By addressing a clear methodological gap and leveraging recent technological advancements, this study offers both a theoretical advancement in multi-criteria energy planning and a practical, scalable tool for supporting sustainability transitions in the aviation sector.
This section reviews the current literature on multi-criteria decision-making (MCDM) methods in sustainable energy planning, with a specific focus on their applications in aviation infrastructure and airport energy management. The aim is to examine the methodological evolution of MCDM models — especially those integrating optimization, clustering, and fuzzy prioritization — and to identify existing gaps that the present study addresses. To ensure comprehensive coverage of the field, a systematic literature search was conducted using the Scopus and Web of Science databases. The following keywords and their combinations were used: “multi-criteria decision-making”, “MCDM”, “fuzzy AHP”, “Pythagorean fuzzy”, “NSGA-II”, “K-means clustering”, “airport energy planning”, “sustainable infrastructure”, “aviation energy strategy”, and “smart airport decision model”. Boolean operators (AND/OR) were applied to filter articles, and results were narrowed to peer-reviewed journal publications from 2018 to 2025. Preference was given to studies published in high-impact journals in the fields of energy systems, decision sciences, and aviation management. This review also synthesizes the state of the art in hybrid decision models that combine optimization, clustering, and expert-driven prioritization under uncertainty. The following subsections provide a structured analysis of these domains, setting the foundation for the hybrid framework proposed in this study.
Multi-Criteria Decision-Making (MCDM) methods have become indispensable for solving complex problems in sustainable energy and infrastructure planning, where decisions must incorporate conflicting criteria such as cost, environmental impact, technical feasibility, operational risks, and social acceptance. Classical MCDM methods, including Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), have provided foundational decision support frameworks by transforming qualitative assessments into quantifiable rankings.
For instance, Dash et al. employed AHP to assign subjective weights to criteria such as economic cost, capacity factor, and environmental impact for evaluating wind energy alternatives in India. They then used TOPSIS to identify the solution closest to an ideal point, and VIKOR to rank alternatives based on a compromise solution that considers group utility and individual regret. This comparative application demonstrated that integrating multiple classical MCDM methods can enhance decision robustness and build consensus among stakeholders. Similarly, Kshanh and Tanaka applied AHP to structure hierarchical criteria and sub-criteria for energy efficiency investments in a petrochemical complex, followed by PROMETHEE to evaluate and rank alternatives through pairwise comparisons using preference functions. Their study highlighted PROMETHEE’s ability to discriminate effectively among closely ranked options and its compatibility with stakeholder engagement processes.
Geographic Information Systems (GIS) integrated with MCDM have allowed for spatially explicit evaluations. Richards et al. used GIS-based MCDM with a Weighted Linear Combination (WLC) approach to incorporate spatial layers such as environmental sensitivity, wind resource distribution, and land-use conflicts in onshore wind energy planning in Japan. Luhaniwal et al. combined GIS and AHP to assess offshore wind farm sites, calculating relative importance scores for factors like wind speed, sea depth, and proximity to grid infrastructure. Saraswat et al. extended this approach by integrating hybrid MCDM methods, including entropy weighting for objective criteria and fuzzy AHP for subjective criteria, into GIS for multi-renewable energy potential site analysis. Santos et al. further combined GIS, Building Information Modeling (BIM), and MCDM, creating a comprehensive spatial-technical framework for infrastructure project planning that supports complex multi-layer data integration.
Fuzzy and hybrid MCDM approaches have advanced the capacity to manage vagueness and uncertainty inherent in expert judgments. Li et al. integrated fuzzy sets with cumulative prospect theory within an MCDM framework to capture stakeholders’ risk attitudes toward renewable energy development paths in Malaysia. This hybrid model combined subjective weighting of criteria with a behavioral component to better reflect real-world decision-making under risk. Jameel et al. proposed an integrated hybrid MCDM framework incorporating both subjective (AHP-based) and objective (entropy-based) weighting methods for renewable energy strategy prioritization, effectively balancing conflicting sustainability criteria.
Advanced applications combining SWOT analysis, game theory, and MCDM highlight strategic energy planning dimensions. Hasankhani et al. applied SWOT analysis to identify internal and external factors affecting waste-to-energy options in Iran, then used a hybrid MCDM framework involving fuzzy AHP for weighting and game theory for strategic interaction modeling among stakeholders. Zhao and Guo utilized a hybrid MCDM approach combining DEMATEL (Decision Making Trial and Evaluation Laboratory) for identifying interdependencies among criteria, and VIKOR for selecting optimal urban integrated energy system plans, balancing technical, economic, and social considerations. Ruiz-Vélez et al. developed a custom NSGA-II model with specialized repair operators to optimize sustainable road infrastructure solutions, merging evolutionary multi-objective optimization with MCDM evaluations to ensure Pareto-efficient and practically feasible outcomes.
In industrial settings, MCDM methods have been used to evaluate renewable energy integration with detailed technical considerations. Parvaneh and Hammad employed a hybrid MCDM approach using fuzzy AHP for weighting technological, environmental, and economic criteria to select sustainable power-generating technologies. Yılmaz and Uyan utilized a combination of AHP and GIS-based overlay analysis to prioritize potential sites for green hydrogen production, emphasizing solar insolation, grid accessibility, and environmental restrictions. Sugumar and Anglani developed a decision-support framework that integrates MCDM with resilience analysis for microgrid technology siting, explicitly considering spatial geographic constraints and alignment with Sustainable Development Goals (SDGs).
AI and machine learning integrations into MCDM frameworks are at the forefront of recent methodological advances. Alijoyo proposed a deep learning-enhanced MCDM framework wherein convolutional neural networks (CNN) forecast energy consumption profiles in smart buildings, and fuzzy AHP subsequently prioritizes energy conservation strategies. Binyamin and Slama introduced IntelliGrid AI, combining blockchain for secure data sharing, deep learning for consumption pattern recognition, and fuzzy logic within MCDM for optimizing vehicle-to-home and home-to-vehicle energy exchanges. Talebi et al. developed a machine learning-driven hybrid MCDM system to strategically plan electric vehicle charging and renewable energy infrastructure, improving real-time adaptability and stakeholder-informed prioritization.
Further cross-domain applications demonstrate MCDM’s versatility. Babatunde et al. utilized fuzzy AHP and TOPSIS to evaluate simulation software for sustainable power systems education, emphasizing usability, modeling capabilities, and long-term learning impact. Thakur et al. applied Pythagorean fuzzy sets within MCDM to support complex urban development decisions under high uncertainty. Başeğmez et al. (2025) integrated GIS, machine learning, and MCDM to manage urban green spaces sustainably, prioritizing ecological value, accessibility, and maintenance cost. Saraswat et al. further showcased hybrid MCDM for renewable site analyses across multi-layer spatial and regulatory contexts. Comprehensive reviews by Sahoo et al. (2025) and Kumar and Pamucar systematically documented these developments, highlighting increased adoption of hybrid, fuzzy, and AI-enhanced MCDM methods to address growing complexity in sustainability-driven decisions. Dwivedi and Sharma demonstrated the use of MCDM to assess SDG performance across Indian states, illustrating its utility in large-scale policy evaluation and regional planning.
Recent studies have advanced cross-disciplinary decision frameworks for renewable energy deployment, illustrating important lessons for aviation infrastructure planning. Almutairi et al. conducted a comprehensive case study in Iran that integrated SWOT analysis to identify strategic directions, weaknesses, opportunities, and threats, combined with SWARA weighting and ARAS-Grey for ranking, further supported by fuzzy Shapley values. Their results showed that strategies emphasizing high-efficiency wind and solar technologies, complemented by hydrogen production and battery storage, achieved superior environmental and economic performance. Similarly, Almutairi et al. demonstrated the effectiveness of a hybrid wind-solar-diesel-battery microgrid model for supplying power to remote facilities, emphasizing resilience and adaptability — qualities crucial for airports operating in isolated or emergency contexts. In parallel, Dehshiri et al. proposed a blockchain-enhanced framework for renewable energy supply chains that integrates strategic alliances and smart contracts to strengthen transparency and traceability, aligning with future airport energy ecosystem needs. Furthermore, Dehshiri et al. introduced a Pythagorean fuzzy-based decision-making approach combining the Best-Worst Method (BWM) and Interval-Valued Pythagorean Fuzzy WASPAS (IVPF-WASPAS) to assess renewable energy projects under economic, social, and environmental sustainability criteria. Their findings highlighted solar energy as the optimal source in their comparative analysis, validated through sensitivity testing — emphasizing the importance of robust uncertainty modeling in critical infrastructure energy decisions. Collectively, these studies underscore the value of integrating hybrid decision models, advanced optimization, and transparent supply chain strategies to guide future airport energy planning and retrofit prioritization.
Despite the substantial progress across these studies, many models remain focused on single-stage ranking or do not fully integrate optimization algorithms with clustering and robust expert-based prioritization. The present study addresses this critical methodological gap by integrating a non-dominated sorting genetic algorithm (NSGA-II) for generating Pareto-optimal solutions, K-Means clustering for interpretable solution grouping, and a Pythagorean fuzzy analytic hierarchy process (PFAHP) for handling uncertainty in expert prioritization. This unified, technically advanced framework equips decision-makers with a transparent and holistic approach to guide complex, sustainability-focused infrastructure decisions, such as airport energy retrofits, ensuring alignment with operational constraints and global environmental targets. To provide a clear overview of the diverse methodological contributions referenced in this section, Table 1 summarizes the key studies that have advanced the application of multi-criteria decision-making (MCDM) methods in sustainable energy and infrastructure planning.
Energy decision-making in the aviation sector has evolved significantly as airports strive to align operations with global decarbonization targets such as ICAO’s CORSIA framework and the IATA Net Zero Roadmap. Airports increasingly adopt Multi-Criteria Decision-Making (MCDM) techniques to evaluate energy retrofit strategies, optimize resource allocation, and prioritize sustainability initiatives. For example, Mizrak, Polat, and Tasar developed an integrated model combining entropy weighting and a 2-tuple linguistic T-spherical fuzzy MCDM to prioritize sustainability actions at Istanbul Airport. Their model addressed both subjective and objective weighting challenges and robustly prioritized criteria such as emission reduction potential, energy cost savings, and regulatory adaptability.
Similarly, Raad and Rajendran created a hybrid framework for selecting optimal airport sites by integrating Slacks-Based Measure Data Envelopment Analysis (SBM-DEA), multiple regression, and GIS-MCDM. Their approach evaluated solar radiation, land availability, and noise impacts, while DEA provided efficiency scores, enabling data-driven and spatially informed decisions. Beyond strategic planning, MCDM techniques also support detailed energy infrastructure integration. SaberiKamarposhti and colleagues emphasized how fuzzy-based MCDM methods — such as Pythagorean fuzzy AHP and fuzzy TOPSIS — enable nuanced evaluation of hydrogen storage, grid synchronization, and load forecasting in smart grid-enhanced airport systems. Sasi Bhushan and co-authors applied a mixed-integer optimization model integrated with fuzzy logic to manage heterogeneous battery energy storage systems (BESS) under time-varying loads. Their approach considered discharge rates, solar input fluctuations, and backup needs while using fuzzy rule-based systems to rank storage options based on safety, capacity, and lifecycle cost. These models are particularly relevant as airports electrify ground operations and integrate advanced HVAC systems, a transition that poses significant infrastructure challenges.
Multi-objective optimization frameworks such as NSGA-II have become popular for solving trade-offs among multiple criteria in airport energy strategy development. Originally designed to combine Pareto optimality with elitism and crowding distance sorting, NSGA-II facilitates diverse and efficient solution sets, helping decision-makers understand operational and sustainability scenarios. In related work, Dey, Dash, and Basu applied NSGA-II to optimize hybrid power systems integrating solar, wind, and thermal sources, illustrating its flexibility for complex energy systems. In retrofitting contexts, NSGA-II supports simultaneous optimization of cost, emissions reduction, implementation time, and operational disruption, aligning technical and strategic objectives.
Clustering methods such as K-Means have been used to categorize retrofit strategies into interpretable groups, aiding stakeholder understanding and decision confidence. Liu, Wu, and Xu introduced probabilistic K-Means clustering for large-scale group decision-making, providing structured groupings like “low-cost/high-disruption” or “high-impact/green,” critical for complex airport projects. Qingguo and colleagues enhanced this by using genetic algorithms in K-Means, offering refined grouping capabilities for dynamic and stakeholder-rich contexts.
Real-world applications further demonstrate the feasibility of MCDM in airport energy planning. Baxter assessed retrofitting strategies at London Gatwick Airport using weighted scoring and benchmarking for HVAC, lighting, and building management systems, showing measurable sustainability and operational improvements. Ereser and Beyhan evaluated terminal architectural alternatives through cost-benefit and lifecycle sustainability metrics, illustrating the architectural impact on overall energy profiles. At Copenhagen Airport, Baxter and Wild applied structured frameworks to improve energy performance, focusing on airside operations and auxiliary services. Goh and colleagues proposed an adaptive energy management strategy integrating solar, wind, and waste-to-energy systems to help airports achieve carbon neutrality, using dynamic weighting to adapt to seasonal and operational conditions.
Emerging frameworks increasingly emphasize smart technologies and long-term resilience. Gao and He applied the Fuzzy Best-Worst Method to prioritize drivers of smart aviation, highlighting renewable energy intelligence, predictive analytics, and cybersecurity as central to future-ready airport systems. Seker developed a hybrid fuzzy MCDM model to assess agility in low-cost carriers adopting sustainability-oriented innovations, underscoring energy flexibility as a strategic capability. Malefaki and colleagues compared normalization techniques in MCDM applications, demonstrating how normalization choice significantly affects rankings — a critical consideration given the data heterogeneity in airport energy planning. Zhou introduced a systems-level perspective, proposing integrated airport energy ecosystems within smart cities, leveraging hydrogen-based renewable-grid-storage architectures to ensure long-term flexibility and sustainability.
Despite these advancements, challenges persist in applying MCDM to complex aviation energy planning problems. Traditional methods often struggle to capture expert uncertainty, especially in interdisciplinary evaluations. While approaches such as those by Shahzad and colleagues proposed Pythagorean fuzzy-based methods to represent hesitant expert opinions, they typically lack integrated optimization or solution interpretability, limiting practical use. Moreover, large Pareto-optimal solution sets from algorithms like NSGA-II can overwhelm decision-makers, making it difficult to select contextually appropriate strategies. Although clustering offers potential interpretability improvements, such techniques are not widely operationalized in airport workflows. Method integration also remains fragmented: Mizrak, Polat, and Tasar’s model for airport sustainability lacked an optimization phase, while Raad and Rajendran’s approach did not include prioritization under uncertainty.
To address these challenges, recent studies advocate for modular hybrid frameworks that combine data-driven analysis, expert reasoning, and scenario-based iteration while maintaining transparency and interpretability. However, a fully integrated, uncertainty-resilient, and practically interpretable framework specifically tailored to airport energy retrofit decision-making remains underdeveloped, reinforcing the significance of the approach proposed in this study. Table 2 provides a consolidated summary of key studies discussed in Sect. 2.2. It highlights the main contributions of each study to the field of energy decision-making in aviation and airport infrastructure, emphasizing methodological advancements, application contexts, and integration of multi-criteria and optimization approaches.
While Multi-Criteria Decision-Making (MCDM) models have become essential for evaluating sustainability strategies in energy and infrastructure planning, particularly when facing conflicting objectives, a significant gap persists in approaches that integrate optimization, interpretability, and uncertainty-aware expert prioritization into a single framework. Many studies have focused on individual components of this analytical triad. For example, multi-objective optimization models using NSGA-II are widely used to generate Pareto-optimal solutions for complex energy system problems, especially to analyze trade-offs among cost, emissions, and energy savings. However, these solutions are usually presented as abstract sets without providing structured guidance or context for decision-makers, limiting their practical usability.
Clustering methods like K-Means are frequently applied for data classification in engineering and sustainability contexts, yet they are rarely employed as a bridging layer to organize Pareto solutions into clear, policy-relevant strategy groups that planners can easily interpret. Meanwhile, fuzzy prioritization techniques, such as those utilizing Pythagorean fuzzy sets, are often applied in isolation as ranking tools without incorporating insights from optimization outputs or clustering processes, restricting their contribution to integrated planning.
In the aviation sector, the need for comprehensive, integrated frameworks is particularly pronounced. Although airports have been rapidly advancing sustainability initiatives, most studies fail to deliver decision models that simultaneously incorporate expert input, evolving operational requirements, and technical trade-offs. For example, Mizrak, Polat, and Tasar developed a 2-tuple linguistic T-spherical fuzzy MCDM model with entropy weighting to prioritize sustainability strategies at Istanbul Airport, capturing expert hesitancy effectively but lacking an optimization mechanism to generate feasible retrofit scenarios. Similarly, Raad and Rajendran combined GIS, DEA, and MCDM for airport site selection in Iran but did not address expert uncertainty or multi-objective trade-offs, leaving the model incomplete for holistic planning needs. Gao and He employed the Fuzzy Best-Worst Method to rank factors supporting smart aviation development, yet their work centered on prioritizing factors rather than creating an end-to-end strategy design.
In broader energy and infrastructure contexts, recent studies have highlighted the importance of hybrid approaches but continue to focus on individual stages. For example, Sahoo and colleagues emphasized the advantages of integrating MCDM for renewable energy prioritization yet pointed out the lack of comprehensive frameworks that unify optimization, expert analysis, and interpretability. Karbassi Yazdi and co-authors examined the use of MCDM for selecting locations for green energy projects but did not integrate post-selection strategy clustering or expert uncertainty modeling Similarly, Jameel, Yasin, and Riaz proposed an integrated hybrid MCDM framework for sustainable energy project prioritization but still focused on independent ranking rather than actionable grouping and scenario optimization.
Further illustrating this gap, Talebi and colleagues advanced machine learning-driven MCDM models for sustainable infrastructure deployment, yet their models lacked integrated expert prioritization layers and interpretability mechanisms necessary for operational decision-making in complex airport settings. Santos and co-authors proposed combining GIS and BIM with MCDM for infrastructure planning, adding spatial and design dimensions but stopping short of integrating iterative optimization and uncertainty-resilient prioritization. Likewise, Parvaneh and Hammad applied MCDM to evaluate power-generating technologies for sustainability, focusing primarily on comparative ranking rather than providing holistic implementation guidance.
To address these methodological shortcomings, this study introduces a three-stage hybrid decision-making framework that integrates NSGA-II for multi-objective optimization, K-Means clustering for solution classification and interpretability, and Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP) for expert-based prioritization under uncertainty. In the first stage, NSGA-II explores trade-offs among key criteria such as cost, emissions reduction, energy savings, implementation time, and operational disruption. By combining fast non-dominated sorting, elitism, and crowding distance mechanisms, NSGA-II facilitates the generation of diverse Pareto-optimal strategies, offering planners comprehensive insight into competing objectives. In the second stage, K-Means clustering groups Pareto-optimal solutions into interpretable strategy clusters, converting abstract solution vectors into decision-ready categories like “cost-efficient,” “balanced,” or “high-impact.” Each solution vector comprises multiple performance indicators derived from NSGA-II outputs, and clustering helps minimize intra-cluster variance, thereby supporting clearer communication and easing stakeholder engagement in decision processes. Finally, the third stage introduces Pythagorean Fuzzy AHP to rank and prioritize these clustered strategies. Unlike traditional AHP, which relies on crisp judgments, Pythagorean fuzzy sets express expert opinions as a combination of membership, non-membership, and hesitancy degrees, allowing for a more realistic representation of expert uncertainty in decision environments characterized by incomplete or evolving data. This approach ensures that decisions remain robust and context-sensitive, especially in multidisciplinary airport planning teams.
Although recent literature demonstrates significant progress in adopting discrete techniques like fuzzy prioritization, clustering, or NSGA-II optimization, these methods are still used in a fragmented manner and do not fully integrate to address the combined needs of optimization, expert uncertainty, and strategic interpretability in airport energy planning. In summary, while past studies have successfully advanced individual elements, they often fail to provide a comprehensive, practical approach that addresses real-world implementation challenges in complex retrofit projects. By combining NSGA-II, K-Means clustering, and Pythagorean Fuzzy AHP, the proposed framework directly responds to this gap, delivering a holistic, uncertainty-resilient, and stakeholder-informed tool for developing actionable, technically feasible, and policy-aligned airport retrofit strategies.
Recent advances in decision science, energy systems engineering, and infrastructure planning have led to the development of increasingly sophisticated hybrid models that integrate optimization, machine learning, and fuzzy logic within multi-criteria decision-making (MCDM) frameworks. In the energy sector, combining evolutionary algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) with fuzzy prioritization and advanced weighting methods has become a prominent approach for managing complex, multi-objective decision environments. NSGA-II is widely recognized for its capacity to generate diverse sets of Pareto-optimal solutions across conflicting objectives, enabling planners to consider trade-offs among criteria like cost, emissions, and operational reliability. However, despite its mathematical strength in providing diverse solution frontiers, its outputs often lack the interpretive structures needed to guide practical stakeholder discussions or translate technical insights into actionable strategies for decision-makers. This disconnect has been emphasized in comprehensive reviews that call for the integration of interpretive and participatory layers into technical optimization frameworks to improve real-world applicability.
In parallel, there has been significant progress in the development and application of fuzzy MCDM models aimed at better capturing and representing expert uncertainty and hesitancy in complex evaluations. The introduction of Pythagorean fuzzy sets has marked a major milestone in this field, allowing decision-makers to express evaluations using degrees of membership, non-membership, and hesitancy all at once. This modeling capability provides a more nuanced and realistic way of capturing human judgments compared to traditional fuzzy or intuitionistic fuzzy methods. While these models are valuable for improving robustness and reflecting subjective judgments in scenarios with high uncertainty, they often remain isolated from optimization or clustering techniques. As a result, although they strengthen the credibility of expert evaluations, they are rarely utilized in conjunction with solution-space generation mechanisms such as genetic algorithms or scenario clustering, thereby limiting their practical effectiveness in comprehensive planning exercises.
Meanwhile, clustering algorithms have increasingly been recognized for their ability to improve the interpretability and usability of large, complex solution sets derived from optimization models. Classical methods like K-Means and its advanced variants, including probabilistic and genetic versions, have been successfully employed in group decision-making, market segmentation, and risk analysis. By transforming complex, multi-dimensional solution spaces into categorized and interpretable strategy groups, clustering helps reduce cognitive overload and supports clearer communication among diverse stakeholder groups. However, despite these advantages, clustering techniques are rarely integrated into infrastructure planning models as a crucial intermediate stage that links technical optimization with expert-informed prioritization. Comparative studies on normalization techniques and ranking logic across different MCDM approaches have revealed that inconsistencies in these steps can dramatically alter final decision outcomes, further underscoring the necessity for cohesive, integrated hybrid systems that ensure transparency and consistency throughout all stages of the decision-making process.
Within the aviation sector, there has been a growing body of research exploring the application of advanced MCDM methods to evaluate sustainability initiatives and inform strategic transformation efforts at airports. For example, a study at Istanbul Airport employed entropy weighting and a 2-tuple linguistic T-spherical fuzzy MCDM approach to prioritize sustainability strategies, successfully accommodating expert hesitancy but ultimately lacking integrated optimization or clustering stages that could guide decision-makers in selecting coherent retrofit packages. Similarly, research combining robust data envelopment analysis (DEA), geographic information systems (GIS), and MCDM for airport site selection focused on evaluating technical, environmental, and spatial criteria but did not account for uncertainty in expert prioritization or the dynamic trade-offs involved in large-scale energy retrofits. Other studies, such as those applying the Fuzzy Best-Worst Method to identify critical enablers for smart aviation development or employing hybrid AHP-SWOT models to enhance airport facility management, have shown promise in specific prioritization and assessment tasks but do not offer a fully integrated framework that connects optimization, solution grouping, and final strategy ranking into a unified workflow.
On the infrastructure and operational side, there have been important contributions focusing on energy management strategies and technological modernization in airports. For instance, recent research has proposed carbon-neutral energy strategies for airports by integrating renewable sources like solar and wind, yet these studies often lack structured decision models for comparing alternative retrofit or modernization pathways, leaving planners with generic recommendations rather than concrete, prioritized action plans. Technical assessments on airport electrification have detailed the equipment and grid upgrades required to support future demands, forecasting up to a fivefold increase in power requirements by 2050. However, these analyses typically do not include systematic decision-support frameworks to quantify trade-offs or align strategies with evolving policy and financial constraints, thereby limiting their value for strategic implementation. Research exploring hydrogen integration and smart airport ecosystems has outlined forward-looking visions and systemic connections to urban energy grids but has largely stopped short of offering modular, adaptive decision tools capable of supporting tactical prioritization and resource allocation in real-world airport environments.
Outside the aviation domain, hybrid MCDM frameworks have demonstrated significant advances in diverse sectors such as resilient supply chains, smart city logistics, and sustainable food production systems. For example, the integration of possibilistic programming with multi-stage hybrid models in supply chain resilience planning showcases how combining robust mathematical optimization with flexible uncertainty modeling can support dynamic, multi-level decision processes. Similarly, applying fuzzy MCDM to drone-based urban logistics and other infrastructure contexts has shown the adaptability and effectiveness of these frameworks for urban planning and operational design under uncertainty. However, these applications generally treat optimization, clustering, and prioritization as separate stages rather than elements of an interconnected and iterative decision flow. Studies involving fuzzy AHP-TOPSIS frameworks or benchmarking of metaheuristic algorithms illustrate how expert assessments can be synthesized with technical optimization, yet they often lack integrated categorization stages to translate solutions into actionable strategy bundles, which is essential for large-scale infrastructure and energy retrofitting projects.
Despite these advancements, a critical methodological gap persists in the literature. Many studies continue to approach optimization, classification, and prioritization as isolated tasks rather than as interconnected phases of a holistic decision-making system. While NSGA-II models are highly effective at generating diverse solution spaces that capture multi-objective trade-offs, they frequently lack interpretive layers that are necessary for practical stakeholder communication and iterative scenario analysis. Clustering approaches, while beneficial for organizing solution sets, are seldom embedded into operational infrastructure models to guide subsequent prioritization and implementation phases. Moreover, fuzzy MCDM techniques, though excellent at accommodating subjective expert inputs, often operate with fixed weighting schemes and do not leverage optimization outputs, thus failing to reflect the full strategic complexity required in modern energy and infrastructure planning.
The hybrid framework proposed in this study directly addresses these shortcomings by seamlessly integrating NSGA-II for trade-off-based solution generation, K-Means clustering for translating these solutions into clearly defined strategic groups, and Pythagorean Fuzzy Analytic Hierarchy Process for expert-informed prioritization under uncertainty. This modular and cohesive approach bridges existing methodological divides and empowers decision-makers to move from abstract technical analyses to concrete, prioritized strategies that are actionable and aligned with policy and operational goals. By offering a comprehensive, uncertainty-resilient, and stakeholder-oriented decision support structure, the framework sets a new standard for sustainable airport infrastructure planning and advances the state of the art in hybrid MCDM applications.
The proposed framework is conceptually grounded in a growing body of interdisciplinary decision science literature that emphasizes the need for robust, adaptive, and uncertainty-resilient models in complex infrastructure systems. At the theoretical level, the model draws from constructivist decision theory, fuzzy set theory, and evolutionary multi-objective optimization — all of which support rational decision-making in conditions of ambiguity, incomplete information, and multidimensional trade-offs. These theories are particularly relevant in the context of airport sustainability planning, where goals such as emissions reduction, financial viability, implementation feasibility, and regulatory compliance must be jointly considered across multiple stakeholder perspectives. The framework reflects the theoretical evolution of MCDM modeling from static and deterministic ranking tools toward dynamic, hybrid decision environments. It leverages conceptual advancements in epistemic uncertainty modeling, such as the adoption of Pythagorean fuzzy logic, which allows more expressive modeling of expert confidence and hesitancy compared to traditional fuzzy or intuitionistic sets. The prioritization logic embedded in the framework aligns with preference theory, while the classification layer draws upon unsupervised learning theory, particularly in its ability to transform solution spaces into human-readable patterns. Rather than offering a novel algorithmic component in isolation, the model represents a synthesis of theoretical ideas tailored to the realities of energy strategy development in high-stakes, regulated environments like airports. From an operational perspective, the model is designed to reflect decision-making as it occurs in practice, not merely as an abstract optimization problem. Decision-makers in airport planning often operate under fragmented information, evolving regulatory targets (such as ICAO’s LTAG or IATA’s net-zero roadmap), and the need to satisfy both internal and external stakeholders. This requires a model that supports not only analytical rigor but also strategic communication, consensus-building, and justification — functions often overlooked in traditional MCDM applications. Therefore, this study emphasizes model transparency, modularity, and adaptability, enabling its deployment in policy planning sessions, technical assessments, or cross-functional evaluation workshops. Importantly, this framework is positioned at the interface between theoretical innovation and applied infrastructure planning. It offers a structured pathway from computational generation of alternatives to stakeholder-informed strategic selection. In this way, the model provides both a conceptual bridge — linking formal methods to decision behavior — and an operational tool — supporting real-world sustainability transitions. This dual focus ensures the model’s value for both academic advancement and practitioner uptake, responding to the complex, multilevel challenges of sustainable aviation infrastructure development.
In this way, the model provides both a conceptual bridge — linking formal methods to decision behavior — and an operational tool — supporting real-world sustainability transitions. This dual focus ensures the model’s value for both academic advancement and practitioner uptake, responding to the complex, multilevel challenges of sustainable aviation infrastructure development.
