Fuzzy Analytic Hierarchy Process Research Articles

Development and evaluation of a novel framework to enhance k-NN algorithm’s accuracy in data sparsity contexts

This paper presents a novel framework for implementing the k-NN algorithm, designed to enhance its accuracy in contexts with sparse data. The framework addresses limitations in the algorithm’s training process by optimizing data structures. It employs composite datasets generated from the initial data using a data-driven fuzzy Analytic Hierarchy Process weighting scheme. This approach is designed to enhance the informational content in the initial datasets, thus reducing the entropy and implementation uncertainty. The framework was evaluated using 75 publicly available datasets and 3 generated datasets, demonstrating significant accuracy improvements across various k-parameter values. The findings were rigorously generalized using non-parametric hypothesis tests; while the resulting sensitivity was assessed by applying different distance metrics. By enhancing informational content, the composite data structures contribute to both accuracy improvements and scalability, particularly in data-sparse contexts. This relationship underscores the critical role of entropy in enhancing the performance of explainable machine learning algorithms, providing a valuable and interpretable tool for transforming data structures in sparse data environments.

Enhancing Economic, Resilient, and Sustainable Outcomes Through Supplier Selection and Order Allocation in the Food Manufacturing Industry: A Hybrid Delphi-FAHP-FMOP Method

In the food manufacturing industry, which is critical to national economies, there is a growing imperative to meet heightened safety, quality, and environmental standards, particularly in the face of supply chain disruptions. This study addresses the gap in literature by integrating sustainable and resilient supply chain theories with risk management and low-carbon principles into a supplier selection framework. Utilizing the Delphi method, fuzzy analytic hierarchy process (FAHP), and fuzzy multi-objective programming (FMOP), we develop a decision-making model specifically calibrated for the food sector. Initially, the study establishes a comprehensive criteria system encompassing quality, cost, delivery, low-carbon, and risk management through a literature review and expert consultation. Subsequently, FAHP is employed to determine the relative importance of each criterion in supplier selection. Furthermore, FMOP is utilized to develop a decision-making model for optimizing supplier selection and order allocation. Validated through a numerical study based on a Chinese food manufacturer, the framework presents a practical tool for food manufacturers, ensuring supply chain stability while aligning with sustainability objectives. This research refines decision making and strengthens the competitive stance of food manufacturers, significantly propelling the industry’s green transformation.

Integrated Approach Using Intuitionistic Fuzzy Multicriteria Decision-Making to Support Classifier Selection for Technology Adoption in Patients with Parkinson Disease: Algorithm Development and Validation.

Parkinson disease (PD) is reported to be among the most prevalent neurodegenerative diseases globally, presenting ongoing challenges and increasing burden on health care systems. In an effort to support patients with PD, their carers, and the wider health care sector to manage this incurable condition, the focus has begun to shift away from traditional treatments. One of the most contemporary treatments includes prescribing assistive technologies (ATs), which are viewed as a way to promote independent living and deliver remote care. However, the uptake of these ATs is varied, with some users not ready or willing to accept all forms of AT and others only willing to adopt low-technology solutions. Consequently, to manage both the demands on resources and the efficiency with which ATs are deployed, new approaches are needed to automatically assess or predict a user's likelihood to accept and adopt a particular AT before it is prescribed. Classification algorithms can be used to automatically consider the range of factors impacting AT adoption likelihood, thereby potentially supporting more effective AT allocation. From a computational perspective, different classification algorithms and selection criteria offer various opportunities and challenges to address this need. This paper presents a novel hybrid multicriteria decision-making approach to support classifier selection in technology adoption processes involving patients with PD. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) was implemented to calculate the relative priorities of criteria and subcriteria considering experts' knowledge and uncertainty. Second, the intuitionistic fuzzy decision-making trial and evaluation laboratory (IF-DEMATEL) was applied to evaluate the cause-effect relationships among criteria/subcriteria. Finally, the combined compromise solution (CoCoSo) was used to rank the candidate classifiers based on their capability to model the technology adoption. We conducted a study involving a mobile smartphone solution to validate the proposed methodology. Structure (F5) was identified as the factor with the highest relative priority (overall weight=0.214), while adaptability (F4) (D-R=1.234) was found to be the most influencing aspect when selecting classifiers for technology adoption in patients with PD. In this case, the most appropriate algorithm for supporting technology adoption in patients with PD was the A3 - J48 decision tree (M3=2.5592). The results obtained by comparing the CoCoSo method in the proposed approach with 2 alternative methods (simple additive weighting and technique for order of preference by similarity to ideal solution) support the accuracy and applicability of the proposed methodology. It was observed that the final scores of the algorithms in each method were highly correlated (Pearson correlation coefficient >0.8). The IF-AHP-IF-DEMATEL-CoCoSo approach helped to identify classification algorithms that do not just discriminate between good and bad adopters of assistive technologies within the Parkinson population but also consider technology-specific features like design, quality, and compatibility that make these classifiers easily implementable by clinicians in the health care system.

An Integrated Approach to Constraints Theory and Fuzzy Analytical Hierarchy Process (FAHP) in Construction Projects (Case Study)

The research endeavors to harness the benefits stemming from the integration of constraint theory into construction project management, with the primary goal of mitigating project completion delays. Additionally, it employs fuzzy analysis to determine the relative significance of fundamental constraints within projects by assigning them appropriate weights. The research problem primarily revolves around two key issues. Firstly, the persistent utilization of outdated methodologies and a heavy reliance on workforce experience without embracing modern computerized technologies. Secondly, the recurring problem of project delivery delays. Construction projects typically encompass five fundamental constraint types: cost restrictions, time constraints, administrative and legal limitations, technical and design constraints, and environmental constraints. To address these issues, a field study was conducted, focusing on the development of a housing complex in Qa'imaqamiya, Al-Mada'in district, Bismayah. The researchers propose a three-stage methodology to tackle these challenges. In the initial stage, the theory of accelerated activities is applied to all concurrent implementable tasks. Subsequently, the second stage introduces a hierarchical analysis process, known as FAHP, to quantify the importance of each constraint. Finally, in the third phase, fuzzy logic is employed to calculate the projected duration and associated costs for each activity, ensuring project execution and delivery within agreed-upon timeframes. After applying the proposed methodology, there is a reduction in completion time by up to (23%). Research Type: Research Paper

Penentuan Penentuan Prioritas Lokasi Fasharkan TNI AL dengan Metode Fuzzy Analytical Hierarchy Process dan Geographic Information System

Based on the regulation of the Navy Chief of Staff No. Per Kasal/39/V/2009 dated May 26, 2009 concerning the development policy of the Navy towards minimum essential force, namely the preparation of the Navy's combat power is directed to achieve minimum essential force or abbreviated as MEF. According to the Chief of Staff of the Navy Number Kep/1771 / XII / 2013 concerning the Administrative Guidebook for the Standardization of TNI AL Bases (PUM-7.03) that a TNI AL Main Base (LANTAMAL) must have a class A Fasharkan, whose ability is to be able to carry out maintenance and repair up to the depot level for all types of KRI, both ship buildings, machinery, sewaco, electricity and ship armaments. There are many studies of location prioritization using the Analytical Hierarchy Process (AHP) method, but the results have uncertainty or decision uncertainty. So this research was conducted using the Fuzzy AHP method to overcome uncertainty or decision uncertainty. The Fuzzy AHP method is used for calculations to select alternatives. After the alternative is selected, then verification of location feasibility is carried out using the Geographic Information System (GIS) display. From the calculation using the FAHP method, Lantamal IX Ambon was selected as an alternative with a weight value of 0.251630342. While verifying the feasibility of the location using GIS, the Tawiri location was chosen as a more feasible land compared to other land options.

Development of a total quality management framework for the mobile network operations Centre

Telecommunication is one of the most fast-paced and rapidly changing sectors globally. This study develops a total quality management (TQM) framework for mobile network operation centres (NOC) to aid in network surveillance and fault management processes for a 5G transmission network. A systematic literature review was conducted using academic articles in journals and conference papers sourced from the EBSCOHost database. A fuzzy analytic hierarchical process (FAHP) was used as a multi-criteria decision-making tool to determine which criteria and factors are of the highest ranking for the development of the TQM framework. Data collection and control sheets were ranked higher, making them critical to the framework. This shows that, without good data, no problems can be identified, visualised, analysed, and solved and processes will not be improved. Process improvement is ranked last, showing it is of the lowest priority compared to the other criteria. This however, does not imply that in TQM it is of lower priority, as it is needed to keep meeting the needs of the customers. The framework includes how the TQM tools and techniques can be used in collecting, visualising, analysing, solving problems, and improving processes.

Multi-Criteria Decision-Making Approach for Optimal Energy Storage System Selection and Applications in Oman

This research aims to support the goals of Oman Vision 2040 by reducing the dependency on non-renewable energy resources and increasing the utilization of the national natural renewable energy resources. Selecting appropriate energy storage systems (ESSs) will play a key role in achieving this vision by enabling a greater integration of solar and other renewable energy. ESSs allow for solar power generated during daylight hours to be stored for use during peak demand periods. Additionally, the proposed framework provides guidance for large-scale ESS infrastructure planning and investments to support Oman’s renewable energy goals. As the global renewable energy market grows rapidly and Oman implements economic reforms, the ESS market is expected to flourish in Oman. In the near future, ESS is expected to contribute to lower electricity costs and enhance stability compared to traditional energy systems. While ESS technologies have been studied broadly, there is a lack of comprehensive analysis for optimal ESS selection tailored to Oman’s unique geographical, technical, and policy context. The main objective of this study is to provide a comprehensive evaluation of ESS options and identify the type(s) most suitable for integration with Oman’s national grid using a multi-criteria decision-making (MCDM) methodology. This study addresses this gap by applying the Hesitate Fuzzy Analytic Hierarchy Process (HF-AHP) and Hesitate Fuzzy VIKOR methods to assess alternative ESS technologies based on technical, economic, environmental, and social criteria specifically for Oman’s context. The analysis reveals pumped hydro energy storage (PHES) and compressed air energy storage (CAES) as the most appropriate solutions. The tailored selection framework aims to guide policy and infrastructure planning to determine investments for large-scale ESSs and provide a model for comprehensive ESS assessment in energy transition planning for countries with similar challenges.

Analysing the feasibility of agro-based value chain configurations in the rural context with B2B marketing

Purpose This paper aims to advocate for small-scale entrepreneurship with commercial cultivation to earn better profits at the farmer level. This paper explores potential value chain configurations of vertical coordination. Design/methodology/approach The aloe vera supply chain was examined for the study where aloe vera juice is the product. The various configurations are complete vertical coordination, semi-vertical coordination and non-vertical coordination. These were analysed to find feasibility and market prospects based on parameters. The cyclic view of supply chains, comparative analysis and fuzzy-analytical hierarchy process algorithm are presented. Findings The semi-vertically coordinated supply chain is indicated as the optimum setup to initiate value chains. The entrepreneurs in this configuration will be able to sell semi-finished produce to a brand that will process and distribute the final product. This B2B setup will help the farmer to earn better revenues in the post-COVID-19 scenario. Effective marketing with improved execution and innovation will boost the food processing industry. Research limitations/implications This study uses an established methodology to distinguish among alternatives based on underlying factors. The study is based on data collected from stakeholders. The study reflects the most accurate picture of real-world scenarios at the farm and market level in the Indian context. Practical implications This study’s learnings will help understand the organisational approach to serve demand by designing unique business configurations. Adapting the fundamental model with technological intervention will improve supply chain performance and sustainability. The most modern approaches in agri-business, such as subscription models, vertical farming, corporate entrepreneurship and technological intervention, will be covered in future work. Social implications This study will help stakeholders, including the government, decide whether to introduce training skills, subsidy policies, business expansion ideas and feasibility studies. Originality/value The study highlights the strategic role of developing the B2B platform to support customer values and customer ecosystem with better product availability.

Application of multi-criteria decision making (MCDM) model for solar plant location selection

This study aims to systematically evaluate and identify the most suitable locations for establishing solar farms in the United Kingdom. It employs a comprehensive Multi-Criteria Decision-Making (MCDM) model that incorporates the Fuzzy Analytic Hierarchy Process (FAHP) method. Eight potential locations have been identified and selected for their climatic conditions and other pertinent parameters. These potential locations underwent additional assessment considering several factors including temperature, annual hours of sunshine, absolute humidity, proximity to major roads, distance from the power network, and potential demand. The data was normalized to eliminate scale effects and improve comparability. A MATLAB program has been developed with universality and user-friendliness in mind. Its purpose is to establish location selection factors graphs according to application-specific criteria and generate the matrix representation of these graphs for each potential candidate location. The permanent matrix, a composite metric synthesizing six criteria, identifies Torquay as the optimal location with a permanent value of 18.16. This quantitative approach, intersected with qualitative assessments, presents a clear hierarchy of site suitability. The model's simplicity, scalability, and proven accuracy make it a valuable tool for assessing renewable energy project viability in diverse regions.

Development of a fuzzy analytic hierarchy process model, sensitivity analysis, and addressing barriers to additive manufacturing implementation in small- and medium-sized enterprises (SMEs)

Additive manufacturing (AM) is gaining popularity worldwide due to its potential to revolutionize manufacturing processes, improve product customization, and reduce waste. Nevertheless, small- and medium-sized enterprises (SMEs) encounter major challenges when implementing AM technologies. The objective of this research is to utilize the fuzzy analytic hierarchy process (FAHP) to identify and prioritize the critical challenges that impede the implementation of AM for SMEs. The study employs a two-stage methodology. First, a thorough literature review and expert discussions identify 15 major barriers to AM adoption. Second, a systematic model is proposed to establish a ranking of these barriers, which is based on the FAHP. The results are validated using sensitivity analysis to ensure the robustness of the findings. Additionally, potential solutions to overcome these barriers are discussed. The research indicates that the two most significant barriers for SMEs are the high costs of equipment and the scarcity of skilled designers. The model that has been presented is a valuable resource for SME managers and practitioners, as it allows them to make strategic and informed decisions that facilitate the efficient implementation of AM. This research is groundbreaking in its comprehensive analysis of the prioritization and ranking of AM implementation barriers, with a particular emphasis on SMEs. It offers essential insights for future research and practical applications.

A Risk Assessment Model of Gas Pipeline Leakage Based on a Fuzzy Hybrid Analytic Hierarchy Process

Given the rising urban demand for gas, it has emerged as a primary energy source for urban activities and daily life. However, China’s urban gas pipeline network has witnessed a surge in accidents, leading to significant losses and disasters. Therefore, it is particularly necessary to study the disaster risk assessment model caused by urban gas pipeline leakage. There are some problems in the previous evaluation methods, such as less consideration of the influence relationships between disaster factors. To redress this issue, a novel fuzzy hybrid analytic hierarchy process evaluation methodology is proposed. First, a hybrid hierarchical risk assessment model is developed by combining the analytic hierarchy process and the network analytic hierarchy process. Membership matrices and impact matrices are utilized to calculate comprehensive factor weights. This approach enhances the understanding of relationships between risk factors within the hierarchical structure model. Subsequently, employing a fuzzy evaluation method, the risk level matrix is derived by using multiplication and bounded operators to ascertain the risk level state. This solves the problem of the fuzzy boundaries when measuring the index factors of the gas pipeline network. Finally, experimental analysis is carried out on the gas pipeline network in the central area of a city and validates the model’s accuracy in practical applications.

Sustainable Supplier Selection Criteria for HVAC Manufacturing Firms: A Multi-Dimensional Perspective Using the Delphi–Fuzzy AHP Method

Sustainable Supplier Selection Criteria for HVAC Manufacturing Firms: A Multi-Dimensional Perspective Using the Delphi–Fuzzy AHP Method

Identification of Supply Chain Disruption Risks and Strategies in the Electric Vehicle Industry Based on Fuzzy Analytic Hierarchy Process (FAHP)

In recent years, the electric vehicle industry has witnessed unprecedented development opportunities. However, with rapid technological iterations and fluctuating market demands, the vulnerability and complexity of the electric vehicle supply chain have significantly increased the potential risk of supply chain disruptions. Against this backdrop, this study adopts the Fuzzy Analytic Hierarchy Process (FAHP) to systematically analyze the risks of supply chain disruptions in the electric vehicle industry. By constructing a judgment matrix through industry data surveys and expert scoring, and conducting consistency checks, the weights of various risk factors are determined. The study finds that technological quality risk, demand forecast deviation, and logistics disruption are the primary factors affecting the stability of the electric vehicle supply chain. Additionally, although the probability is relatively low, the potential destructiveness of exogenous risks is equally undeniable. Accordingly, the study will propose targeted management strategies to enhance the resilience of the new energy vehicle supply chain at both the enterprise and industry levels in response to these risks.

Development of decision support methods for efficient maintenance of urban wastewater systems

The sanitation network is a vital urban infrastructure that requires systematic and co-ordinated activities and practices to realize lowest life cycle cost. The sustainable management of this asset is based on the efficient acquisition of diagnostic data from the sanitation network in order to analyze the defects of each section, then to assess their condition to prioritize and consider the effective mode of maintenance of sections deemed failing. In this perspective, knowledge of the sanitation networks is essential for reliable classification of defects and effective maintenance. A numerical model based on weighted criticality is developed to evaluate and prioritize the defects in an urban sanitation network. It is based on the combination of two methods: Fuzzy Analytic Hierarchy Process (F-AHP) and Weighted Product Model (WPM). A case study of a real sanitation network in Bejaia city (Algeria), consisting mainly of non-visitable pipes evaluated from television inspections, is used to illustrate and validate the proposed model. The results show that the weighting of criticality parameters has a considerable influence on the classification of defects in a sanitation network. The weighted criticality results obtained in the present case are significantly better than those obtained using a method based on the notion of classical criticality. This clearly indicates the relevance of the proposed numerical model.

AI empowered academia: a fuzzy prioritization framework for academic challenges

Purpose The rapid expansion of artificial intelligence (AI) is progressively reshaping the dynamics of human interaction, communication, lifestyle, education and professional endeavors. The purpose of the study is to comprehend and address the barriers which are impeding the implementation of Generative AI Technologies, such as ChatGPT in the educational landscape. Design/methodology/approach The study used the Fuzzy analytic hierarchy process (AHP) model to analyze the responses gathered from 149 academicians belonging to the northern states of India. Findings The study established that the three most important criteria that influence the adoption of generative AI in the education sector are Risk of Academic Integrity, Risk of biased outcomes and Erosion of Critical Thinking. Research limitations/implications The present study was confined to Fuzzy AHP to extract the critical criteria influencing the decision-making. Various other techniques such as PF-Delphi and PF-CoCoSo can be used further. The results provide significant inputs for future research to understand the effect of adoption of Generative AI in different contexts including both opportunities and the challenges faced by them. Practical implications The study will be beneficial to various stakeholders including students, educators, society and policymakers as the study will highlight the importance of AI tools, introduce the various challenges associated with and explain the use of these tools as productivity-enhancing tools. Originality/value To the best of the author’s knowledge, the present study is a novice as the use of AI in Academia is unexplored and the major criteria influencing the choices have yet been undiscovered.

Enhancing Talent Retention in Work 4.0 Era: An Improved Fuzzy Analytical Hierarchy Process and Fuzzy Decision-Making Trial and Evaluation Laboratory Methodology

Enhancing Talent Retention in Work 4.0 Era: An Improved Fuzzy Analytical Hierarchy Process and Fuzzy Decision-Making Trial and Evaluation Laboratory Methodology

Retraction Note: Distance education tool selection using novel spherical fuzzy AHP EDAS

Retraction Note: Distance education tool selection using novel spherical fuzzy AHP EDAS

Urban–Rural Continuum in the Gonda District, India: Quantifying Rurality Using Modified Fuzzy AHP

Urban–Rural Continuum in the Gonda District, India: Quantifying Rurality Using Modified Fuzzy AHP

Evaluation of the Critical Success Factors for Household Product Sustainability

Introduction: Sustainability and sustainable development have received growing at-tention in both industry and academia due to concerns regarding the rapid decrease in natural resources and increase in carbon emissions. Methods: In this study, we focus on the determination, evaluation, and analysis of the critical success factors in product sustainability by specifically focusing on the household goods indus-try. In the first phase of the study, we determine the critical success factors by referring to the existing literature and opinions of the experts who have experience in the household goods in-dustry. Next, we use a trapezoidal type-2 fuzzy AHP algorithm to rank the determined criteria and discuss the main findings from a practical point of view. Results: Computational results bring several important managerial insights. First, we observe that all three aspects of sustainability (economic, environmental, and social) should be consid-ered to ensure product sustainability. Second, the analysis reveals that cost (economic), quality (economic), generated waste and emission during the life cycle (environmental), energy and water consumption during the life cycle (environmental), and occupational health and safety (social) are among the highly ranked criteria. Conclusion: In this regard, in order to increase product sustainability, the companies should de-termine ways to decrease water usage, energy usage, carbon emission, and waste without ne-glecting the cost and quality of the product and without ignoring occupational health and safety.

Selection of Health Insurance Policy: Using Analytic Hierarchy Process and Combined Compromised Solution Approach Under Spherical Fuzzy Environment

The process of health insurance policy selection is a critical decision with far–reaching financial implications. The complexity of health insurance policy selection necessitates a structured approach to facilitate informed decision-making amidst numerous criteria and provider options. This study addresses the health insurance policy selection problem by employing a comprehensive methodology integrating Spherical Fuzzy Analytic Hierarchy Process (SF–AHP) and Combined Compromise Solution (CoCoSo) Algorithm. Eight experienced experts, four from academia and industry each, were engaged, and eleven critical factors were identified through literature review, survey, and expert opinions. SF–AHP was utilized to assign weights to these factors, with Claim settlement ratio (C9) deemed the most significant. Subsequently, CoCoSo Algorithm facilitated the ranking of insurance service providers, with alternative A6 emerging as the superior choice. The research undertakes sensitivity analysis, confirming the stability of the model across various scenarios. Notably, alternative A6 consistently demonstrates superior performance, reaffirming the reliability of the decision-making process. The study’s conclusion emphasizes the efficacy of the joint SF–AHP and CoCoSo approach in facilitating informed health insurance policy selection, considering multiple criteria and their interdependencies. Practical implications of the research extend to individuals, insurance companies, and policymakers. Individuals benefit from making more informed choices aligned with their healthcare needs and financial constraints. Insurance companies can tailor policies to customer preferences, enhancing competitiveness and customer satisfaction. Policymakers gain insights to inform regulatory decisions, promoting fair practices and consumer protection in the insurance market. This study underscores the significance of a structured approach in navigating the intricate health insurance landscape, offering practical insights for stakeholders and laying a foundation for future research advancements.