Multi-criteria Decision-making Methodology Research Articles

Integrated fuzzy decision-making methodology with intuitionistic fuzzy numbers: An application for disaster preparedness in clinical laboratories

Society is on constant alert due to the increasing frequency and severity of Seasonal Respiratory Diseases (SRDs), posing significant challenges from both a humanitarian and public health perspective. The recent COVID-19 pandemic has tested the capacity of clinical laboratories to address seasonal infections, epidemic outbreaks, and critical emergencies. This scenario has led to operational burdens, primarily from resource limitations, a lack of proactive planning, and the low adaptation to unforeseen circumstances. Coupling different data-driven approaches considering multi-criteria weighting, interdependence assessment, and outranking are critical for devising effective interventions upgrading the operability of clinical labs during SRDs. Nonetheless, a deep literature review revealed there are no studies using these hybridized approaches when addressing this problem. Consequently, this article proposes the application of an innovative hybrid Multicriteria Decision-Making (MCDM) methodology that integrates the Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP), Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL), and Combined Compromise Solution (CoCoSo) to assess the disaster preparedness of clinical laboratories during SRDs. Initially, we applied IF-AHP to assign the relative weights to criteria and sub-criteria, considering the inherent hesitation and uncertainty in decision-making. Subsequently, IF-DEMATEL was utilized to analyze the interrelationships between criteria, providing insights into the interrelations among clinical lab disaster management drivers. Finally, the CoCoSo method was applied to estimate each lab’s Preparedness Index (PI) and detect response gaps when coping with SRDs. The suggested methodology was validated across nine clinical laboratories in Colombia during the most recent respiratory pandemic. This study contributes to the healthcare sector authorities by identifying key criteria and sub-criteria affecting the response of clinical labs, the elicitation of main response drivers in clinical labs when facing SRDs, and the calculation of a multidimensional indicator representing the preparedness of the labs. This work also enriches the literature by applying the IF-AHP, IF-DEMATEL, and CoCoSo approach to a challenging case study requiring a multi-method data-driven application. Furthermore, it suggests future directions to improve the proposed framework in other related contexts.

Multi-attribute-based procedure for seismic loss scenario in a historical area

AbstractSeismic events have shown to be exceedingly damaging to structures over time, with serious social and economic consequences. As a result, large-scale seismic risk assessments are essential for reducing the potential damage from future earthquakes. Therefore, the proposed study attempts to examine the vulnerability and risk of unreinforced masonry buildings (URM) placed in aggregate conditions in a historical area of the city centre of Lisbon. To this purpose, a comprehensive exposure model was developed combining satellite remote sensing, GIS software, and census data. Subsequently, seismic hazard was evaluated in the area, considering both Peak Ground Acceleration (PGA) and macro-seismic intensity for different return periods (i.e. 2-50-100-475-975-2500-5000 years). Vulnerability was assessed by introducing a novel approach to earthquake risk assessment using Multi-Criteria Decision-Making methodologies. Specifically, the method employs the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) computational methods to evaluate parameter weights and vulnerability index. Damage scenarios, fragility curves and mean damage ratios curves were provided to offer an overview of the vulnerability of the assets exposed to risk. Finally, the expected consequences were evaluated in terms of direct economic losses showing an economic loss of 595 M€ for a 475-years return period, while 1108 M€ for 5000-years. This study significantly enhances seismic analysis for urban areas by introducing a Multi-Criteria Approach. This method simplifies vulnerability assessment, ensuring ease of application and reproducibility. Its insights offer valuable support for disaster risk management decisions, facilitating the implementation of resilience and risk-reduction strategies.

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.

Sustainable urban water management index for developing countries. A case study in Puebla City, Mexico

Several indicator and index frameworks have been reported to assess management efficiency and promote transparency, facilitating decision-making and innovation. However, they cannot be applied in cities with limited information, making them impractical. This study proposes a Sustainable Urban Water Management Index (SUWMI) for such cities, selecting six AquaRating indicators plus two indexes recommended in the Sustainable Development Goal 6, yielding a simplified and practical version for diagnosing the management performance rate of the Water Operating Systems (WOS) in cities. Conceptual and mathematical models were defined for SUWMI, and the eight selected terms were hierarchized based on a decision tree with the participation of an expert panel and further multicriteria decision-making methodology by the Analytic Hierarchy Process (AHP) to calculate the weighting parameters. The selected indicators represent five categories on a percentage scale. The highly populated Puebla City Metropolitan Zone in Mexico was the case study. Information was gathered from five Water Operation Systems (WOS) from 2002 to 2020. The tap water area's coverage and physical efficiency improved despite the population growth due to an adequate political strategy after 2014. The SUWMI values of each WOS diagnoses its performance, highlighting their differences and providing a valuable tool for guiding urban water public policy programs. The paper proposes a simple, fast, and easy-to-calculate index that can be helpful for a broader cluster of cities and urban settlements with deficient available information, which is common in developing countries.

Enhancing readiness degree for Industrial Internet of Things adoption in manufacturing enterprises: An integrated Pythagorean fuzzy approach

This study proposes a hybrid multi-criteria decision-making (MCDM) methodology designed to enhance the readiness for adopting the Industrial Internet of Things (IIoT) in manufacturing enterprises. The Pythagorean fuzzy approach is employed to address uncertainty and imprecision throughout decision-making processes. The development framework in this study incorporates TOE (Technology-Organization-Environment) and HOT fit (Human-Organization-Technology) to pinpoint barriers to IIoT adoption. Additionally, a triple helix model (THM) emphasizing on the synergy among university-industry-government is utilized to formulate pragmatic strategies. The agro-food processing industry in Thailand is used as a case study. In this study, even barriers are identified and validated through the Delphi method. The SWARA (Step-wise Weight Assessment Ratio Analysis) method determines the importance weights of these barriers, revealing “lack of digital culture”, “lack of knowledge and expertise,” and “job displacement concerns” as the three most critical barriers. The COBRA (COmprehensive Distance Based Ranking) method is employed to prioritize pragmatic strategies under THM, indicating that the role of the university in enhancing human capital capabilities is the most important, followed by the government's roles in enabling national ICT infrastructures and offering investment incentives as the second and third pragmatic strategies, respectively. A sensitivity analysis validates the proposed framework's reliability and robustness. The study's findings emphasize the potential of this integrated framework to guide future research endeavors among scholars and academicians across diverse industries beyond agri-food processing.

A multi-criteria decision support framework for designing seismic and thermal resilient facades

Facades play a pivotal role in the performance of a building, serving various environmental, structural and operational functions. As climate-induced extreme events become more frequent, developing resilient facades is becoming crucial. Although facades can contribute significantly to the total post-disruption losses, their resilience is not sufficiently addressed in current design approaches. In response to this research gap, this study proposes a multi-criteria decision-making methodology to select optimal facade designs using resilience criteria: resilience loss and economic loss. The framework addresses the complexity of facade design, considering multiple hazards such as earthquakes and heatwaves. For seismic hazard, the facade’s resilience is defined as its ability to mitigate damage. In the case of heat hazard, resilience is assessed based on the ability to keep indoor conditions within a comfortable thermal range. To demonstrate the applicability of the proposed methodology, a case study of an 18-story office building in Izmir (Turkey) is used to compare alternative facade packages. These packages identify the facade design cases, each coupled with a dataset of seismic and thermal fragility curves. Numerical simulations are conducted to derive seismic and thermal resilience curves for each facade package, along with resilience criteria. These criteria are embedded into a practical decision-making process to enable the selection of the optimal design case based on project specifications.

Prioritization of investment order in Brazil among renewable energy sources: an analysis using the SWARA-MOORA-3NAG method

Purpose This paper aims to comprehensively analyze renewable energy alternatives in Brazil, focusing on identifying the most suitable option for investment in the country’s sustainable development. Design/methodology/approach The study adopts the step-wise weight assessment ratio analysis-multiobjective optimization by ratio analysis −3NAG (a combination of three normalization methods) methodology, a multicriteria decision-making approach, to evaluate and rank renewable energy sources based on key criteria such as resource availability, cost-effectiveness, job creation potential and environmental impact. Findings The analysis reveals that solar energy emerges as the preferred choice for Brazil, offering significant advantages over other alternatives such as hydroelectric, wind and biomass energy. Solar energy’s distributed generation capability, cost reduction trends and positive environmental impact contribute to its favorable position in meeting Brazil’s energy needs. Research limitations/implications While the study provides valuable insights into renewable energy selection, there are limitations regarding the criteria’ scope and the exclusion of specific renewable energy options. Future research could explore sensitivity analyses and incorporate additional criteria to enhance the study’s comprehensiveness. Originality/value This research contributes to the existing literature by thoroughly analyzing renewable energy alternatives in Brazil using a robust multicriteria decision-making methodology. The study’s findings provide actionable guidance for policymakers, businesses and stakeholders seeking to promote sustainable energy development in the country.

Ranking and Challenges of Supply Chain Companies Using MCDM Methodology

Background: Supply chain companies have merits and demerits regarding operational and economic transactional policies. The effectiveness of supply chain companies corresponds to a cumulative score on a multi-criteria and perspectives-based evaluation. In this paper, we analyse the performances and challenges of several celebrated e-commerce companies to perceive their overall impression of supply chain management. Method: A mathematical model is framed as a multi-criteria decision-making (MCDM) problem with challenges as criteria and companies as alternatives. The criteria importance through inter-criteria correlation (CRITIC) method is used in this paper to adjust weights representing the available data. The ranking of e-commerce companies is evaluated using multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method. Results: This model investigates the most dependent criteria and sub-criteria for the adaptation challenges of supply chain companies (SCCs). Furthermore, the SCCs are prioritized based on various conflicting criteria. Conclusion: Various challenges of SCCs, like logistics constraints, disruptions in supply chains, issues with technology, ethical sourcing and inconsistency between the products’ availability and the pace of consumption, are considered and analysed. We amassed the difficulties as criteria and sub-criteria in a numerical process using the MCDM approach. Additionally, the sensitivity and comparative of several optimal phenomena are analysed based on distinctive combinations of challenges in the ranking arena.

Cost-benefit analysis in UK hotels: A hybrid SOCP-MCDM approach

Performance evaluation has been an important topic of concern for tourism industry practitioners as well as academic researchers, and its investigation in the UK hotel sector is paramount because this industry has been experiencing a higher level of competition. The present study contributes to the previous literature on hotel performance evaluation in general by proposing an innovative hybrid method combining the second-order cone programming (SOCP) method and multi-criteria decision-making (MCDM) method to estimate the performance of the UK hotel sector. The innovation lies in the synergistic combination of SOCP and MCDM methodologies, enabling a comprehensive assessment of hotel performance by managing a non-linear optimisation. Overall, this hybrid method benefits from the ability to be more flexible in addressing complex operational issues and provide more accurate results. This research provides a cost-benefit analysis within the proposed method, suggesting important policy implications in the tourism industry.

An efficient decision-making model for evaluating irrigation systems under uncertainty: Toward integrated approaches to sustainability

Agriculture is essential for many countries that depend on crops for food production and security. So, evaluating irrigation systems and selecting the best one is critical and gives various benefits such as water use efficiency, productivity, and agricultural development. This paper proposed a framework for conserving water and increasing effectiveness by using a suitable irrigation system. We used the multi-criteria decision-making (MCDM) methodology to deal with conflict criteria in the evaluation process. We used two MCDM methods such as Criteria Importance Through Inter-Criteria Correlation (CRITIC) method to compute the weights of the irrigation system criteria, and the spherical fuzzy double normalization-based multiple aggregations (DNMA) method to rank the irrigation systems (alternatives). The main advantage of CRITIC method computes the conflict and variability of the criteria by calculate weights of them. The main advantage of DNMA is used the two normalization method to rank the alternatives. These methods are integrated with spherical fuzzy set (SFS) fuzzy information in the assessment process. It has three values: membership, non-membership, and hesitant degrees to overcome uncertainty in the assessment steps. The proposed methodology is applied to a case study to show its performance. This study used 20 criteria of irrigation systems and 10 irrigation systems (alternatives) to select the best alternative. The results are discussed from the perspective of five experts. The sensitivity analysis is conducted to show the stability of the results. The comparative analysis is performed to show the validity and effectiveness of the proposed methodology. The results show the proposed methodology is more robust compared to other methods.

A fuzzy logic-based risk assessment framework for the crude oil transportation supply chain

This study proposes a novel approach to increase the efficiency of crude oil transportation by integrating fuzzy logic and the TOPSIS method. The proposed technique addresses the uncertainties and imprecisions in decision-making processes to identify the optimal shipping alternatives. The study also evaluates the static and dynamic risk indicators related to the transportation of crude oil. The China-Pakistan Economic Corridor (CPEC) is found to be a promising option for crude oil shipments, reducing the risks associated with current routes. Using a multi-criteria decision-making (MCDM) methodology, the research determines China's most efficient crude oil supply channel using a fuzzy-based Improved TOPSIS approach. The economic feasibility of the CPEC option has been confirmed through a thorough cost-benefit analysis (CBA). Additionally, the optimal mode of transportation from CPEC seaports to Western China is determined using the fuzzy-based Improved TOPSIS method. The MCDM criteria considered in all scenarios include time, financial outlays, energy consumption, environmental impact, and security risks. The results indicate that the CPEC route and the maritime route via Myanmar outperform the conventional route that passes through the seaports of Eastern China. This study emphasizes the significant advancement in decision-making in crude oil transportation made possible by the fuzzy-based Improved TOPSIS technique.

A Bibliometric Analysis of Material Selection Using MCDM Methods: Trends and Insights

The selection of appropriate materials is a critical decision-making process in engineering, impacting both performance and sustainability. Multi-Criteria Decision-Making (MCDM) methods have become increasingly popular for navigating the complex trade-offs involved in material selection. This study presents a comprehensive bibliometric analysis of research on material selection using MCDM methods, covering publications from 2010 to 2024. Using tools like VOSviewer 1.6.20, this analysis identifies key trends, influential authors, prominent institutions, and geographic distribution of research contributions. The findings reveal a significant growth in publications, particularly after 2010, reflecting the expanding recognition of MCDM’s importance in material selection processes. Notable contributors, including key researchers and leading institutions, have shaped the field through high-impact studies. The geographic analysis highlights a strong concentration of research in regions such as Asia and Europe, underscoring their leadership in advancing MCDM methodologies. This paper provides valuable insights into the evolution of material selection research using MCDM, offering a foundation for future studies and applications in engineering and beyond.

Digital twin and fuzzy framework for supply chain sustainability risk assessment and management in supplier selection

Risks in the supply chain can damage many companies and organizations due to sustainability risk factors. This study evaluates the supply chain risk assessment and management and then selects the best supplier in a gas company in Egypt. A comprehensive methodology can use the experts' opinions who use the linguistic variables in the spherical fuzzy numbers (SFNs) to evaluate the criteria and suppliers in this study based on their views. Selecting the best supplier is a complex task due to various criteria related to supply chain risk assessment, such as supply risks, environmental risks, financial risks, regularity risks, political risk, ethical risks, and technology risks and their sub-criteria. This study suggested a new combined model with multi-criteria decision-making (MCDM) under a spherical fuzzy set (SFS) environment to overcome uncertainty and incomplete data in the assessment process. The MCDM methodology has two methods: the Entropy and COmbinative Distance-based Assessment (CODAS) methods. The SFS-Entropy is used to compute supply chain risk assessment and management criteria weights. The SFS-CODAS method is used to rank the supplier. The main results show that supply risks have the highest importance, followed by financial and environmental risks, and ethical risks have the lowest risk importance. The criteria weights were changed under sensitivity analysis to show the stability and validation of the results obtained from the suggested methodology. The comparative analysis is implemented with other MCDM methods named TOPSIS, VIKOR, MARCOS, COPRAS, WASPAS, and MULTIMOORA methods under the SFS environment. This study can help managers and organizations select the best supplier with the lowest sustainability risks.

Assessment of techno-economic analyzes of grid-connected nuclear and PV/wind/battery/hydrogen renewable hybrid system for sustainable and clean energy production in Mersin-Türkiye

The main novelty in the presented paper is that it presents an energy analysis for a hybrid system that integrates nuclear power plants with wind/solar power plants for sustainable and clean energy production. In addition, excess energy is used to produce hydrogen. A techno-economic feasibility assessment is performed to ensure continuous electricity supply for hourly and peak residential load requirements in Mersin located in Türkiye. In the first stage of the study, suitable areas and sizes for solar and wind energy for energy production were determined with ArcGIS and Multi-Criteria Decision-Making methodologies. In the second stage, six scenarios were investigated in terms of electricity production, and excess energy was used for hydrogen production. The results brought out that more electricity was produced than the city's electricity consumption (5395 GWh) in all 6 scenarios considered. The maximum electricity production at a rate of 11900 GWh/yr and the maximum hydrogen at a rate of 132675 tons was produced in Scenario 4 with the cost of energy (0.31 $/kWh) and cost of hydrogen (12.6$/kgH2). In this scenario, the percentages of solar, wind and nuclear energy in total production 1 %, 12 %, and 87 %, respectively. According to the results, it was concluded that 236621 tons of carbon emissions were reduced when the city's energy needs were met according to the 6 scenarios in this study.

Navigating critical barriers for green bond markets using A fuzzy multi-criteria decision-making model: Case study in Vietnam

As potent financial instruments channeling capital into climate and environmental projects, green bonds' issuance is frustrated by myriad obstacles. Therefore, this study aims to explore the critical barriers to green bond issuance in Vietnam. This study elucidates the salient barriers by utilizing an integrated multi-criteria decision-making methodology synthesizing the Fuzzy set theory-based Delphi, Decision-Making Trial and Evaluation Laboratory (DEMATEL), and DEMATEL-based Analytic Network Process (DANP). An extensive literature review of green bonds delineates five key dimensions related to impediments: Policy, Market, Financial, Capacity, and Awareness, with 38 discrete indicators representing potential obstacles. Firstly, employing a Fuzzy Delphi survey of 16 experts, 32 indicators are distilled for further analysis. Then, Fuzzy DEMATEL modeling evaluates the interrelationships and interactions among barriers. Finally, DANP is applied to obtain the relative importance of key barriers. Results unveil the five most formidable barriers as a weak regulatory framework and infrastructure (PO1), Limited availability of green bond issuance guidelines and templates (PO2), Insufficient incentives or tax benefits for green bond issuers (PO3), Limited coordination and alignment with international green bond standards (PO5), Lack of investor confidence in the quality and credibility of green projects (AW4), and Capacity constraints among issuers, particularly in smaller organisations or government agencies (CA1). The research contributes to the broader literature on green finance and offers valuable insights for promoting sustainable finance practices in Vietnam and other developing economies.

A systematic literature review of logistics services outsourcing

Logistics is critical in every company's supply chain (SC), and outsourcing helps businesses concentrate on their core competencies. Third-party logistics (3 PL) or logistics service providers (LSPs) assist businesses in cutting costs while improving performance, sustainability, and revenue. Logistics evaluation and LSPs choice are complicated and critical components of value delivery. This study aims to review logistics outsourcing literature to understand the trends, prospects, factors, and strategies used in logistics companies' outsourcing choices. This work examines the literature on LSPs selection published between 2010 and 2023. This paper uses VOSviewer (version 1.6.19) to visualize the relationships. Pricing, timely shipment, service quality, reliability, agility, technology, and consumer feedback are the most commonly utilized, whereas societal and environmental factors are seldom used. The study comprises journal publications, the year, selection criteria, and assessment methodologies. Numerous scholars have discovered and employed many critical selection criteria. Many investigators have also embraced multi-criteria decision-making (MCDM) methodologies, and their fuzzy form is widely used. In conclusion, recommendations for theorists and managers, limits, and future directions for research are offered.

"Thin" Structure of Relations in MCDM Models. Equivalence of the MABAC, TOPSIS(L1) and RS Methods to the Weighted Sum Method

This paper introduces the conceptual framework of the multi-criteria decision-making (MCDM) rank model, which embodies the integration and harmonization of the aggregation method, the weighing method, the decision matrix normalization technique, and the selection of distance metrics. This definition serves to broaden the spectrum of acceptable MCDM methodologies for problem-solving and specifiing the associated tools. A Multi-Method Model (3M) approach is employed for multi-criteria selection to enhance the reliability of the results. The methodology is outlined for adjusting the rankings of alternatives to account for the distinguishability of ratings in a particular MCDM model using the Relative Performance Indicator (RPI) of alternatives. Through RPI, four methods are established for aggregating individual characteristics of alternatives that yield identical results: Weighted Sum Model (WSM), Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS (L1)), and Ratio System approach (RS), eliminating the need to duplicate these methods in the 3M approach. A comprehensive comparison of numerous multi-criteria methods is conducted based on two lists: ranking and rating. Additionally, a method for step-by-step linear transformation of alternative ratings obtained from various MCDM models is defined, facilitating comparison and aggregation of ratings.

A MCDM-based measurement proposal of job satisfaction comprising psychosocial risks

Preserving human well-being has become challenging for businesses, which continue efforts to overcome managing business processes concurrently, due to the cumulative effects of psychosocial risks at work that may seriously impair one’s health. By focusing on this need, this paper proposes a more effective and realistic way of measuring job satisfaction comprising psychosocial risks by integrating multi-criteria decision-making (MCDM) methodology. Simplified PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA-S), i.e. the selected MCDM method, is used for weighting domains based on the opinions of employees working for a company in Türkiye, who also provide their attitudes towards job satisfaction through 36 items of Job Satisfaction Survey (JSS). This paper uses nine subscales of JSS to represent domains relevant to psychosocial risks. Three developed scenarios discuss the effectiveness of the proposed subscale-weighted job satisfaction measurement by presenting the subscales having different ranges in standard scores relative to the subscale-weighted scores.

Evaluation Challenges of Leadership Management in the Energy Sector using Multi-Criteria Decision Making Approach

Leadership management has various benefits and advantages in the energy sector, such as improving energy sector development and obtaining sustainability benefits. However, the leadership management faces multiple challenges in the energy sector that must be evaluated and ranked. So, this study used the fusion multi-criteria decision-making (MCDM) methodology to assess and rank the challenges of leadership management in the energy sector. The WASPAS method is a fusion MCDM method used to rank the alternatives. Five experts are invited to evaluate the criteria and alternatives. Fourteen criteria and 13 alternatives were collected in this study. The results show that alternative 4 is the best and alternative 8 is the worst. A sensitivity analysis was conducted to show the stability of the alternatives' ranks. The sensitivity analysis was conducted in two parts. In the first part, the lambda value of WASPAS changed between 0 and 1. In the second part, 14 cases are introduced in criteria weights. The results show that the rank of alternatives is stable in different cases.

Leveraging text mining and analytic hierarchy process for the automatic evaluation of online courses

This study introduced a multi-criteria decision-making methodology leveraging text mining and analytic hierarchy process (AHP) for online course quality evaluation based on students’ feedback texts. First, a hierarchical structure of online course evaluation criteria was formulated by integrating topics (sub-criteria) identified through topic modeling and interpreted based on transactional distance and technology acceptance theories. Second, the weights of the criteria in the hierarchical structure were determined based on topic proportions. Third, the AHP was employed to determine the overall relative advantage of online courses and their relative advantage within each criterion based on the hierarchical framework and criterion weights. The proposed approach was implemented on the datasets of 6940 reviews for knowledge-seeking courses in Art, Design, and Humanities (D1) and 44,697 reviews for skill-seeking courses in Computer Science, Engineering, and Programming (D2) from Class Central to determine ranking positions of nine courses from both D1 and D2 as alternatives. Results revealed common concerns among knowledge and skill-seeking course learners, encompassing “assessment”, “content”, “effort”, “usefulness”, “enjoyment”, “faculty”, “interaction”, and “structure”. The article provides valuable insights into the online course evaluation and selection processes for learners in D1 and D2 groups. Notably, both groups prioritize “effort” and “faculty”, while D2 learners value “assessment” and “enjoyment”, and D1 learners value “usefulness” more. This study demonstrates the efficacy of leveraging online learner reviews and topic modeling for automating MOOC evaluation and informing learners’ decision-making processes.