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.
