Criteria Importance Through Intercriteria Correlation Research Articles

Assessing the adoption barriers for the AI in food supply chain finance applying a hybrid interval-valued Fermatean fuzzy CRITIC-ARAS model.

The identification and evaluation of barriers to artificial intelligence (AI) adoption in food supply chain finance (FSCF) can be addressed as a multiattribute decision-making problem. However, only a few studies have reported the application of decision models for evaluating barriers to the implementation of AI in FSCF, especially within an uncertain context. Hence, this work explores the evaluation issue of implementation barriers via an integrated decision model. In this model, the conventional additive ratio assessment (ARAS) model integrated with the Choquet integral and criteria importance through intercriteria correlation (CRITIC) is extended into the interval-valued Fermatean fuzzy (IVFF) setting for ranking the barriers. The IVFF weighted average operator based on the Choquet integral is introduced to form a group decision matrix. Then, the developed ARAS model with the IVFF-CRITIC method is proposed to evaluate the implementation barriers for AI in FSCF, which can depict the interactions between the barriers. Finally, a case of an FSCF, including four participants, is presented to illustrate the application of the reported model and demonstrate its reliability. The result shows that "Data privacy" ([Formula: see text]) is the main barrier impeding AI adoption in FSCF, and the participant "small and medium-sized processing enterprises" ([Formula: see text]) has the highest barrier level to AI adoption.

A Spatial Decision-Support System for Wind Farm Site Selection in Djibouti

The escalating energy demand in Djibouti requires the investigation of renewable energy sources, with wind energy emerging as a promising solution. To ensure the long-term efficiency and sustainability of wind energy projects, it is imperative to determine suitable sites for wind farm construction. When selecting a suitable site for a wind farm, there are multiple criteria to consider, such as wind velocity, ground slope, and distance to urban areas. Nevertheless, the current body of the literature reveals that no previous research has been conducted to explore an approach which involves multiple criteria to determine suitable sites for wind farms in Djibouti, as opposed to solely considering wind energy potential. This study proposes a spatial decision-support system to address the research gap in the selection of wind farm sites. Seven criteria are simultaneously evaluated in this system, including wind velocity, changes in wind direction, ground slope, distance to urban areas, distance to road network, distance to energy transmission networks, and land use. The CRITIC (Criteria Importance Through Intercriteria Correlation) method is used to objectively calculate the weights of the criteria. According to the results of performing the CRITIC method, wind velocity and distance to energy transmission networks were determined to be the most important criteria, while ground slope and land use were determined to be the least important criteria in comparison to others. A final suitability map showing the possible locations of wind farms in Djibouti was generated by considering the said criteria and their respective weights. The final suitability map reveals that the most suitable sites for the development of wind farms in Djibouti are located in the northeastern area between Obock and Khor-Angor, the southeastern area encompassing Lakes Ghoubet and Bara, and the southwestern area stretching from Lake Abbe to the Hanlé region. Using the proposed spatial decision-support system, decision makers would be empowered to make strategic and well-informed decisions when selecting the most suitable site for a wind farm in Djibouti.

A Machine Learning and Fuzzy Logic Model for Optimizing Digital Transformation in Renewable Energy: Insights into Industrial Information Integration

The most essential criteria to improve digital transformation in renewable energy projects should be identified. This situation helps the companies to use limited financial budgets and human resources in the most efficient way. Therefore, a new study is needed to analyze the performance indicators of the digital transformation process in renewable energy projects. Accordingly, this study aims to identify the most significant performance indicators of digital transformation for these projects. A three-stage machine learning and fuzzy logic-based decision-making model has been constructed in this process. The first stage includes the weight calculation of the experts by dimension reduction methodology. Secondly, essential factors of digital transformation in renewable energy projects are examined via Fermatean fuzzy criteria importance through intercriteria correlation (CRITIC). The final part consists of the ranking of emerging seven countries with Fermatean fuzzy weighted aggregated sum product assessment (WASPAS). On the other side, combined compromise solution (CoCoSo) method is also taken into consideration in this process to make a comparative evaluation. The main contribution of this study is the generation of novel machine learning and fuzzy logic integrated decision-making model to make evaluation related to the digital transformation of renewable energy projects. In this model, machine learning technique is used to determine the importance weights of the experts. Similarly, integrating Fermatean fuzzy numbers with CRITIC and WASPAS techniques also contributes to the literature by minimizing the uncertainty and identifying the relationship between the items. The findings demonstrate that employing qualified personnel plays the most critical role in increasing digital transformation in renewable energy projects. Additionally, government support is very critical in the successful implementation of digital transformation processes in renewable energy projects.

CRITID: enhancing CRITIC with advanced independence testing for robust multi-criteria decision-making

In multi-criteria decision-making and model evaluation, determining the weight of criteria is crucial. With the rapid development of information technology and the advent of the big data era, the need for complex problem analysis and decision-making has intensified. Traditional CRiteria Importance Through Intercriteria Correlation (CRITIC) methods rely on Pearson correlation, which may not adequately address nonlinearity in some scenarios. This study aims to refine the CRITIC method to better accommodate nonlinear relationships and enhance its robustness. We have developed a novel method named CRiteria Importance Through Intercriteria Dependence (CRITID), which leverages cutting-edge independence testing methods such as distance correlation among others. This approach enhances the assessment of intercriteria relationships. Upon application across diverse data distributions, the CRITID method has demonstrated enhanced rationality and robustness relative to the traditional CRITIC method. These improvements significantly benefit multi-criteria decision-making and model evaluation, providing a more accurate and dependable framework for analyzing complex datasets.

A novel multi-criteria decision making method to evaluate green innovation ecosystem resilience

Green innovation ecosystem resilience evaluation is crucial for ensuring sustainable development in dynamic environments. By comprehensively assessing resilience, it helps identify risks and facilitate effective responses, thus promoting sustainable development. To effectively evaluate the resilience of green innovation ecosystems, this paper proposes a model based on the interval-valued intuitionistic fuzzy multi-criteria decision-making (MCDM) method, designed to support decision-makers (DMs) in resilience evaluation. In this model, we improve the Criteria Importance Through Intercriteria Correlation (CRITIC) method by introducing Spearman's correlation coefficient to more accurately determine the objective weights of various indicators based on their interrelationships. Furthermore, we propose an Interval-Valued Intuitionistic Fuzzy Hybrid Average Aggregation (IVIFHAA) operator, which considers both the subjective and objective weight order for aggregating expert evaluation results. Additionally, a scoring function is introduced to generate ranked evaluation results. We also establish a resilience evaluation criteria system covering interconnectivity, sustainability, and diversity within green innovation ecosystems to assist DMs in resilience evaluation. Finally, to validate this method, we apply it to assess the resilience of regional green innovation ecosystems and compare it with other methods. The results demonstrate that the improved operator effectively reduces information loss and improves evaluation efficiency, while the improved CRITIC method handles outliers during the evaluation process.

Comparison of Criteria Weight Determination Using MEREC and CRITIC Methods in Choosing The Best Student Accommodation with the MOORA Method Case Study: Coventry University

One of the challenges faced by IISMA Awardees and students in general in Coventry University is choosing a comfortable place to live. Although various student accommodations are provided, differences in facilities and considerations from other parties such as parents and friends make the selection process complicated. This study develops a decision support system to help students choose student accommodation objectively without any intervention from others and provides a comparison of the use of different combinations of methods as additional guidance in the decision-making process. Two methods, Method Based on the Removal Effects of Criteria (MEREC) and Criteria Importance Through Intercriteria Correlation (CRITIC), are used to weight the criteria. The Multi-Objective Optimization (MOORA) method is used to determine the best alternative after the weight calculation is known. The results using a combination of the MEREC-MOORA method and a combination of the CRITIC-MOORA method place Alternative 5 (A5) in first place, while the remaining alternatives show a similar ranking order. In this study, scenario testing was also carried out by deleting and adding criteria and alternatives which then provided ranking results with a positive correlation even though different combinations of methods were used in determining the ranking.

Evaluating the Business Performance of Seaport Enterprises in Vietnam

International economic trade plays a crucial role in global merchandise supply chains; as such, the development of industries is critical to enhance the expansion and growth of marine transportation services. In Vietnam, commercial activities are rapidly expanding, and there is a strong foundation for developing seaport enterprises. This research aims to evaluate and rank the business performance of seaport enterprises in Vietnam by analyzing the ratio of various variables in the financial statements from 2019 to 2023 via an integration of the Criteria Importance Through Intercriteria Correlation (CRITIC) and Combined Compromise Solution (COCOSO) methods. First, the weight of each variable will be determined by the CRITIC method. Second, the efficiency and rank of each alternative will be identified by the COCOSO method. These empirical results revealed the business performance and position of each seaport enterprise based on the efficiency scores, providing valuable insights into their financial operations. Additionally, the findings described a comprehensive overview of maritime transport and recommended actionable solutions to improve the business performance of seaport enterprises in the future.

The CRITIC-AHP performance degradation staged fusion evaluation of wet clutch friction components

The performance of wet clutches plays a crucial role in the efficiency and durability of mechanical transmission systems. As a vital component of the clutch, the degradation of friction components directly affects its stability and reliability. To investigate the performance degradation of friction components, we establish a multi-indicator evaluation system and conduct an accelerated life test, which is validated by an accelerated model. Next, we build a Random Forest model to classify the friction stage. Criteria Importance through Intercriteria Correlation (CRITIC) and the Analytic Hierarchy Process (AHP) is proposed to assign weights for each degradation indicator during different stages. A Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) scoring method combines indicator weights to construct a CRITIC-AHP staged fusion evaluation model of friction components. Notably, the average relative error of the evaluation results of the model is 9.35%.

Holistic Methodology of Hypersoft in Tandem with Uncertainty-based Triangular Neutrosophic for Wave Energy Converter Optimization

Global warming, environmental degradation, and climate change have recently emerged as compelling arguments against the combustion of fossil fuels. As well Eliminating and forgoing the intake of fossil fuels is also encouraged by the Sustainable Development Agenda's goals. The healthy substitute for fossil fuels, renewable energy is one of the main devoid power sources found in our natural surroundings. An effective proposition for harnessing wave energy with an emphasis on increasing energy and efficiency are wave energy converters, or WEnCos. It contributes to producing a clean, limitless energy source that might drastically lessen our need for fossil fuels. Generally speaking, the selecting of the appropriate converter is an important endeavor that this study takes on. The process of selecting the optimal converter has been conducted based on effective benchmarks or criteria and its attributes. Multiple Criteria Decision Making (MCDM) techniques seem to be effective tools when it comes to handling the complex issue of benchmarking WEnCos. These approaches consider a variety of criteria, allowing for a comprehensive and nuanced examination. Each utilized technique plays vital role in our decision problem. Hence, CRiteria Importance Through Inter-criteria Correlation (CRITIC) method—which determines the weights of criteria and its attributes in decision making problems. These weights are utilized in Weighted Sum Product (WISP) method. Totally, these techniques are implemented under uncertainty theory of Neutrosophic which combined with soft sets notion to generate Triangular Neutrosophic HyperSoft (TrNSHSS). Totally, all these techniques are contributed to constructing soft decision-making model for analyzing and recommending optimal WEnCo.

Quality Evaluation of Public Spaces in Traditional Villages: A Study Using Deep Learning and Panoramic Images

In the context of rapid urbanization, public spaces in traditional villages face challenges such as material ageing, loss of characteristics, and functional decline. The scientific and objective assessment of the quality of these public spaces is crucial for the sustainable development of traditional villages. Panoramic images, as an important source of spatial data, combined with deep learning technology, can objectively quantify the characteristics of public spaces in traditional villages. However, existing research has paid insufficient attention to the evaluation of the quality of public spaces in traditional villages at the micro-scale, often relying on questionnaires and interviews, which makes it difficult to meet the needs of planning and construction. This study constructs an evaluation system for the quality of public spaces in traditional villages, taking national-level traditional villages in the Fangshan District of Beijing as an example, based on traditional field research, using deep learning and panoramic images to automatically extract the features of public spaces in traditional villages, using a combination of the Analytic Hierarchy Process (AHP) and Criteria Importance Through Intercriteria Correlation (CRITIC) methods to determine the weights of the indicators and applying the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to evaluate the quality of public spaces in traditional villages. The study found that the quality of public spaces in Nanjiao Village is Grade I; Shuiyu Village and Liulinshui Village, Grade III; and Heilongguan Village, Grade IV and that there is still much room for improvement in general. The evaluation results match well with the public’s subjective perceptions, with an R2 value of 0.832, proving that the constructed evaluation system has a high degree of accuracy. This study provides a scientific basis and an effective tool for the planning, design, and management of public spaces in traditional villages, which helps decision-makers better protect and utilize them.

Assessing Southeast Asia countries’ potential in the semiconductor supply chain: an objectively weighting multi-criteria decision-making approach

The semiconductor industry serves as a key enabler of technological progress and economic growth on a global scale. With Southeast Asia emerging as a region of increasing importance in the global supply chain landscape, understanding the potential for its countries to participate in the semiconductor supply chain becomes crucial. Despite extensive research on global supply chain dynamics, a focused analysis on the role of Southeast Asia within this crucial industry has been notably absent, particularly considering recent geopolitical and economic shifts. This gap has been filled by employing a rigorously designed Multi-Criteria Decision Making (MCDM) approach that integrates the Criteria Importance Through Intercriteria Correlation (CRITIC) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Data on fifteen indicators, including economic stability, technological innovation capacity, and infrastructural capabilities, were collected and identified through an extensive literature review and consultations with stakeholders. These indicators were weighted using the CRITIC method to reflect their relative importance, and each country’s position relative to an ideal solution was assessed using the TOPSIS method. It was revealed that countries such as Singapore and Malaysia possess significant potential due to their advanced technological infrastructure and stable economic environments. Other countries, like Vietnam and Indonesia, were also found to exhibit promising capabilities, albeit with certain limitations. The study highlights the varying strengths and strategic areas that should be considered by policymakers and investors to enhance integration into the global semiconductor supply chain. Significant contributions to the strategic decision-making process have been made by providing a nuanced analysis of the region’s readiness and identifying specific areas for investment and policy intervention. By offering a methodological framework that can be adapted to other regions and sectors, not only has an existing research void been filled, but the practical understanding of effectively leveraging Southeast Asia’s potential in the semiconductor industry has also been enhanced.

Study on the Driver Visual Workload in High-Density Interchange-Merging Areas Based on a Field Driving Test.

A visual workload model was constructed to determine and evaluate drivers' visual workload characteristics in high-density interchange-merging areas. Five interchanges were selected, and a real-vehicle driving test was conducted with 47 participants. To address the differences in drivers' visual characteristics in the interchange cluster merging areas, the Criteria Importance Through Intercriteria Correlation (CRITIC) objective weighting method was employed. Six visual parameters were selected to establish a comprehensive evaluation model for the visual workload in high-density interchange-merging areas. The results show that the average scanning frequency and average pupil area change rate are most strongly correlated with the visual workload, whereas the average duration of a single gaze has the lowest weight in the visual workload assessment system. Different driver visual workloads were observed depending on the environment of the interchange-merging areas, and based on these, recommendations are proposed to decrease drivers' workload, thereby increasing road safety.

Optimizing underwater connectivity through multi-attribute decision-making for underwater IoT deployments using remote sensing technologies

The underwater Internet of Things (UIoT) and remote sensing are significant for biodiversity preservation, environmental protection, national security, disaster assistance, and technological innovation. Assigning tasks to autonomous underwater vehicles (AUVs) is a fundamental challenge in underwater technology and exploration. Remote sensing and AUVs are vital for pollution detection, disaster prevention, marine observation, and ocean monitoring. This work presents an optimized network connectivity using a multi-attribute decision-making approach for underwater IoT deployment. A feature engineering approach highlights the significant characteristics of underwater things, incorporating remote sensing data, and a multi-objective optimization method is used to select optimal UIoT for effective task allocation in deep-sea environments. A balance between data transmission, energy economy, and operational performance is necessary for efficient task distribution. Effective communication algorithms and protocols are needed to maintain environmental sustainability, protect marine ecosystems, and improve underwater monitoring enhanced by remote sensing technologies. Multi-criteria decision-making (MCDM) is beneficial for addressing various challenges in underwater technology, considering factors such as mission objectives, energy efficiency, environmental conditions, vehicle performance, safety, and much more. The proposed criteria importance through intercriteria correlation (CRITIC) methodology will assess technical competencies like communication, resilience, navigation, and safety in an underwater environment, leveraging remote sensing and aiding decision-makers in selecting appropriate undersea devices and vehicles for enhancing communication and transportation. This method prioritizes characteristics and aligns them with specific objectives, improving decision-making quality in the marine environment.

A novel framework for uncovering the coordinative spectrum-effect correlation of the effective components of Yangyin Tongnao Granules on cerebral ischemia-reperfusion injury in rats

Ethnopharmacological relevanceIschemic stroke is currently a major public health hazard.Yangyin Tongnao Granules (YYTN), a traditional Chinese medicinal prescription, exerts potential therapeutic effects on subsequent cerebral ischemia-reperfusion injury (CIRI) after ischemic stroke. However, further studies are required to comprehend the underlying mechanism of YYTN for treating CIRI and the associated spectrum-effect mechanisms. Aim of the studyTo investigate the coordinated correlation between the fingerprint and the pharmacodynamic indexes of the effective components (total flavonoids, total saponins, total alkaloids, and total phenolic acids) in YYTN for treating CIRI in rats. MethodsThe fingerprints of five specific components (ligustrazine, puerarin, ferulic acid, calycosin, and formononetin) of YYTN in rats with middle cerebral artery occlusion (MCAO) were established using high-performance liquid chromatography (HPLC), and their peak areas were quantified in plasma samples. The pharmacodynamic indexes of tumor necrosis factor-alpha (TNF-α), cytochrome c (Cyt-C), and total superoxide dismutase (T-SOD) were integrated using the Criteria Importance Through Intercriteria Correlation (CRITIC) method to create a comprehensive evaluation index. Spectrum-effect correlation was analyzed by performing gray relation analysis (GRA), correlation analysis (CA), and partial least squares regression (PLSR). The Borda method was then applied to integrate the obtained results. ResultsIn MCAO rats, the effective components of YYTN reduced TNF-α and Cyt-C and increased T-SOD, which indicates their anti-inflammatory, antiapoptotic, and antioxidant effects. Spectrum-effect CA revealed certain associations between the chromatographic peaks of the five main components and the comprehensive pharmacodynamic evaluation index. Of these components, formononetin displayed the highest correlation, whereas ferulic acid exhibited the lowest correlation. All components showed a positive correlation. Using the Borda method, the components were ranked as follows based on correlation: formononetin > calycosin > ligustrazine > puerarin > ferulic acid. ConclusionsThe effective components of YYTN exhibited synergistic effects in the treatment of MCAO rats, which could potentially be attributed to their multitarget and multipathway mechanisms. The Borda method-based spectrum-effect correlation analysis provides a coordinated approach to investigate the relationship between fingerprint and pharmacodynamics of traditional Chinese medicine (TCM).

Multi-criteria decision-based hybrid energy selection system using CRITIC weighted CODAS approach

A complex and multicriteria decision making (MCDM) problem arises in a bid to select the most appropriate hybrid energy system among several combinations in distributed electricity generation as it involves conflicting criteria that must be simultaneously considered. In this work, Criteria Importance Through Intercriteria Correlation (CRITIC) along with Combinative Distance-Based Assessment (CODAS) was employed to select a suitable hybrid energy system combination for water pumping from four conflicting alternatives of Biomass-Battery (S1), PV-Battery (S2), PV-Biomass (S3), and PV-Biomass-Battery (S4) by objective weight estimation. This was followed by a confirmation by the Additive Ratio Assessment (ARAS) method for the solution. The results presented show that CRITIC method reveals 0.275, 0.224, 0.248, and 0.252 as different weights of the four alternatives of S1, S2, S3, and S4, respectively. The ranking results reveal S4 as the best alternative based on an assessment score of 0.4693. The same hybrid system was confirmed by ARAS based on overall performance and degree of utility of 0.287697 and 0.823716, respectively.

Adapting to climate change in arid agricultural systems: An optimization model for water-energy-food nexus sustainability

Sustainable management of water, energy, and food (WEF) under climate change will be a significant challenge for arid agricultural systems. This study developed a fractional non-linear multi-objective programming (FNLMOP) model to optimize resource allocation and improve agricultural sustainability in these systems under climate change. The model was designed in the framework of the WEF nexus to simultaneously improved energy productivity (profit/energy), and water productivity (profit/water), while mitigating environmental damage (damage to groundwater resources/output) and ensuring food security in an arid watershed in Iran. The long Ashton research station weather generator (LARS-WG) and the coupled model intercomparison project 6 (CMIP6) were employed to project climate parameters for both future dry and wet conditions. The sustainability of the optimal solutions was then assessed using a hybrid criteria importance through intercriteria correlation (CRITIC)-VIKOR approach. The optimal solutions revealed a reduction in the land under cultivation and produced less water-intensive crops. The optimization model can ensure WEF security, enhancing agricultural system sustainability by optimizing crop cultivation patterns and resource allocation. Current crop choices were highly inefficient with the bigger changes being from the current crops to optimal crops. Climate change showed a substantial but lesser influence on optimal crop choice.

Underground concrete pipe crack damage monitoring by the fuzzy analysis of microseism

Abstract Underground concrete pipe is critical for the safety of the urban infrastructure. Crack would lead to the accidents such as fluid spillage, ground subsidence, as well as waterlogging. Thus, it is of great significance to measure and monitor the crack of pipeline. In this research, a fuzzy monitoring method of the concrete pipe crack damage based on the microseism signal GMM (Gaussian Mixed Model) analysis was proposed. First, the multi parameters of microseism signal were extracted. Then, CRITIC (Criteria Importance Through Intercriteria Correlation) weight vector was constructed using the importance information of parameters. Further, the GMM membership matrix was created by the similarity of the probability density distribution. The crack condition was assessed by means of the fuzzy calculation between the weight vector and membership matrix. The experiment indicated that the proposed method could monitor and assess the different crack conditions in real time.

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.

Analysis of the Influence of Online Public Opinion on Corporate Brand Value: An Efficient Way to Avoid Unexpected Shocks from the Internet

Nowadays, online public opinions (OPOs) significantly impact corporate brand value (CBV). To prevent corporate brand crises caused mainly by OPOs, it is essential to detect anomalies in OPOs related to corporate reputation in a timely manner. This study explores how dramatic changes in OPOs affect market capital value (MCV), the primary indicator of CBV, and aims to construct a CBV early warning evaluation model. First, a set of OPO indicators dedicated to CBV are selected based on correlation analysis between various popular OPO and CBV indicators collected through a literature review. The method of Criteria Importance Through Intercriteria Correlation (CRITIC) is then employed to determine the indicator weights using data collected from popular social media platforms. Finally, the vector auto-regression (VAR) model is applied to validate the effectiveness of the proposed evaluation model. A case study involving several Chinese enterprises shows that abnormal changes in their MCVs consistently follow abnormal fluctuations observed in their OPOs, with a significant delay. This finding enables managers to promptly detect potential crises from the internet and take actions to avoid unexpected shocks.

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.