Conventional Analytic Hierarchy Process Research Articles

A Novel Procedure for the AHP Method for the Site Selection of Solar PV Farms

This study proposes a novel approach to enhance the analytic hierarchy process (AHP) for the selection of suitable sites for solar photovoltaic (PV) farms. This approach is particularly beneficial when it is possible to establish a predefined objective relation in the final weights of the AHP method. The methodology focuses on achieving this predefined relation introducing a systematic revision of the constants of related constraints. In this study, the costs of constructing a unit transmission line and road in the Kayseri Province are objectively related, and the initial constant matrix of the AHP method is iteratively revised until the relation of the final weights converges to the predefined one. The suitability of solar PV farm locations is classified into five classes, revealing approximately 28% (40-100% of suitability) of the province as favorably suitable and designating about 67% as restricted zones. The findings reveal notable distinctions between the revised weights and those derived from the conventional AHP method. The disparity in weights for various constraints varies from 13.5% to 7.2%. Consequently, the alterations in the area of suitability regions range from 3.4% to 50%. The revision of AHP weights results in a reduction in higher-suitability areas, coupled with a significant expansion in the region exhibiting lower suitability. Notably, the extent of change in the suitability map increases when the difference in ratios between two criteria obtained from the AHP and the predefined objective relation is high. The proposed method demonstrates its applicability in regions like Kayseri where an objective relation between criteria can be established. Given the inherent subjectivity of the AHP method, the proposed procedure becomes essential to attain more objective weights. Since the methodology objectively adjusts weights based on known ratios, it increases the accuracy and reliability of site selection studies.

Integrated analytic hierarchy process and fuzzy analytic hierarchy process for Sahel watershed flood susceptibility assessment, Algeria

Abstract Effective disaster management heavily relies on accurate flood susceptibility mapping. The fuzzy analytic hierarchy process (FAHP) is adept at considering the imprecise nature of decision-making criteria. This study assesses FAHP's effectiveness in flood susceptibility mapping, comparing it with the conventional analytic hierarchy process (AHP). By using Geographic Information System-analyzed remotely sensed data, the research systematically evaluates flood risk southeast of Algiers. Various datasets, including Digital Elevation Model, slope, precipitation, and land use maps, were collected via remote sensing. A linear fuzzy membership function transformed the data into fuzzy values. AHP determined the importance of each dataset, with calculated weights multiplied by corresponding fuzzy values. Fuzzy analysis combined these characteristics into a five-category flood risk map, verified with Google Earth and satellite images. Results indicate a high potential for flood hazard mapping, categorizing 30% of frequently flooded regions as high risk. Maps reveal north basin areas are more flood prone due to excessive precipitation, and urban areas in floodplains are vulnerable. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) assessments demonstrate AHP and FAHP's effectiveness. AUC values of 88.40 and 92% indicate that both models accurately predict flood-prone areas. FAHP excels, reducing subjectivity and ambiguity in human judgments.

Using Markov Random Field and Analytic Hierarchy Process to Account for Interdependent Criteria

The Analytic Hierarchy Process (AHP) has been a widely used multi-criteria decision-making (MCDM) method since the 1980s because of its simplicity and rationality. However, the conventional AHP assumes criteria independence, which is not always accurate in realistic scenarios where interdependencies between criteria exist. Several methods have been proposed to relax the postulation of the independent criteria in the AHP, e.g., the Analytic Network Process (ANP). However, these methods usually need a number of pairwise comparison matrices (PCMs) and make it hard to apply to a complicated and large-scale problem. This paper presents a groundbreaking approach to address this issue by incorporating discrete Markov Random Fields (MRFs) into the AHP framework. Our method enhances decision making by effectively and sensibly capturing interdependencies among criteria, reflecting actual weights. Moreover, we showcase a numerical example to illustrate the proposed method and compare the results with the conventional AHP and Fuzzy Cognitive Map (FCM). The findings highlight our method’s ability to influence global priority values and the ranking of alternatives when considering interdependencies between criteria. These results suggest that the introduced method provides a flexible and adaptable framework for modeling interdependencies between criteria, ultimately leading to more accurate and reliable decision-making outcomes.

Risk analysis of autonomous vehicle test scenarios using a novel analytic hierarchy process method

AbstractScenario‐based test methods are employed to assess the safety and performance of autonomous vehicles. The analytic hierarchy process (AHP) method is a common assessment method for determining the criticality of test scenarios. However, the AHP method is subjective and less reproducible when performed by different persons, as the elements of pairwise comparison values that are directly linked to the outcome must be assigned by the person involved. This paper proposes a novel AHP method that automatically generates pairwise comparison values by optimizing the correlation between performance metrics and risk of test scenarios by simulation. Performance metrics are defined as the minimum relative distances and corresponding relative velocities between vehicles, and the risk of the test scenario is determined by the pairwise comparison values of AHP. The novel AHP method was evaluated using a cut‐in scenario. The results showed that the minimum relative distance and the risk determined by the novel AHP method achieved a better correlation coefficient of −0.96, which is better than the conventional AHP of −0.828 and Fuzzy AHP of −0.824. These results suggest that the criticality of the test scenarios determined by the novel AHP method can more accurately reflect real‐world driving environments.

Analytic hierarchy process (AHP) assessment of Kuwait mega construction projects' complexity

PurposeMegaproject's construction is essential for the development and economic growth of any country, especially in the developing world. In Kuwait, megaprojects are facing many restrictions that discourage their execution causing a significant delay in bidding, design, construction and operation phases with the execution quality being affected. The objective of this study is to develop a complexity measurement model using analytic hierarchy process (AHP) for megaprojects in Kuwait, with a focus on the New Kuwait University multi-billion campus Shadadiyah (College of Social Science, Sharia and Law (CSSL)) as a case study.Design/methodology/approachThe study applies a hybrid fuzzy analytic hierarchy process (FAHP) method to compare the results with those obtained using the conventional AHP method. This can facilitate the project management activities during the different stages of construction. Data were collected based on the results of a two-round Delphi questionnaire completed by seniors and experts of the selected project.FindingsIt was found that project modeling methodology was responsible for complexity. It was grouped under several categories that include technological, goal, organizational, environmental and cultural complexities. The study compares complexity degrees assessed by AHP and FAHP methods. “Technological Complexity” scores highest in both methods, with FAHP reaching 7.46. “Goal Complexity” follows closely behind, with FAHP. “Cultural Complexity” ranks third, differing between methods, while “Organizational” and “Environmental Complexity” consistently score lower, with FAHP values slightly higher. These results show varying complexity levels across dimensions. Assessing and understanding such complexities were essential toward the completion of such megaprojects.Originality/valueThe contribution of this study is on providing the empirical evidential knowledge for the priority over construction complexities in a developing country (Kuwait) in the Middle East.

Integrating Dynamic Bayesian Networks and Analytic Hierarchy Process for Time-Dependent Multi-Criteria Decision-Making

The analytic hierarchy process (AHP) has been a widely used method for handling multi-criteria decision-making (MCDM) problems since the 1980s. However, it postulates that criteria are independent and static, which may not always hold true in realistic situations. Although several methods have been proposed to relax the assumption of independence between criteria in the AHP, such as the analytic network process (ANP), these methods do not account for time-dependent criteria in the AHP. Consequently, this paper presents an innovative method that integrates dynamic Bayesian networks (DBNs) with the AHP to model dynamic interdependencies between criteria in MCDM problems. We illustrate the proposed method through a comprehensive numerical example and compare the result with the conventional AHP. The findings suggest that the proposed method extends the AHP to accommodate time-dependent issues and, when ignoring specific information, reduces to the conventional AHP, thereby demonstrating that our approach serves as a more general AHP model.

An Improved Unascertained Measure-Set Pair Analysis Model Based on Fuzzy AHP and Entropy for Landslide Susceptibility Zonation Mapping

Landslides are one of the most destructive and common geological disasters in the Tonglvshan mining area, which seriously threatens the safety of surrounding residents and the Tonglvshan ancient copper mine site. Therefore, to effectively reduce the landslide risk and protect the safety of the Tonglvshan ancient copper mine site, it is necessary to carry out a systematic assessment of the landslide susceptibility in the study area. Combining the unascertained measure (UM) theory, the dynamic comprehensive weighting (DCW) method based on the fuzzy analytic hierarchy process (AHP)-entropy weight method and the set pair analysis (SPA) theory, an improved UM-SPA coupling model for landslide susceptibility assessment is proposed in this study. First, a hierarchical evaluation index system including 10 landslide conditioning factors is constructed. Then, the dynamic comprehensive weighting method based on the fuzzy AHP-entropy weight method is used to assign independent comprehensive weights to each evaluation unit. Finally, we optimize the credible degree recognition criteria of UM theory by introducing SPA theory to quantitatively determine the landslide susceptibility level. The results show that the improved UM-SPA model can produce landslide susceptibility zoning maps with high reliability. The whole study area is divided into five susceptibility levels. 5.8% and 10.16% of the Tonglvshan mining area are divided into extremely high susceptibility areas and high susceptibility areas, respectively. The low and extremely low susceptibility areas account for 30.87% and 34.14% of the total area of the study area, respectively. Comparison with the AHP and Entropy-FAHP models indicates that the improved UM-SPA model (AUC = 0.777) shows a better performance than the Entropy-FAHP models (AUC = 0.764) and the conventional AHP (AUC = 0.698). Therefore, these results can provide reference for emergency planning, disaster reduction and prevention decision-making in the Tonglvshan mining area.

A New Vertical Handover Prediction Method for Heterogeneous Wireless Networks

Long Term Evolution (LTE) is the fourth generation (4G) cellular network technology that provides improved performance that related to data rate, coverage and capacity compared to earlier cellular systems. The addition of many small cells in a heterogeneous network configuration provides a means to enhance capacity through extreme frequency reuse. The heterogeneous environment of different network technologies can provide high data rate and enhance multimedia services, but it is challenging to provide optimized handover (HO). In this paper, a new method is proposed to select the optimal network from available networks to which a UE may be handed over to achieve better QoS performance. The aggregation of multiple criteria for calculation of overall ranking of networks is obtained by Analytical Hierarchy Process (AHP) [1] and is combined with the history of previously visited cells and regression analysis of the Layer 1 (L1) and Layer 3 (L3) filtered Received Signal Strength (RSS) data for prediction of future values. The AHP is used to calculate the weights for the system attributes and to rank the available networks based on multiple criteria Multiple Attribute Decision Making (MADM). The sequence of visited cells is used in target network selection as it reduces frequent handover. The sequence of visited cells would be modeled as a Markov Chain. To assess RSS, beside L1, L3 filtering RSS prediction and smoothing is used to predict future signal samples to hasten the process of HO. RSS forecasting is used to predict handover necessity so as to reduce the handover delay. The results show that the number of handovers is reduced by up to 50% compared to the conventional AHP. The results also show that the threshold crossing rate and average duration of fades are reduced by 47% compared with the AHP. Handover delay is also reduced by up to 40 ms due to RSS forecasting.

Analytic Network Process-Based Sustainability Life Cycle Assessment of Concrete Bridges in Coastal Regions

Since establishing the Sustainable Development Goals in 2015, the assessment of the sustainability performance of existing and future infrastructures has been in the spotlight of the scientific community. This is because the construction sector is essential for promoting the social welfare and economic development of countries, but is also one of the main environmental stressors existing to date. However, assessing infrastructure sustainability throughout its life cycle remains a significant challenge, as the criteria involved in sustainable design are often complex and conflicting. The Analytic Network Process (ANP) is recognized as a powerful decision-making tool to model such problems. Here, the life cycle sustainability performance of different design alternatives for a concrete bridge near the shore is evaluated using ANP. The obtained results are compared with those obtained using the conventional Analytical Hierarchy Process (AHP). The results obtained using ANP are more reliable than those derived from the conventional AHP in terms of the expert’s consistency and the number of comparisons made.

Selection of an Optimal Maintenance Strategy Under Uncertain Conditions: An Interval Type-2 Fuzzy AHP-TOPSIS Method

The selection of an optimal maintenance strategy is one of the principal strategic decisions that must be taken in many contexts in order to maintain an asset with minimum deterioration and to deliver maximum output with high quality. When considering maintenance cost, reliability, and safety level of industrial assets, decision makers must select an appropriate maintenance strategy, preferably, with a known degree of uncertainty. This article utilizes a new interval type-2 fuzzy (IT2F) multicriteria decision-making method based on the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), which can effectively handle the degree of uncertainty in group decision making and assist in the selection of the most feasible and optimal industrial asset maintenance strategy. The proposed method is compared with the results obtained from the conventional AHP-TOPSIS and type-1 fuzzy AHP-TOPSIS—the rank obtained from IT2F-AHP-TOPSIS is identical to those methods and can be used as an effective alternative to the type-1 fuzzy method. Compared with conventional AHP-TOPSIS and type-1 fuzzy AHP, IT2F-AHP-TOPSIS has the added advantage that it allows decision makers to define the membership function with greater flexibility and is able to handle uncertainty during decision making.

Application of Monte Carlo and Fuzzy Analytic Hierarchy Processes for ranking floating wind farm locations

A Monte Carlo simulation procedure is developed to select the optimum location of wind farms by using major decision criteria and applying subjective judgments from decision-makers. The approach combines Monte Carlo simulation with conventional Analytic Hierarchy Process and with the Fuzzy Analytic Hierarchy Process. Monte Carlo simulation is used to determine the confidence level of each alternative determined by the Analytic Hierarchy Process concerning the uncertainty of decision-maker opinions. The proposed method is applied to offshore wind farms located in Spain, and the most appropriate locations for turbine positioning are ranked.

E-AHP: An Enhanced Analytical Hierarchy Process Algorithm for Priotrizing Large Software Requirements Numbers

One of the main activities of software requirements analysis is requirements prioritization. The wrong requirements prioritization is risky as it leads to many software failures. The current requirements prioritization techniques can’t deal with large requirement numbers efficiently, which is considered one of their main issues. Many researchers have agreed that the analytical hierarchy process (AHP) is one of the best prioritization techniques as it produces highly accurate results. AHP has two main problems: scalability and inconsistency. These problems have motivated us to propose an improved version of AHP for software requirements prioritization, namely Enhanced AHP (E-AHP). A performance evaluation has been done for the conventional AHP, E-AHP, and one of the recent algorithms that also try to solve the AHP scalability problems, namely removing eigenvalues and introducing the dynamic consistency checking algorithm into AHP (ReDCCahp) algorithms The evaluation shows which algorithm takes the least time, uses the least memory, produces the most consistent and accurate results, and has the highest scalability. The three algorithms have been evaluated by running their codes using different numbers of requirements ranging from 10 to 500. The results show that E-AHP is more scalable, takes the least time, uses the least memory, and produces the most consistent and accurate results compared to the other two algorithms. That becomes remarkable when the number of requirements increases. Therefore, E-AHP is suitable to be applied in large software projects, as it can deal efficiently with the large software requirements numbers.

Comparison of Landfill Site Suitability Assessment Using Ahp and Fuzzy Ahp Methods

Landfill site selection is a multi-criteria decision problem and has strategic value for various countries. The conventional method for landfill site selection is not enough to address the vague nature of linguistic assessment. An integrated Fuzzy AHP method was used to resolve this issue. Suitability maps produced by conventional Analytical Hierarchy Process (AHP) and integrated Fuzzy AHP methods were compared and validated, which adds a new dimension in the waste management field. Various environmental, socio-economic, and infrastructure factors were used as predictors, which included groundwater level, soil type, slope, land cover, distance to settlements, railways, roads, surface water sources, airport, power lines, wells, and protected areas. Relative Operation Characteristic (ROC) analyses gave results of 0.77 and 0.86 for the suitability maps of AHP and Fuzzy AHP, respectively. The landfill site suitability maps produced from this study could help urban planners, engineers, decision-makers, and land-use developers to make suitable decisions. Furthermore, the resulted integrated model showed the best suitability that could be implemented to resolve the fundamental landfill siting problems in other cities of Pakistan.

Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP-BWM Model.

Frequent lane changes cause serious traffic safety concerns for road users. The detection and categorization of significant factors affecting frequent lane changing could help to reduce frequent lane-changing risk. The main objective of this research study is to assess and prioritize the significant factors and sub-factors affecting frequent lane changing designed in a three-level hierarchical structure. As a multi-criteria decision-making methodology (MCDM), this study utilizes the analytic hierarchy process (AHP) combined with the best–worst method (BWM) to compare and quantify the specified factors. To illustrate the applicability of the proposed model, a real-life decision-making problem is considered, prioritizing the most significant factors affecting lane changing based on the driver’s responses on a designated questionnaire survey. The proposed model observed fewer pairwise comparisons (PCs) with more consistent and reliable results than the conventional AHP. For level 1 of the three-level hierarchical structure, the AHP–BWM model results show “traffic characteristics” (0.5148) as the most significant factor affecting frequent lane changing, followed by “human” (0.2134), as second-ranked factor. For level 2, “traffic volume” (0.1771) was observed as the most significant factor, followed by “speed” (0.1521). For level 3, the model results show “average speed” (0.0783) as first-rank factor, followed by the factor “rural” (0.0764), as compared to other specified factors. The proposed integrated approach could help decision-makers to focus on highlighted significant factors affecting frequent lane-changing to improve road safety.

Analytic Hierarchy Process with the Correlation Effect via WordNet

The analytic hierarchy process (AHP) is a well-known approach in decision-making because of its simplicity and rationality. However, in conventional AHP, it cannot account for the correlation effect between criteria. In this paper, we use the lexical database, WordNet, to calculate the similarity between criteria set by a decision-maker. Then, we use the similarity matrix to process the factor analysis and obtain the independent factors, which are composed of their criteria. Finally, the weights of factors are derived to evaluate the alternatives. Moreover, we use a case study of online shopping to illustrate the proposed method and compare the result with the conventional AHP.

An Integrated Decision Support Model for Evaluating Public Transport Quality

Big cities suffer from serious complex problems such as air pollution, congestion, and traffic accidents. Developing public transport quality in such cities is considered an efficient remedy to obviate these critical issues. This paper aims to determine the significant supply quality criteria of public transportation. As a methodology, a hybrid Analytic Hierarchy Process (AHP) combined with the Best Worst Method (BWM) is applied. The proposed model is basically a hierarchy structure with at least a 5 × 5 pairwise comparison matrix or larger. A real-world complex problem was examined to validate the created model (public transport quality improvement). An urban bus transport system in the Jordanian capital city, Amman, was used as a case study; three stakeholder groups (passengers, nonpassengers, and representatives of the local government) participated in the evaluation process. The conventional Analytic Hierarchy Process (AHP) leads to weak consistency in the case of existing 5 × 5 pairwise comparison matrices or larger, particularly in estimating complex problems. To avoid this critical issue in AHP, we used Best Worst Method (BWM) comparisons, which make the evaluation process easier for decision makers; moreover, it saves survey time and provides more consistency when compared to AHP pairwise comparisons. The model adopted highlighted the most significant service quality criteria that influence urban bus transport systems. Furthermore, the sensitivity analysis conducted detected the stability of the criteria ranking in the three levels of the hierarchical structure. Since the proposed AHP–BWM model (which is the sole example of this sort of combination) is independent from the decision attributes, it can be applied to arbitrary hierarchically structured decision problems with a relatively large number of pairwise comparisons.

An approach to evaluate CAM software alternatives

ABSTRACTThe selection process for the best computer-aided manufacturing (CAM) software among a set of potential number of alternatives in market has been critical issue for most companies that aim to make their design/manufacturing-related activities automated. Because this selection process is very vital for companies because a wrong decision might put them into a difficult position in terms of economical, market share and time spent. Therefore, today’s companies have used different multiple-criteria decision making (MCDM) methods for the evaluation study of CAM software alternatives to make this complex process easily applicable and not time-consuming. Among the MCDM methods in literature, the analytic hierarchy process (AHP) method has been widely used for various MCDM problems in both academic researches and industrial practices. However, in some cases, because of the vagueness and uncertainty on the judgments of decision maker(s), the crisp pair wise comparison in the conventional AHP seems to be insufficient and imprecise to capture the right judgments of decision maker(s). Therefore, a fuzzy logic method is integrated in the pair wise comparison of AHP to make up for this deficiency in the conventional AHP, called as fuzzy AHP. Moreover, the proposed approach is also realized on a case study.

Z-number-based selection of suitable underground coal gasification site considering information reliability

ABSTRACTEnjoying the twelfth largest coal reserve in the world, only 1% of Iran’s energy consumption basket is supplied by coal. In the recent decade, new technologies that produce low-cost and clean energy from coal have received much attention. One of these methods is underground coal gasification (UCG). The UCG has a potential for converting the world’s coal resources into energy, liquid fuels, and chemicals. For extraction of this valuable energy, firstly, the best site for UCG should be selected to minimize the implementation complications and to distribute this energy source properly in the country. UCG site selection is affected by many uncertainties including environmental, economic, and operational parameters. Therefore, it is important to use methods capable of taking into account the uncertainty of various parameters in the site selection. In this study, Z-numbers which are a new generation of fuzzy numbers are used to overcome this issue in suitable site selection for the UCG method. Technical considerations in UCG site selection consist of coal properties, surrounding rock properties, groundwater, and surface infrastructures of the region. In this paper, first, 15 criteria influencing UCG method and four available alternatives (four coal mines in Iran) are considered. Then, by using the Z-AHP method, which is a combination of Z-numbers and conventional analytical hierarchy process (AHP), the suitable mine site for UCG is determined. Based on the results, the alternative mine named Mazino is selected as the best place for UCG site with a final weight of 0.3713. The comparison of final weights of criteria also illustrates that the two criteria of “existence of fault” (C11) and “permeability of coal seam and overburden” (C9) are the most important parameters in UCG site selection. The Z-AHP method has the capability to make an appropriate decision in the existence of experts’ disagreement or lack of experts’ confidence.

A multibelief analytic hierarchy process and nonlinear programming approach for diversifying product designs: Smart backpack design as an example

Owing to the cost constraint, it is difficult to incorporate all critical features into a single product design. To deal with this issue, analytic hierarchy process is a well-known method that compares the relative priorities of critical features. This study aims to illustrate that the judgment of a designer can be used to generate multiple diversified product designs. To this end, this study proposed a multibelief analytic hierarchy process and nonlinear programming approach. In the proposed methodology, a decision maker’s judgment matrix is decomposed into several single-belief judgment matrices that are more consistent than the original judgment matrix and represent diversified points of view regarding the relative priorities of factors. To this end, a nonlinear programming model is established and optimized. The proposed methodology was applied to a smart backpack design problem. It was concluded that a designer’s judgment was often inconsistent, which was ignored in the conventional analytic hierarchy process method but could be employed to diversify product designs.

Fuzzy AHP-GRA approach to evaluating energy sources: a case of Turkey

Purpose Since the demand for energy has dramatically increased in the countries which have fast-growing population and economy, they have faced with a critical problem of how to evaluate a set of potential energy sources (i.e. nuclear, natural gas, bio, geothermal, hydro, wind and solar) and choose the ultimate energy source for their needs. On the other hand, this critical problem turns into a multiple-criteria decision-making (MCDM) in the presence of a set of energy source alternatives and evaluation criteria. In literature, there are many MCDM methods introduced to solve for different kinds of problems. The purpose of this paper is to present an integrated approach for evaluating energy sources using fuzzy AHP and GRA, with a case for Turkey. Design/methodology/approach In this paper, the analytic hierarchy process (AHP) and grey relational analysis (GRA) methods are used because of their advantages for similar problems. On the other hand, due to the fact that the conventional AHP by a nine-point scale and GRA method using a scale with crisp values can be unable to handle to capture the right judgments of a decision-maker(s), to reflect the vagueness and uncertainty on the judgments of a decision-maker, the fuzzy logic is integrated with the AHP and GRA. Findings The contributions of the paper to the literature are given in two dimensions as follows: it presents an integrated approach for complex decision processes with subjective data or vague information; the proposed approach, the fuzzy AHP-GRA method for energy source selection, is unique for the related problem in literature. The results of the proposed model from the case of Turkey will help practitioners and experts of how to apply it to the similar problems in the field of energy management. Research limitations/implications In short, in this paper, an integrated approach is proposed through the fuzzy AHP and the fuzzy GRA methods. As the fuzzy AHP is used to determine the weights of evaluation criteria, the fuzzy GRA is used to rank energy source alternatives. Practical implications In addition, a case study for Turkey is presented to show the applicability of the proposed approach for potential practitioners who are authority in the field of energy in public and private sectors. Social implications On the other hand, the proposed approach, the fuzzy AHP-GRA for energy source selection can also be an intelligent tool for public and private energy companies in Turkey, as well as others in the world. Originality/value On the other hand, in this paper, to the best of the authors’ knowledge, the study contributes to the literature that the first time, they use the fuzzy alpha-cut AHP and GRA in fuzzy environment for energy source evaluation problem.