molding optimization. The efficacy of the selected optimization method has been corroborated in prior research. Ultimately, employing the fuzzy-TOPSIS optimization method yields a minimum shrinkage value of 16.34% and a sink mark value of 0.0516 mm. Similarly, utilizing the ANN optimization method results in minimum values of 16.42% for shrinkage and 0.0519 mm for the sink mark. Doi: 10.28991/ESJ-2024-08-05-025 Full Text: PDF

Selecting the most successfull recycling strategy over daily consumption products: application of q-Rung Orthopair Fuzzy Topsis method

Recycling is the process of collecting and reusing that helps the countries to achieve their sustainable development goals. This study, for the first time in the literature, considers the recycling of many daily consumption products as a decision-making problem with the q-rung orthopair fuzzy (q-ROF) approach. In Turkey, recycling initiatives are primarily led by the government and municipalities, involving either reprocessing in public facilities or collaboration with private enterprises. The research evaluates the effectiveness of recycling strategies, considering paper, plastic, textiles, batteries, frying oils, electronics, glass, and wood as alternative products. Criteria such as convertibility rate, resource usage for recycling, converted product lifespan, recycling process complexity, economic gain, product consumption rate, and trading opportunities are employed in the decision-making process. The q-rung orthopair fuzzy Technique for Order Preference by Similarity to Ideal Solution (q-ROFTOPSIS) method is applied to assess these criteria. Decision makers, comprising a recycling expert, a recycling business engineer, and an academician specializing in recycling studies, contribute to the evaluation. The study reveals electronic products as the most successful in recycling, while frying oils exhibit the least success.

МЕТОД ВИБОРУ ТЕРМІНАЛЬНОГО ОБЛАДНАННЯ ПАСИВНИХ ОПТИЧНИХ МЕРЕЖ НА ОСНОВІ НЕЧІТКОГО БАГАТОКРИТЕРІАЛЬНОГО ПРИЙНЯТТЯ РІШЕНЬ

Currently, optical communication is the dominant technology in cable channels of transport networks, working with powerful homogeneous digital data streams. In particular, the PON (Passive Optical Network) technology is developing dynamically. However, one of the problems holding back PON development is the cost of line and subscriber equipment (Optical line terminal and Optical Network Unit). An urgent task at the moment is the rational choice of equipment when designing optical networks. Most problems in the design of telecommunication networks can be modeled as a multi-criteria decision-making problem, where it is necessary to evaluate several criteria and choose the most acceptable solution option for design engineers according to their preferences and requirements. The article considers the process of selecting an optical line terminal (OLT), which is associated with the evaluation of possible options by a set of indicators. The purpose of the study: building a model of the optimal choice of optical linear terminals in conditions of unclear information. The ambiguity of human judgments when evaluating the importance of criteria (indicators) and evaluating alternatives based on the criteria creates certain problems for decision-makers. The use of fuzzy logic allows processing inaccurate and uncertain information, providing a more complete and accurate assessment of alternatives and evaluation of the importance of criteria. The article proposes the use of fuzzy multi-criteria decision-making methods (Fuzzy MCDM). The Fuzzy SWARA and Fuzzy TOPSIS methods allow you to present an assessment of criteria and alternatives in a descriptive (linguistic) form, which facilitates the decision-making process. In the course of the research, alternatives and criteria for their evaluation were determined, a survey of experts was conducted, evaluations of the degree of importance of criteria and evaluation of alternatives according to criteria were determined. Based on the Fuzzy SWARA and Fuzzy TOPSIS methods, a fuzzy model for selecting an optical line terminal is built. The ranking of the selected optical linear terminals was obtained and the best one was determined.

Novel Linguistic Intuitionistic Fuzzy Score and Accuracy functions for Linguistic Intuitionistic Fuzzy TOPSIS Method

Abstract: This work proposes various Linguistic Intuitionistic Fuzzy Score and Accuracy functions (LIF-Score and LIFAccuracy), including linguistic degree, membership, non-membership, and hesitancy degree, for Linguistic Intuitionistic Fuzzy Sets (LIFSs) in Multiple Attribute Group Decision Making (MAGDM) problems. The proposed functions are integrated into the Linguistic Intuitionistic Fuzzy-Technique of Order Preference by Similarity to Ideal Solution (LIF-TOPSIS) MAGDM approach to rank the alternatives, and a numerical example demonstrating their use in ranking alternatives is shown in the paper.

A Different Perspective for Determining the Optimal Treatment Modality in the Treatment of GERD: An Application of the Fuzzy TOPSIS Method

A Different Perspective for Determining the Optimal Treatment Modality in the Treatment of GERD: An Application of the Fuzzy TOPSIS Method

A group decision-making model for architectural programming in megaprojects

PurposeDue to the complexity and diversity of megaprojects, the architectural programming process often involves multiple stakeholders, making decision-making difficult and susceptible to subjective factors. This study aims to propose an architectural programming methodology system (APMS) for megaprojects based on group decision-making model to enhance the accuracy and transparency of decision-making, and to facilitate participation and integration among stakeholders. This method allows multiple interest groups to participate in decision-making, gathers various perspectives and opinions, thereby improving the quality and efficiency of architectural programming and promoting the smooth implementation of projects.Design/methodology/approachThis study first clarifies the decision-making subjects, decision objects, and decision methods of APMS based on group decision-making theory and value-based architectural programming methods. Furthermore, the entropy weight method and fuzzy TOPSIS method are employed as calculation methods to comprehensively evaluate decision alternatives and derive optimal decision conclusions. The workflow of APMS consists of four stages: preparation, information, decision, and evaluation, ensuring the scientific and systematic of the decision-making process.FindingsThis study conducted field research and empirical analysis on a practical megaproject of a comprehensive transport hub to verify the effectiveness of APMS. The results show that, in terms of both short-distance and long-distance transportation modes, the decision-making results of APMS are largely consistent with the preliminary programming outcomes of the project. However, regarding transfer modes, the APMS decision-making results revealed certain discrepancies between the project's current status and the preliminary programming.Originality/valueAPMS addresses the shortcomings in decision accuracy and stakeholder participation and integration in the current field of architectural programming. It not only enhances stakeholder participation and interaction but also considers various opinions and interests comprehensively. Additionally, APMS has significant potential in optimizing project performance, accelerating project processes, and reducing resource waste.

Selection model of chemical tanker ships for cargo types using fuzzy AHP and fuzzy TOPSIS

There are many chemical tanker ships on the market with very different characteristics. However, their operational performance for cargo types remains uncertain. Also, it is unclear which criteria should be taken into consideration primarily in the selection of chemical tanker ships. Therefore, choosing the most suitable ones for the cargo to be transported is an important problem for maritime companies. In this respect, the study aims to determine suitable chemical tanker ships for the four main cargo groups transported by them: animal and vegetable oils, corrosives (abrasives), organic chemicals, and petroleum products. A methodology that combines the fuzzy AHP, fuzzy TOPSIS, and clustering methods is used. 30 alternative chemical tanker ships are determined to represent the characteristics of all ships with a tonnage of 1–30 k DWT on the market. They are clustered into performance levels according to their fuzzy TOPSIS ranking scores. Consequently, a chemical tanker selection manual is developed based on the common characteristics of high-performance ships as well as the order of importance of criteria. It is envisaged that it would be a reference for ship operators and shipowners in the selection of suitable ships for the type of cargo.

A new MCDM integrating fuzzy rough set and TOPSIS method

A new MCDM integrating fuzzy rough set and TOPSIS method

Integration of Fuzzy Sets, AHP, and TOPSIS Methods for Estimation of Liquefaction Potential Zones in Wates Groundwater Basin, Kulon Progo, Special Region of Yogyakarta

Abstract In regional development planning, geological, environmental, and socio-economic conditions must be considered. Among the crucial things is the potential for disasters in the region. The Wates Groundwater Basin area has great regional development potential, starting from the establishment of the New Yogyakarta International Airport (NYIA) to the Southern Causeway (JJLS) construction. Various developments are predicted to continue to grow along with the development of areas targeted by the government for this area so the mapping of potential disasters is necessary as a means of achieving Sustainable Development Goals (SDGs). One of the potential disasters in CAT Wates is a liquefaction disaster, which is supported by geological, hydrogeological, and seismic data in this area which is prone to liquefaction. To confirm this potential, this research was conducted using geological and hydrogeological data from previous studies. The result of this research is a more detailed and objective zoning map of the liquefaction potential even though it uses minimal data. This is done by integrating the Fuzzy Sets, AHP, and TOPSIS methods, while simultaneously validating the result using the Cone Penetration Test (CPT) data. The results show that the eastern and southern regions of the basin which are composed of river deposits and coastal deposits, respectively, have a high to very high relative vulnerability to liquefaction, while the central region of the basin has a low to moderate relative vulnerability, and the northern region of the basin tends to have relatively very low vulnerability. That result is aligned with the total thickness of the liquefiable layer from CPT data. It indicates that the combined method is proven to be reliable for liquefaction susceptibility spatial mapping. Thus, the result hopefully can be used as a reference for land use planning in the Wates Groundwater Basin area.

From responsible sourcing of wastes to sustainable energy consumption in the blue hydrogen supply chain: Case of nearshoring in Nuevo Leon

Despite global uncertainties, Mexico has become the largest supplier to the United States, emphasizing its growing role in cultivating resilient supply chains. Nearshoring practices from Mexico and Canada to the U.S. present new production opportunities. Increasingly, U.S. consumers place a premium on sustainable consumption, so blue hydrogen plants must be sourced responsibly. This paper firstly goes beyond economic motivations, aiming to formulate a multi-stage methodology for assessing suppliers of municipal solid waste for blue hydrogen plants, marking a pioneering effort in catering to the environmentally conscious preferences of U.S. consumers. The purpose of this work is to examine a case study of Blue Hydrogen production in Nuevo Leon, emphasizing the importance of developing responsible sourcing criteria for waste suppliers to limited blue hydrogen plants. The developed hybrid approach integrates various methodologies, including Critical Systems Heuristics (CSH), fuzzy rough sets, hierarchical fuzzy rough analysis, Fuzzy rough WASPAS, TOPSIS, and Best-Worst Method (BWM), providing a comprehensive framework for decision-making under uncertainty. Key findings include the identification of water conservation (E2) and job creation (S2) as the top environmental and social criteria, respectively. Supplier WC2 was identified as the top-performing waste collector, achieving a final score of 4.77. This structured methodology prioritizes product categories for hydrogen consumption, considering sustainability criteria and U.S. consumer preferences, thereby offering valuable insights for businesses navigating this evolving landscape.

Enhancing Contractor Selection through Fuzzy TOPSIS and Fuzzy SAW Techniques

Contractors play an integral role in construction projects, and their qualifications directly impact various aspects of a project’s success. The unbiased selection of contractors is a challenge in the construction industry worldwide, particularly in public projects where impartiality in the final selection is essential. Numerous factors must be considered when evaluating contractors, making the selection process challenging for the human brain. This paper introduces and compares two methods for assessing contractor prequalification by applying the fuzzy theory. The idea is to facilitate using human judgments in a mathematical system for decision-making with regard to selecting contractors. The method is based on identifying a fuzzy weight for the selection criteria using the Buckley method. Fuzzy TOPSIS and Fuzzy SAW methods are then used for the qualification ranking of the contractors. The proposed models are assessed using a case study. A sensitivity analysis was also conducted to compare the two models. The introduced method is expected to improve the quality of the qualification-based selection of contractors and prevent possible losses from hiring unsuitable contractors.

T-Spherical fuzzy TOPSIS method based on distance measures and Hamacher Heronian mean averaging aggregation operators and its application to waste management

T-Spherical fuzzy sets provide a robust framework for handling uncertainty in real-world decision-making. This paper addresses the construction of T-Spherical fuzzy Heronian mean averaging aggregation operators using novel Hamacher operations. The study mathematically verifies the properties of these operators and develops new distance measures such as Hamming, Euclidean and Jaccard for quantifying dissimilarity between T-Spherical fuzzy numbers. Furthermore, the TOPSIS method is implemented using the proposed operators and distance measures to solve a practical decision-making problem in selecting the best transportation firm for medical waste disposal. A sensitivity analysis is made to assesses the efficacy of the proposed operators, while a comparison study with existing methods is exhibited to establish their efficiency.

Type-2 Fuzzy ANP and TOPSIS methods based on trapezoid Fuzzy number with a new metric

Type-2 Fuzzy ANP and TOPSIS methods based on trapezoid Fuzzy number with a new metric