This study compares two AHP-based approaches for defining criteria weights in occupational hearing risk assessment: Gaussian AHP, which incorporates statistical variability into the weighting process, and the AHP with objective weights, which reduces subjective judgment by deriving weights from observable quantitative data. The study evaluates the consistency and applicability of both methods in a real decision-making scenario involving combined occupational exposures. The methods were applied to real data from a maintenance workshop, considering six risk criteria: noise, heat, vibration, ototoxic solvents, heavy metals, and asphyxiating substances. The weights calculated by each approach were applied to a decision matrix composed of four Similar Exposure Groups (SEGs), allowing comparison of the resulting rankings and consistency. The results showed that both methods produced consistent rankings and demonstrated strengths in terms of simplicity, reproducibility, and reduced dependence on subjective judgments. The study contributes to the literature by empirically comparing objective AHP-based approaches in the context of occupational hearing health and by supporting the prioritization of workers exposed to combined risk factors in industrial environments.
In various strategic decision-making problems, a phased decision-making framework is often required, for example, in planning the development of the coconut agroindustry. Therefore, this study aims to determine the priority of coconut processing industry types that are feasible to develop in the short-term and to identify the optimal locations for the development of an integrated coconut agroindustry in the long-term. This study uses an MCDM approach through the integration of the AHP, entropy, and TOPSIS methods. The AHP is used to obtain criterion weights based on expert assessment, while the entropy method produces objective weights based on empirical data variation. The integration of these two approaches allows for the combination of subjective and objective information, resulting in more balanced criterion weights. Furthermore, the TOPSIS method was applied to determine the final priority of industrial alternatives and development locations. The analysis results showed that coconut-based animal feed industry was the most feasible priority alternative to be developed in the short-term. Meanwhile, Binuang sub-district was identified as the optimal location for integrated coconut agroindustry development in the long-term. The relationship between industrial and location decisions was interpreted through a conceptual network-based decision-making perspective to reflect the interdependence and feedback between decisions. Based on this approach, this study proposes a phased decision support framework that integrates industrial priorities and location selection into an integrated analytical structure, and has the potential to be applied in various contexts of agroindustry development planning for various commodities, regions, and other multi-stage strategic decisions.
The cold chain is crucial for maintaining the quality of raw tuna materials and products. The assessment of the tuna cold chain sustainability performance is frequently disjointed between the upstream (fish catchers) and downstream (agro-industry) sectors. This study seeks to integrate a performance evaluation at both levels utilizing the SCOR DS (Supply Chain Operations Reference Digital Standard) framework, the Analytic Hierarchy Process (AHP) for SCOR DS metric weighting, and fuzzy AHP for the weighting of performance enhancement measures. A performance assessment was conducted on resilience, economic efficiency, and sustainability. The results of the literature review and observations revealed 24 metrics for the agro-industry and 19 metrics for fish catchers. These metrics correspond to the SCOR DS properties. The choice of measures is grounded in the scholarly literature and expert consensus. The metric weights were generated using the AHP approach based on expert evaluations. The findings indicate that agro-industry and fish catchers cold chain metrics exhibit performance levels of 69.99% and 74%, respectively, with the sustainability performance category was classified as moderate. Low performance criteria include downgrades, value addition, market direction flexibility (MDF), and return on fixed asset (ROFA). The fuzzy AHP analysis results indicated that options for enhancing low performance metrics include partnerships and competency development. Consequently, comprehensive research underscores the need for collaboration among stakeholders in improving the overall supply chain efficacy. This study enhances a comprehensive framework for the integration of multi-level performance within the tuna supply cold chain. Its constraints pertain to data acquisition within a confined temporal framework and geographical scope. The significance of this research is the utilization of the AHP and fuzzy AHP to develop a weighted framework for performance measurement indicators and an integrated SCOR DS performance enhancement strategy at the agro-industry and fish catchers levels.
The safety and efficiency of air traffic operations depends heavily on air traffic controllers, making the accurate identification of suitable candidates a critical challenge for air navigation service providers. The selection process is traditionally based on standardized aptitude testing combined with structured interviews and expert evaluation, which may be affected by subjectivity and require substantial time and resources. This study introduces a machine learning based decision support approach for air traffic controller (ATCO) candidate selection. The candidate evaluation task is formulated as a binary classification problem to predict interview outcomes using historical data from an actual selection process involving 194 candidates, including 30 who passed the interview phase. Three machine learning models, Logistic Regression (LR), Support Vector Machine (SVM), and Decision Tree (DT), are trained using candidate background variables such as prior examination scores, type of high school attended, and high school grade point average. The performance of the models is evaluated in comparison with existing approaches based on the Analytic Hierarchy Process and the Analytic Network Process. The results of this study provide preliminary evidence that, in the context of ATCO selection, machine learning models may approximate the outcomes of traditional interviews, with LR achieving an accuracy of 93%, and both DT and SVM achieving accuracies of 90%. Using the best-performing model, 46 eliminated candidates were correctly classified as eliminated (true negatives) and 9 selected candidates were correctly identified as selected (true positives), while 4 eliminated candidates were incorrectly classified as selected (false positives). These results suggest that the proposed approach may support air navigation service providers and related institutions by assisting early-stage screening through data driven prioritization of ATCO candidates.
The Siau Tagulandang Biaro Islands Regency (Sitaro) in Indonesia, rich in natural volcanic resources, presents significant potential for sustainable tourism development. However, post-COVID-19 tourism growth poses challenges in attracting visitors amidst stiff competition. This study aims to design a comprehensive, participatory tourism development strategy for the region, integrating community and expert insights. A mixed-methods approach was employed, combining Focus Group Discussions (FGD), SWOT analysis, and the Analytical Hierarchy Process (AHP) to prioritize key development factors. Sensitivity analysis was conducted to assess the stability and robustness of the results. Subsequently, Quality Function Deployment (QFD) and the House of Quality (HoQ) methods were used to translate these prioritized factors into actionable strategies. The study identifies eight operational strategies, with citizens' awareness, performance, and institutional development emerging as the top priorities. This research offers valuable insights for policymakers and stakeholders aiming to foster resilient and inclusive tourism growth.
The study aims to prioritize the tourism development strategy of Quang Ngai province after the merger period. In the context of multidimensional tourism development, while facing many challenges in infrastructure, human resources and finance, choosing suitable and feasible strategies is an urgent requirement to promote the sustainable development of the local tourism industry. The research method combines three analytical tools: a SWOT analysis helps identify the strengths, weaknesses, opportunities, and challenges of the Quang Ngai tourism industry; the AHP is used to allocate weights to SWOT criteria to show the relative importance of each factor; and TOPSIS is applied to rank development strategies based on the evaluation scores of experts, helping to select the optimal strategy based on the degree of closeness to the ideal solution. Data collected from tourism and management experts were systematically aggregated and analyzed using the integrated SWOT–AHP–TOPSIS framework to ensure consistency and reliability in the evaluation process. The research results show that the top priority strategies focus on developing transport infrastructure, socializing investment resources, and improving the quality of accommodation facilities. At the same time, the development of green, environmentally friendly tourism models and enhancing tourism brand promotion are also highly appreciated. These strategies reflect the consensus between internal and external factors and are consistent with the goal of sustainable development of the local tourism industry. The research contributes by providing a scientific basis for policymaking and orientation for tourism development in Quang Ngai in the new period and at the same time serves as a reference model for localities with similar conditions.
Maritime transportation enables the transportation of large quantities of goods across the globe at low costs. A significant portion of global transportation takes place via sea routes. For this reason, maritime transportation also holds an important place in the global economy. However, the widespread use of traditional fossil fuels in the maritime sector leads to the emission of harmful substances such as carbon dioxide (CO₂), nitrogen oxides (NOx), and sulfur oxides (SOx) into the environment, posing a threat to human health and marine life. In order to minimize these effects, the maritime sector needs a sustainable transformation. International regulations, the limited availability of fossil fuels, high fuel costs, and societal pressures have increased the shift towards alternative fuels. In this study, fuel alternatives used in the maritime sector such as heavy fuel oil (HFO), marine diesel oil (MDO), liquefied natural gas (LNG), liquefied petroleum gas (LPG), electricity, hydrogen, methanol, and ammonia are evaluated in terms of sustainability using integrated AHP VIKOR methods. According to the obtained results, electricity has been identified as the most suitable alternative for achieving sustainability in the maritime sector due to its high energy efficiency, low carbon emissions, and ease of maintenance. Hydrogen, methanol, and ammonia, which stand out with their zero carbon emission feature, are considered among the alternative fuels that can be evaluated in the long term. Due to their ability to reduce negative environmental impacts, improve energy efficiency, and be economically viable in the long run, alternative fuels come to the forefront in sustainable maritime transportation. In this context, the study contributes to the promotion of sustainability in the maritime sector.
This study aims to identify the priority ranking of zakat performance risks in South Sulawesi, Indonesia, using both qualitative and quantitative research methods. The qualitative stage was conducted through a systematic literature review and direct consultation with an expert panel consisting of academics, regulators, and zakat practitioners to determine the relevant risk criteria. Subsequently, structured expert judgments were employed to collect pairwise comparison data among risks, after which the Analytic Hierarchy Process (AHP) was combined with a risk matrix approach. The results indicate that there are twelve risk criteria categorized into three main dimensions: internal, external, and systemic risks, which are ranked according to their priority levels based on a composite risk index. The findings show that two risks fall into the high-risk group (H), characterized by high impact and high probability, namely low public zakat literacy and the absence of integrated muzaki-mustahik data. A second group consists of several risks classified as medium-high and medium risk levels, mainly related to internal organizational capacity and governance, with risk scores ranging between moderate impact and likelihood. The remaining risks are categorized into low and very low risk groups, indicating relatively lower immediate threats to zakat performance. This study provides practical value for zakat regulators and zakat management organizations operating under limited resources, as it offers a structured basis for prioritizing risk mitigation strategies and improving the overall effectiveness and sustainability of the zakat system.
Weldability is a critical factor in the fabrication of various grades of stainless steel used in industries. Tungsten Inert Gas (TIG) welding, known for its precision and high-quality results, is frequently employed in industrial applications. However, one of the common challenges with TIG welding is achieving adequate penetration of weld joints, despite the flexibility to modify welding parameters. A suitable combination of welding parameters such as current, inert gas flow rate and root gap can increase heat input and improve penetration depth, leading to a significant enhancement in the quality of weld joints and its productivity. This, in turn, ensures better performance and long-term reliability. In this work, the welding process of 304H austenitic stainless steel is tried to be optimized, that is known for its strength and corrosion resistance in boiler components. The experiment is designed using Response Surface Methodology (RSM) with Central Composite Design (CCD), considering three factors: heat input, root gap and gas flow rate. Each factor is tested at three different levels to understand their effect on the quality of the welds, aiming primarily for full penetration, which is crucial for creating a strong joint. A total of 14 samples of 8mm thick 304H stainless steel plates, each measuring 50 mm by 50 mm, are welded using Gas Tungsten Arc Welding (GTAW), popularly known as TIG welding, with similar filler material to improve the overall weld quality. In this study, the Analytical Hierarchy Process (AHP), a multi-criteria decision-making tool, is employed to validate the optimal values determined. The results obtained from the research align well with the AHP results.
This article explores the barriers to big data adoption and proposes strategic solutions for improving disaster risk resilience to overcome these barriers in humanitarian supply chains (HSCs). Its theoretical model is grounded on the resource-based view (RBV). Based on a combination of a literature review and interviews with experts from humanitarian organizations, 24 barriers to big data adoption were identified. These barriers span infrastructural, technological, managerial, financial, and human-related factors within the humanitarian supply chain. Additionally, eight strategies were defined as solutions to overcome these barriers. The study uses the Fermatean Fuzzy Analytic Hierarchy Process (FF-AHP) to obtain the weights of barriers and the Borda Social Choice Function. The findings offer valuable insights into evaluating solutions to address barriers to big data adoption. The study ranks the barriers based on their influence. The most significant barrier is the shortage of facilities to research and develop big data tools. Other critical barriers include high costs, lack of training facilities, data quality issues, and lack of government support. The results show that the most effective solutions for improving disaster risk resilience involve increasing IT infrastructure, developing strategic plans, and securing government support to overcome barriers to big data adoption. A comprehensive understanding of the barriers to big data adoption can provide policymakers and practitioners with a roadmap for enhancing disaster risk resilience and addressing challenges associated with the adoption of big data.
This study aims to contribute to the development of the metaverse by identifying and evaluating its success factors from a business perspective using the Analytic Hierarchy Process (AHP). Based on recent trends in metaverse research—which expanded rapidly during the COVID-19 pandemic—ten key success factors were identified. These factors were categorized into three primary groups: technological, market, and functional. A hierarchical model was constructed with these three top-level factors and their corresponding second-level sub-factors. Expert opinions were collected through a structured survey, and the relative importance of each factor was analyzed using the AHP. The results show that functional factors are the most critical, followed by market and technological factors. Among the second-level sub-factors, fun emerges as the most important, followed by network and communication, market size, and immersion. The analysis also confirms differences in priority among the three expert groups—academics, researchers, and industry practitioners—through the AHP’s compatibility index. These findings provide meaningful insights into the strategic direction and future development of the metaverse.
In this study, we introduce a novel framework for enhancing leadership decision-making through the integration of topic modeling techniques and the Analytic Hierarchy Process (AHP). Despite the critical role of decision-making in leadership effectiveness, existing literature lacks robust methodologies for selecting and applying key decision-making criteria. Addressing this gap, we employed topic modeling, specifically Term Frequency–Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA), to analyze 178 leadership articles published from 2015–2023. Our analysis identified three empirically derived criteria essential for effective leadership decisions: feasibility, reliability, and adaptability & flexibility. We implemented these criteria using the AHP methodology, demonstrating their practical application through a case study of employee selection. The findings reveal that adaptability & flexibility emerged as the most critical criterion (weight=0.56), followed by reliability (0.32) and feasibility (0.12). This integrated approach transforms theoretical leadership constructs into a practical decision-making framework that enhances objectivity, reduces cognitive bias, and improves strategic outcomes. The study contributes to leadership theory by providing a systematic, transparent methodology for evaluating decision alternatives in increasingly complex organizational environments, while offering practitioners a replicable tool that can be calibrated to specific contextual demands.
Effective road maintenance planning is crucial for ensuring sustainable and efficient infrastructure management, especially in urban areas facing increasing traffic volumes and budget constraints. This study employs the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) to establish an objective framework for prioritizing road maintenance. AHP is utilized to evaluate multiple criteria, including traffic volume (23.3%), road authority (22.1%), strategic value (20.8%), population density (16.5%), handling type (8.9%), and environmental impact (8.5%), based on expert assessments. The weight calculation results are validated using SuperDecision software, ensuring consistency in decision-making. The analysis identifies Persatuan Road, Berlian Road, and LKMD I Road as the highest-priority segments requiring urgent maintenance interventions. GIS is integrated to provide a spatially driven visualization, allowing for more effective resource allocation and informed decision-making. The proposed methodology is scalable, adaptable, and transferable, offering a structured approach that can be applied to other cities with similar infrastructure challenges. By incorporating spatial decision support, this approach offers a novel integration of MCDM and GIS, improving transparency and sustainability in road infrastructure planning. Future research should explore real-time data integration and predictive modeling for enhanced infrastructure management.
This study proposes a comprehensive decision framework for evaluating Business Process Automation (BPA) platforms, addressing the complexity of selecting tools that align with diverse organizational needs. The framework combines the Fuzzy Delphi Method (FDM) for refining selection criteria and the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) for prioritizing them. Expert input from multiple industries ensured the framework’s robustness and applicability. Key findings highlight Security and Compliance as the highest-ranked criteria, followed by Scalability, Reliability, and Usability. The research also emphasizes the importance of Interoperability, Cost-Effectiveness, and Vendor Support. Beyond serving as a methodology for BPA tool evaluation, the framework provides actionable insights that organizations can adopt directly, offering dual utility as both a structured decision-making approach and a practical resource. By systematically addressing criteria weighting and evaluation, this framework ensures improved alignment with strategic objectives and reduced implementation risks, bridging the gap in structured decision-making for BPA tool evaluation.
The determination of the Soil Quality Index (SQI) was a valuable activity for land resource planning and management. This study aimed to evaluate the soil quality index in Nagari Nanggalo, Koto XI Tarusan Subdistrict, Pesisir Selatan Regency, Indonesia, by integrating the Principal Component Analysis (PCA) method with the Analytical Hierarchy Process (AHP) and visualizing the results using Geographic Information Systems (GIS). Based on the PCA analysis, six principal components were identified and weighted using AHP, namely pH (17.40%), OC (34.67%), BS (13.53%), BD (18.50%), K-exch (5.34%), and HC (10.56%). The SQI analysis results showed that 87.90% (324.48 ha) of the area was classified as poor and 12.10% (44.67 ha) as moderate. This study indicated that the area had generally poor soil quality. However, it should be noted that proper soil management, as well as soil and water conservation measures, needed to be considered to improve the current condition and enhance the soil’s carrying capacity. The soil quality index map developed in this study served as a useful tool for decision-making in land management.
This literature review explores the advancements and applications of Fuzzy Analytic Hierarchy Process (FAHP) technique between 2019 and 2024, in addition to the studies that combined or compared FAHP with other methods. FAHP integrates Analytic Hierarchy Process (AHP) with fuzzy set theory to manage uncertainty and imprecision in Multi-Criteria Decision-Making (MCDM) problems. This review covers 85 papers from prominent journals using well-known databases. It introduces a novel taxonomy that categorizes FAHP research into three main categories: outcome types, methodological variations, and application domains, with further subcategories explained in this paper. This paper highlights diverse applications of FAHP across many fields and domains, proving FAHP’s effectiveness in addressing complex decision problems. Observations reveal FAHP’s strength in uncertain problems, while gaps in the literature call for further exploration in less applied fields like agriculture and healthcare. Other future research directions also are discussed in this paper.
Today, the use of lignocellulosic residues in the production of engineered wood products promises a sustainable future. This research aims to compare and evaluate the properties of LSL glulam (laminated strand lumber) made from date palm leaf axis and a composite (a combination of cedar wood and LSL from date palm leaf axis) using two types of adhesives: epoxy glue and polyvinyl acetate. Combined glulam with palm tree residues and epoxy binder generally results in better physical and mechanical properties, making it suitable for construction applications and service conditions. This study presents a sustainable policy for decision-making in choosing the best option for producing combined glulam using the ANP (Analytic Network Process) method. First, 12 intermediate indicators and 92 important sub-indices were determined and categorized into economic, market, environmental, social, cultural, and technology groups. The effectiveness of all four options was then evaluated. The results showed that the combined glulam (cedar wood, LSL, and epoxy) option had the highest priority. The options of combined glulam (cedar wood, LSL, and polyvinyl acetate), glulam (LSL and epoxy), and glulam (LSL and polyvinyl acetate) ranked second to fourth, respectively. Additionally, the results of the sensitivity analysis indicated that changes in costs have the most significant impact on the choice of options and should be given special attention in future policy decisions. This policy framework aims to promote sustainable practices in the construction industry by encouraging the use of renewable materials like palm leaf axis as a substitute for traditional wood, thereby reducing deforestation.
The rapid advancement of artificial intelligence and automation is reshaping the nature of work, yet most occupational risk estimates remain aggregated, overlooking the variability of automation susceptibility across specific tasks. This study introduces and validates a novel, transferable methodological framework using the Analytic Hierarchy Process (AHP) to assess automation risk at the task level, grounded in structured expert judgment. The framework decomposes an occupation into discrete tasks, evaluates them against five systematically defined criteria—repetitiveness, cognitive complexity, human interaction, regulatory variability, and technological adaptability—and calculates priority vectors through pairwise comparisons. Internal consistency is verified, and inter-rater agreement is measured using Kendall’s coefficient. Applied to the accounting profession with an international panel of experts, the method identifies record keeping and report preparation as the most vulnerable tasks, while data analysis and management demonstrate greater resilience. Beyond generating ranked task vulnerability profiles, the approach reveals diversity in expert perspectives, reflecting differences in regulatory and organizational contexts. The proposed framework offers decision-makers in diverse professional and geographical contexts a replicable, evidence-based tool for anticipating technological disruption and supporting workforce adaptation strategies in the era of digital transformation.
The steel industry is a major global player in the world economy and significantly contributes to a country’s development. Maintenance is indispensable for the productivity of steel industry assets. The growing use of high-precision operations in these organizations makes hydraulic systems a critical concern. Multi-criteria decision-making (MCDM) methods can facilitate decision-making, particularly with decisions about the best maintenance policies/strategies to be employed. The Analytic Hierarchy Process (AHP) is a consolidated and appropriate method for dealing with multiple factors and uncertainty. This research proposes a model to support decision-making for selecting a maintenance strategy for hydraulic systems in steel plants. The development of this model followed the Design Science Research (DSR) methodology, which has five stages. The main scientific contribution of this research is to demonstrate that the AHP allows a landscape with a qualitative approach regarding the maintenance strategy selection of hydraulic systems in the steel industry, which enables the development of a hierarchical framework that incorporates four maintenance strategies, criteria, and sub-criteria identified in the current literature. The criteria of cost, safety, reliability, quality, and feasibility were examined to determine the best maintenance strategy to be applied. Predictive maintenance was selected as the priority strategy, while safety was the criterion with the highest added value. The sensitivity analysis confirmed the robustness of the framework, showing that classifications remained stable even when the weights of the criteria varied.
The use of the Internet and its applications has radically transformed the ways in which consumers communicate, obtain information and make purchasing decisions. This work aims to identify innovative solutions to facilitate and make the process of searching and booking restaurant services by users who use digital platforms more efficient. The main objective is to analyze the intrinsic complexity of the user’s decision-making process, highlighting the main critical issues and difficulties with perception and possible information distortions, in order to design a support tool capable of offering a highly personalized and high-quality choice experience. The contribution proposes an advanced decision-making model that integrates the use of two multi-criteria methodologies: the Analytic Hierarchy Process (AHP) and the Data Envelopment Analysis (DEA). These tools make it possible to simultaneously consider a multiplicity of qualitative and quantitative criteria, configuring an evaluation much more adherent to the real preferences of users. The combined adoption of these two approaches constitutes a methodological innovation capable of providing each user with a dynamic, personalized and efficient search path, significantly improving the quality of decisions and the level of perceived satisfaction. This model, which overcomes the limitations of traditional recommendation systems based on simple averages is proposed as a reference tool for the design of online platforms dedicated to catering, with potential extensions to other service sectors.
