Multi-criteria Decision-making Tool Research Articles

Use of the Ahp Method in the Development of an Accident Prevention Program in the Chemical Industry In Brazil

Objective: The objective of this research was to investigate whether the decision-making for the prioritization of the implementation of the Accident Prevention Program (APP) elements in a chemical industry was consistent with the judgment of a group of professionals with different academic backgrounds and different positions. Theoretical Framework: From a PPA implemented in a chemical industry, the use of the multicriteria tool for decision-making, Analytic Hierarchy Process (AHP) and the Survey carried out with different professionals, a solid support for the result obtained was defined. Method: The methodology applied was qualitative, using bibliographic and documentary research, survey (data collection) and case study. Data collection was carried out through a questionnaire available on the Survey-Monkey platform for 100 professionals, with a respondent percentage of 44%. Results and Discussion: The results obtained demonstrate the great challenge for the corporate world on the need for a broad debate on the people and facilities safety, stimulating the use of multicriteria tools for decision-making in the case of implementing a program as complex as the APP. Implications of the Research: With investors' increasing concern about environmental, social and governance (ESG) issues, the research provides a reliable and solid framework for the implementation of an accident control program in the chemical industry. Originality: The research on the use of multicriteria analysis for the APP implementation is original, especially in the use of the Analytic Hierachy Process, a powerful tool for multicriteria decision-making.

A comprehensive review of parametric optimization of electrical discharge machining processes using multi-criteria decision-making techniques

Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013–22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi’s L9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decision-making tools.

E-waste circular economy decision-making: a comprehensive approach for sustainable operation management in the UK

AbstractE-waste generation has broadly increased worldwide and is called intense pressure on sustainable practice implementation firms by recycling and redesigning the products. Thus, e-waste operation management in developed countries like the UK has become the top priority and is subjected to multiple sustainable circular economies (CE) contributing factors, including social, technical, environmental, and governmental policies. The authorized decision-makers can benefit from a well-established systematic decision-making tool to assess and evaluate the e-waste operation management considering the potential CE contributing factors. An extensive literature overview is expanded to identify the most relevant and influential contributing factors to e-waste CE. The city of London Metropolitan has been selected as the case location. In this regard, it is necessary to utilize an advanced multi-criteria decision-making tool to explore the interdependency and causality of CE-relevant factors. The present study proposed an innovative decision-making approach to address the multiple contributing factors of causality, interdependency, data, and model uncertainty in practice. It uses the step-wise weighted influence nonlinear gauge system method integrated with Fermatean fuzzy linguistic sets. This study conducted a sensitivity analysis to evaluate the effectiveness of the proposed decision-making approach in e-waste operation management. The results are promising, clearly demonstrating the framework’s competence. The CE index, crucial in designing e-waste operation management strategies, was calculated to be 2.8036. Among the various factors analyzed, “Environmental Management Systems” emerged as the most significant driving factor. This underscores the critical need to improve environmental management systems within e-waste operations.

Multi-criteria decision making tool for sustainable concrete production using waste polyethylene terephthalate bottle cap aggregates

The widespread use of concrete has raised concerns about the consumption of its raw materials. To address this issue, researchers are actively exploring alternative waste materials to reduce the environmental impact of the construction industry. With this objective current study aims to investigate potential use of waste polyethylene terephthalate (PET) bottle cap aggregates on the characteristics of concrete, both in its fresh and hardened states. Three variations of modified waste PET aggregates (PETA): full (F), half (H), and quarter (Q), with the aim of evaluating their suitability as replacements for conventional coarse aggregates (CCA) was examined. The research assesses the effects of different PETA proportions (3%, 5%, and 8%) on concrete workability, compressive strength, splitting tensile strength, and flexural strength. The findings reveal that PETA-F and PETA-H adversely affect workability, primarily due to their larger surface area and difficulty in achieving homogeneous blending with other ingredients. In contrast, PETA-Q exhibits superior workability. The use of PETA-Q results in reduced compressive strength due to its smaller size and limited load-bearing capacity. Notably, PETA-H demonstrates improved compressive strength after 28 days. Interestingly, PETA-Q shows enhancements in splitting tensile and flexural strength, making it the top-performing alternative for fresh and hardened concrete properties. Overall, this research indicates that PETA-Q is most suitable for sustainable concrete production. Furthermore, the application of a multi-criteria decision-making tool validates our findings and determines that a 5% replacement of CCA with PETA is optimal, as higher replacements result in strength reduction. The utilization of PETA not only minimizes CCA consumption but also contributes to the development of eco-friendly, sustainable concrete. This study underscores the potential of environmentally conscious concrete and encourages the adoption of sustainable solutions for plastic waste management.

Novel energy efficient in-situ bitumen upgrading technology to facilitate pipeline transportation using natural gas: Sustainability evaluation using a new hybrid approach based on fuzzy multi-criteria decision-making tool and techno-economic and life cycle assessment

An assessment of the techno-economics and life cycle impacts of a new in-situ bitumen upgrading technology using natural gas has been conducted in this study. A comparison was also made between the two other commercial technologies such as hydrogen-assisted upgrading (HAU) and dilbit production with the novel proposed methane-assisted upgrading (MAU). To determine the total capital investment (TCI), the total operating cost (TOC), and the energy consumption, a process simulation was undertaken. HAU process has the highest TCI among all the processes and the lowest TCI belongs to dilbit production. In terms of TOC, MAU has the lowest operating costs compared to other technologies. The Internal Rate of Return (IRR) and Payback Period were calculated in three scenarios over the project's lifetime. As a result of the techno-economic analysis, the MAU process has the highest IRR and the shortest payback period for all scenarios. A comparison was also made between the proposed MAU process and other research studies. Results indicate that MAU with the total operating cost of (34.1 $/bbl. bitumen) and transportation cost of (1.1 $/bbl. Bitumen for 500 km pipeline transportation) has the best results. Based on the life cycle assessment results, it was determined that the MAU process has a lower impact on climate change, human toxicity, ecotoxicity, eutrophication potential, and resource usage than the HAU process. Finally, the sustainability index for processes was calculated using a hybrid approach based on fuzzy decision modeling, TEA, and LCA results. As compared to other commercial technologies like dilution, heating, and dilbit production, the MAU process has the highest level of sustainability.

Performance measurement of factors affecting consumer experience in healthcare services using fuzzy and grey techniques

PurposeThe importance of consumer experience in service industries, particularly healthcare, is widely acknowledged as it captures the intricacies of quality management. In tandem with the emerging research trends that evaluate service excellence through user experience, this study renders a performance analysis of the dimensions of consumer experience that individually or collectively shape healthcare consumers’ perceptions of service quality.Design/methodology/approachA cross-sectional study was conducted across 13 mid-tier corporate hospitals to collect data from 438 patients. The data was processed through factor analysis in SPSS to confirm sample adequacy and factor extractability. Further, two independent multi-criteria decision-making (MCDM) tools, Fuzzy Technique for Order Performance by Similarity to Ideal Solution (F-TOPSIS) and Grey Relational Analysis (GRA), were executed to render performance analysis of identified factors.FindingsUsing F-TOPSIS, factors such as “information” and “hospital environment” received higher performance ratings, while items related to “communication with doctors” and “humanistic care” received lower rankings. Minor yet anticipated deviations were observed while verifying performance scores using GRA. Nonetheless, both outcomes exhibited a strong correlation coefficient of 97.14%, confirming analytical consistency.Originality/valueHitherto, such usages of hybrid MCDM techniques have rarely been executed to convey a clear understanding of consumers’ experiences in healthcare services. Moreover, the findings provide a clear insight into consumers’ key response areas, which can further be translated to maximize consumer gratification, thus assisting healthcare managers in improving service performance and clinical decision-making.

Closed-form solutions of consistency ratio in best worst method minmax optimization model: max of edge error matrix and minmax edge error determinant methods

The Best Worst Method (BWM), a reduced version of the AHP, is a recent multi-criteria decision-making tool based on pairwise comparisons with reference to the best and worst criteria. Consistency Ratio (CR) measurement for the rating quality and prioritizations is still a controversial topic. Firstly, the computation for the current CR of BWM must rely on a software optimization solver to find the optimal values, and the solver may not always guarantee the exact optimal solutions, especially if the computational cost settings are not large enough for higher number of criteria. Secondly, much effort to evaluate optimization algorithms is needed to find the best solutions with the least computational resources due to diverse solvers possibly leading to different results with different performances. Thirdly, optimization programming code is not trivial to be implemented for general BWM users. To address these issues, this paper presents the closed-form solutions, Max of Edge Error Matrix (MEEM) (Eq. (44) of Theorem 4) and Minmax Edge Error Determinant (MEED) (Algorithm 1), to replace the BWM optimization models to directly calculate the CR values. Two simulations have been performed with a basic laptop using a single process. One simulation of twenty thousand random pairs of vectors took 26.34 h to perform to verify that the approximate results are higher than or very close to the exact closed-form values of both methods when high computational cost is allocated for the solver to increase the precision. Another simulation of one million random pairs of vectors only took 1.27 h to perform to verify that the MEED and MEEM methods always produce the same results for the number of criteria up to nine. The computational time for the exact results is dramatically reduced when the solver is not needed. The advantages of the proposed solutions include the following: the software to solve the optimization model to obtain CR is unnecessary, and the proposed calculation is extremely efficient to obtain the exact accuracy. The two-step optimization model can preserve the fixed Minmax Edge Error to find the weights which add up to one, which is the condition to determine if the model reaches exact optimal solutions. As the CR optimization model produces multiple versions of weights, which are recommended not to be used, the new method does not need to compute the unnecessary weight values to get the Minmax Edge Error. With the provision of equations leading to closed forms, users can understand the properties of CR in much clearer perspectives. Due to the computational efficiency and explainability, the proposed closed forms can replace the CR optimization model to compute CR efficiently and accurately for all diverse applications using BWM.

Spherical fuzzy sets based integrated DEMATEL, ANP, VIKOR approach and its application for renewable energy selection in Turkey

Renewable energy continues occupying the global agenda for the low-carbon transition. Identifying suitable sources for renewable energy involves different criteria and alternatives, which can be defined as a Multi-Criteria Decision-Making (MCDM) problem. MCDM methods can be enriched with Group Decision Making (GDM) to merge individual judgments into group opinions, as well as with fuzzy sets to better simulate human opinions. To handle uncertainties of DMs’ preferences, a recently developed fuzzy set extension, the Spherical Fuzzy Sets (SFSs), is combined in this study with different MCDM tools to establish a decision support tool for the energy source selection problem. This article proposes a novel MCDM approach that integrates SF-DEMATEL (Spherical Fuzzy Decision-Making Trial and Evaluation Laboratory), SF-ANP (Spherical Fuzzy Analytic Network Process), and SF-VIKOR (Spherical Fuzzy Vlse Kriterijumska Optimizacija Kompromisno Resenje) algorithms in a GDM environment and presents its application in a case study for selecting renewable energy sources in Turkey. Comparing wind, geothermal, solar, hydropower, and biogas, the proposed method highlighted both wind and solar energy as a compromise solution as the most suitable options for Turkey. It has been found that compared to similar fuzzy sets, SFS can better reflect the hesitation degree to address the lack of knowledge and errors in the membership function definitions. The robustness and plausibility of these findings are demonstrated with sensitivity and comparative analyses, suggesting this MCDM method can also be applied to similar problems in other settings.

Multicriteria decision-making tool for investigating the feasibility of the green roof systems in Egypt

Urbanization in Egypt detracts from green spaces, reduces the per capita green ratio, and increases adverse effects such as heat islands, air pollution, and energy consumption. In addition, it affects social human comfort issues. In this context, building rooftops is a potential solution that could reduce the impact of green space scarcity. Such a solution has multiple evidence-based environmental, economic, and social benefits. Consequently, numerous governmental and private initiatives have spread the rooftop greening concept in Egypt. These initiatives have adopted several planting systems, such as soil-based, deep-water culture, and nutrient film technique systems. This manuscript examines these prevalent systems through environmental, economic and social lenses. This paper pioneers a user-centric tool to facilitate the system selection that aligns with individual needs. An analysis was conducted to ascertain the value of various factors influencing system choice, encompassing a literature review, expert opinion solicitation, market survey, and energy simulation. The Analytical Hierarchy Processes methodology was proposed to appraise the factors, aiding in arriving at an informed decision. The paper presents a novel contribution by studying many factors spanning diverse scientific domains. Furthermore, creating an accessible decision-support tool encapsulates a substantial addition to the body of knowledge.

A novel Multi-Criteria Decision Making framework based on Evidential Reasoning dealing with missing information from online reviews

Online reviews are a valuable tool for Multi-Criteria Decision Making (MCDM) because they provide insightful information on several aspects. However, not all reviews yield complete information on all the necessary criteria for the MCDM problem. In practice, users might not effectively analyze all of them due to a lack of knowledge or time constraints. Despite this shortcoming, these online reviews still offer useful information that would be lost if neglected. Therefore, this paper proposes a framework that offers a new approach to using online reviews in MCDM and effectively addresses missing information on a given criterion or criteria. This new approach first establishes an information processing mechanism to transform online reviews into Probabilistic Linguistic Term Sets (PLTSs) with interval uncertainty. Afterward, a weight determination model is defined to calculate the criteria weights, which are given as interval values to reflect the uncertainty of the criteria and consider information from online reviews to better capture the opinions of reviewers. Next, the Evidential Reasoning (ER) algorithm is extended to the PLTSs with interval uncertainty environment to fuse the evaluation information and generate the interval-valued expected utilities of the alternatives by considering the uncertainty related to the missing information. Finally, a case study is conducted to illustrate the implementation process of the proposed framework, and a comparative analysis is carried out to evaluate its performance.

Development of a new tool for seismic risk assessment and multi-criteria decision making

The present paper introduces a new risk-informed based tool that can support disaster management authorities in making decisions in response to earthquakes at local and regional scales by using estimated physical and socio-economic losses. The risk-informed based tool is building upon existing risk assessment methods, and multi-criteria decision-making (MCDM) methodology. The integrated risk assessment methods are adapted from the earthquake loss estimation tool SELENA. The integrated MCDM methodology, allowing selection of optimal mitigation measures by taking into account the priorities and preferences of disaster management authorities, was developed within the EU Horizon 2020 TURNkey project [1]. The new tool presented in this study also allows easy integration and incorporation of existing database and models that can be openly available. The tool is characterized with user-friendly interface and flexible format of the input and output data, that can be easily understood by different actors in disaster risk management.

An Integrated Entropy-TOPSIS Approach for Electric Vehicle Selection

Electric vehicles (EVs) have gained a significant role in reducing emissions of harmful greenhouse gases, which can result in serious effects like global warming, deteriorating air quality, etc. Thus, it helps tackle concerns related to the environment and health of the general public. Many nations are switching towards this new era of electric mobility, thus promoting and contributing to the goal of sustainable development. Due to the rapid evolution and expanding scope of the electric vehicle market, selecting the best alternative between various existing EV models was tedious. In this paper, the electric vehicle in the Indian market context was considered along with the TOPSIS approach as a multi-criteria decision-making tool (MCDM) for choosing and ranking the best available choice for the electric vehicle present in the Indian market for the customer. The entropy method is used to obtain the weights associated with the criteria. For this study, thirteen electric vehicles have been selected as alternatives. This study contributes by giving a real preference order, considering a broad set of selection criteria, and evaluating the best alternative from the existing EVs.

Life cycle assessment and economic analysis of Reusable formwork materials considering the circular economy

Economic development and population growth have impacted on fossil-based energy consumption, contributing to environmental pollution. Adopting circular economy research is more pressing than ever to ease pressure on the environment and the economy. Evaluating the best construction materials is not new. To date, many researchers have assessed materials using various criteria. Formwork differs from other construction materials in terms of serviceability and reusability. These materials may be reused multiple times (from 7 to around 50 times). This raises the question of which material is the best from a sustainability perspective. In this paper we have evaluated four of the most widely-used formwork materials used in the construction of buildings in Malaysia. These include plastic, steel, plywood and timber. Evaluations of life cycle assessment (LCA), embodied energy, and life cycle cost (LCC) were conducted from cradle to cradle. For a single use of formwork, timber is best in all categories except human non-carcinogenic toxicity. However, when 50 reuses are considered for the same wall a completely different result arises. In the environmental category, steel formwork produces the lowest emissions and impact in all categories except global warming potential (GWP). Plastic formwork has the lowest carbon emissions. In terms of embodied energy and cost, plastic formwork presents the best option being approximately 20% lower than steel formwork. Because of the inconsistency in the results for LCA, embodied energy, and LCC for 50-cycles of usage, a multi-criteria decision-making (MCDM) tool was used to normalize the results. The MCDM shows that plastic formwork is an ideal choice for sustainability among the alternatives considered.

Evaluating the performance of electrocoagulation system in the removal of polystyrene microplastics from water

Emerging pollutants, particularly microplastics, present a significant threat to both the environment and human health. Traditional treatment methods lack targeted strategies for their removal. This study thoroughly investigated the efficacy of electrocoagulation as a method for efficiently extracting microplastics from water. Various critical operational parameters, including electrode combinations, pH levels, electrolyte concentrations, electrode geometries, configurations, current intensities, and reaction times, were systematically examined. The study systematically examined the impact of different combinations of aluminium (Al) and stainless steel (SS) electrodes, including Al–Al, SS-SS, Al-SS, and SS-Al. Among these combinations, it was found that the Al–Al pairing exhibited outstanding efficiency in microplastic removal, while simultaneously minimizing energy consumption. Initial pH emerged as a critical parameter, with a neutral pH of 7 demonstrating the highest removal efficiency. In the pursuit of optimizing parameters like electrolyte concentrations, electrode geometry, and configuration, it's noteworthy that consistently achieving removal efficiencies exceeding 90% has been a significant achievement. However, to ascertain economic efficiency, additional factors such as energy consumption, electrode usage, and post-treatment conductivity must be taken into account. To tackle the complexity posed by various parameters and criteria, using multi-criteria decision-making tools like TOPSIS is essential, as it has a track record of effectiveness in practical applications. The electrolyte concentration of 0.5 g L−1 is identified as optimal by TOPSIS analysis Additionally, the TOPSIS highlighted the superiority of cylindrical hollow wire mesh electrodes and established the monopolar parallel configuration as the most effective electrode connection method. The investigation carefully evaluated the effect of reaction time, determining that a 50-min window provides optimal microplastic removal efficiency. This refined system exhibited remarkable proficiency in eliminating microplastics of varying size ranges (0–75 μm, 75–150 μm, and 150–300 μm), achieving removal efficiencies of 90.67%, 93.6%, and 94.6%, respectively, at input concentration of 0.2 g L−1. The present study offers a comprehensive framework for optimizing electrocoagulation parameters, presenting a practical and highly effective strategy to address the critical issue of microplastic contamination in aquatic environments.

Analytical Hierarchical Process as a Multicriteria Decision Tool in Material Selection for Prefabricated Wood Buildings

The popularity of prefabricated wooden buildings is increasing in North America, but choosing suitable materials for these structures can be complicated. This can lead to problems like financial losses, production delays, and lower quality. Therefore, the main goal of this study was to use the Analytical Hierarchy Process (AHP) decision-making tool to rank the criteria for material selection for prefabricated wood buildings in Canada and the United States. The methodology involved surveys experts in the prefabricated wood construction industry from Canada and the United States. The data obtained from the questionnaires utilized for the AHP analysis were modeled using R programming language. The results revealed that for structural materials, the top five subcriteria were safety and security of building occupants (0.234), location, shape, and height of the building (0.218), comfort, satisfaction, and well-being of the building (0.155), occupant health (0.121), and availability of materials (0.098). For selecting envelope materials, the top five subcriteria were comfort, satisfaction, and well-being of the building (0.252), safety and security of building occupants (0.206), location, shape, and height of the building (0.178), occupant health (0.132), and availability of materials (0.078).

Research on the evaluation of the livability of outdoor space in old residential areas based on the AHP and fuzzy comprehensive evaluation: a case study of Suzhou city, China

ABSTRACT China has experienced rapid urban development and renewal. However the infrastructure of many old residential areas (ORAs) more than 20 years old is deteriorating, affecting the living standards of residents and urban vitality. In this paper, the outdoor space of ORAs is studied. The aim is to discuss the livability evaluation system for the outdoor space of ORAs, to analyze the weights of elements, and to analyze the livability grade of ORAs, providing a clear reference for decision makers and city managers. Doing so requires the use of relatively fair and objective elements. As a comprehensive multicriteria decision-making tool, the analytic hierarchy process (AHP) is used to solve these problems. This paper optimizes the element weights obtained through the AHP by using the expert judgment weight method, and it evaluates the livability score and grade by using fuzzy comprehensive mathematics.The results show that the parameters obtained by optimizing the element weights have clear meanings and that the fuzzy comprehensive evaluation (FCE) results are robust. Taking the elements with high weights but low FCE scores as the priority targets, incorporating existing design guidelines and cases, this paper proposes a transformation scheme for the object plot and visualizes the analysis results.

Multi-Criteria Decision-Making Tools for Project Selection by International Conglomerates

Multi-Criteria Decision-Making Tools for Project Selection by International Conglomerates

Comparative evaluation of alternatives for management of wood wastes by using multi-criteria decision tools

In Turkey, the wood industry is a prevailing sector generating economically valuable by-products such as sawdust, wood chips and wood shavings. The utilization of these materials supports an important contribution to the revival of the economy. Selection of the best management alternative considering economic and environmental factors synchronously requires the application of statistical decision-making methods. The significance of this study is to determine the suitability of promising alternatives for the utilization of wood wastes (production of pellet, panel, cemented wood composites and pyrolysis) by using multi-criteria decision-making methods (PROMETHEE and FPROMETHEE). Super Decision Program was used to evaluate the importance of the selected criteria (environmental impact, operation risk, installation cost, applicability, operation cost and market competition) based on data obtained from the 9-scale questionnaire. In PROMETHEE and FPROMETHEE applications, both equally weighted and calculated importance of criteria was considered. According to results of AHP, installation cost was the most important criterion with 31% ratio. Results of the study showed that pellet production was the best alternative in all applications with the highest-ranking values. Wood-based panel production was followed by pellet production; however, it was seen that the production of cemented wood composites and pyrolysis are not suitable for the utilization of wood wastes.

Material Selection Framework for Lift-Based Wave Energy Converters Using the Fuzzy Technique for Order Preference by Similarity to Ideal Solution

Material selection is a crucial aspect in the design of reliable, efficient and long-lasting wave energy converters (WECs). However, to date, the development of tailored methodologies applied to the material selection of WECs remains vastly unexplored. In this paper, a material selection framework for the case of lift-based WECs is developed. The application of the methodology is demonstrated with the hydrofoils of the device. Offshore steel, high-strength offshore steel, aluminium alloys, and carbon- and glass-fibre-reinforced composites are considered and evaluated subject to relevant criteria for wave energy converters, namely structural reliability, hydrodynamic efficiency, offshore maintainability, total manufacturing cost and environmental impact. Candidate materials are assessed via fuzzy TOPSIS for three scenarios of the life cycle of the WEC: conceptual, commercial and future projection stages. Results show that the choice of optimal materials could change from present to future and that multi-criteria decision-making tools aided by a fuzzy approach are useful design tools for novel WECs when field data are scarce. Hence, methodologies such as the ones presented in this work can help in reducing the probability of mechanical failures of emerging WEC technology.

Sustainable stationary hydrogen storage application selection with interval-valued intuitionistic fuzzy AHP

The transition to a hydrogen-based economy necessitates a comprehensive evaluation of different hydrogen storage options, considering their sustainability performance. This study innovatively applies the Interval-Valued Intuitionistic Fuzzy Analytic Hierarchy Process (IVIF-AHP) to evaluate and compare four hydrogen storage options: Compressed Hydrogen Gas (CHG), Cryogenic Liquid Hydrogen (CLH), Metal Hydride (MH), and Underground Hydrogen (UH). The evaluation criteria are derived from four dimensions of sustainability: economic, environmental, social, and technical performance, each further decomposed into sub-criteria. The study's novelty lies in using a novel intuitionistic fuzzy AHP, offering a more nuanced and robust understanding of the trade-offs between the various options and effectively capturing the vagueness and subjectivity inherent in human decision-making. Through this methodology, CHG emerged as the most promising option with a preference score of 0.487, closely followed by UH with a score of 0.453. The lowest preference score was accorded to MH, with a score of 0.301. These quantitative insights underscore the relative sustainability performance of each technology under the defined criteria. The findings contribute to the growing body of literature on sustainable hydrogen storage, providing policymakers and practitioners with a multicriteria decision-making tool that captures the complexity of sustainability considerations. This study underlines the critical role of holistic, multicriteria evaluations in advancing sustainable hydrogen storage. It encourages further exploration and validation of its approach in different contexts and with updated technological advancements.