Traditional Analytic Hierarchy Process Research Articles

An operational risk assessment method for petrochemical plants based on deep learning

The petrochemical industry is an important guarantee for the development of people’s lives, and the operational risk assessment method of personnel in the operation of petrochemical enterprises is the most important. Based on a deep learning algorithm, a new method based on the micro-Doppler effect and fuzzy analytic hierarchy process is proposed to evaluate the operational risk of personnel in petrochemical enterprises. The original monitoring image of petrochemical equipment is invoked, and micro-Doppler analysis is performed based on the original image to identify the activity target of the personnel on the job site and generate the activity map of the petrochemical plant operators. Based on the analysis data of the micro-Doppler effect, the fuzzy function and hierarchical analysis method are combined. The fuzzy theory is introduced into the analytic hierarchy process, and the inherent expert evaluation scores are transformed into fuzzy numbers. It makes the expert evaluation of petrochemical plants more accurate in the subsequent coupling and improves the objectivity of the traditional analytic hierarchy process. The scheme proposed in this paper can monitor real-time operation safety and provide a guarantee for the personal safety of field operators. This method plays an important role in improving the safety of petrochemical plants.

Emergency Capability Evaluation of Port-Adjacent Oil Storage and Transportation Bases: An Improved Analytic Hierarchy Process Approach

The large-scale storage and stable supply of oil products are essential for national energy security and economic development. As the economy expands and energy demands rise, centralized storage and supply systems become increasingly vital for ensuring the efficiency and reliability of oil product distribution. However, large oil storage depots present substantial safety risks. In the event of fires, explosions, or other accidents, emergency response efforts face stringent demands and challenges. To enhance the emergency response capabilities of oil storage and transportation bases (OSTBs), this paper proposes an innovative approach that integrates the improved analytic hierarchy process (IAHP) with the Entropy Weight Method (EMW) to determine the combined weights of various indices. This approach reduces the subjective bias associated with the traditional analytic hierarchy process (AHP). The emergency response capabilities of OSTBs are subsequently evaluated through fuzzy comprehensive analysis. An empirical study conducted on an OSTB in the Zhoushan archipelago quantitatively assesses its emergency preparedness. The results show that the base excels in pre-incident prevention, demonstrates robust preparedness and response capabilities, and exhibits moderate recovery abilities after incidents. These findings provide a theoretical foundation for reducing the likelihood of accidents, enhancing emergency response efficiency, and mitigating the severity of consequences. Practical recommendations are also offered based on the results.

Multi-criteria decision making beyond consistency: An alternative to AHP for real-world industrial problems

The Analytic Hierarchy Process (AHP) is a widely used method for multi-criteria decision-making that hinges on the assumption of consistency in pairwise comparisons. However, in real-world scenarios, decision-makers often struggle to provide fully consistent judgments. This article introduces a decision-making framework that operates independently of consistency. Utilizing the Skew-Symmetric Bilinear representation of preferences allows decision-makers to more accurately evaluate alternatives and criteria, making this framework more applicable in practical settings. The proposed method is validated through practical examples and an in-depth case study in the textile industry, effectively resolving a complex decision-making problem related to acquiring a data analytics tool for supplier selection. The results underscore the robustness and adaptability of this consistency-independent technique as a flexible alternative to traditional AHP methods.

Augmenting input-output system under the curse of dimensionality: AHP-RAS integration and algorithm simulation

ABSTRACT In the estimation of input-output tables, one frequently encountered challenge is the aggregation of sectors in initial tables, which complicates the estimation of more detailed tables due to missing data—a phenomenon referred to as the “curse of dimensionality” when augmenting from lower to higher dimensions. We propose an AHP-RAS method to address the substantial data deficiencies encountered in the expansion of input-output tables. The method applies the Analytical Hierarchy Process (AHP) to transform the subjective relative importance of sectors into input coefficients using Pairwise Comparison Matrices (PCM) and eigenvector methods. Subsequently, it incorporates the RAS approach to supplement objective data, thereby integrating subjective judgement and objective information to tackle the curse of dimensionality. Unlike traditional AHP methods, this study employs binary integer programming to decompose large-scale matrices into subsets, making the AHP technique applicable to input-output tables with a broader range of sectors. We employ Monte Carlo simulation techniques to evaluate the robustness of the proposed methodology, specifically assessing the impacts of subjective judgement, objective data utilization, and the variation in sector numbers. Overall, the method overcomes the curse of dimensionality, yielding coefficients within an acceptable error margin, thereby demonstrating a significant advancement in the field of input-output analysis.

Application of Monte Carlo Analytic Hierarchy Process (MAHP) in Underground Mining Access Selection

This paper presents the application of risk analysis associated with the multicriteria decision method, through the MAHP (Monte Carlo analytic hierarchy process) technique for mining. As an example of application, the problem of choosing an access and transportation route for an underground mine is addressed. A decision can be made based on a technical, economic, and social-environmental approach. As a topic of interest for the modeling of mining projects, this work presents how the technique can qualify the decision-making process, reducing its subjectivity. It is verified that in comparison with the traditional AHP (analytic hierarchy process) method, the risk analysis allows considering the variability of weights and preferences assigned to criteria and options. In the example case, the following options are evaluated: shaft, ramp by diesel or electric truck, and conveyor belt.

Fuzzy Analytic Hierarchal Process for Sustainable Public Transport System

The analytic The Analytic Hierarchy Process (AHP) is a well-established methodology for tackling complex multi-criteria decision problems in practical contexts. However, like many decision-making approaches, AHP confronts certain limitations, particularly in scenarios where evaluations are fraught with uncertainty and imprecision. This study sets out to enhance the capabilities of the AHP method and provide a comprehensive evaluation of public bus transport service quality within Budapest, Hungary. To address the inherent uncertainties in real-world decision-making, the study leverages the Fuzzy Analytic Hierarchy Process (FAHP), a fusion of Fuzzy Set Theory with the traditional AHP. This novel approach equips decision-makers with a more robust framework to handle the multifaceted nature of real-world decision problems. The study is grounded in empirical data obtained through dynamic surveys, ensuring its relevance to the actual conditions experienced in Budapest. Expert evaluators, well-versed in the field, contribute their assessments to enrich the analysis. This novel FAHP approach doesn't just promise improved decision-making outcomes; it also champions simplicity and comprehensibility. Its computational efficiency streamlines the decision-making process, providing a powerful tool for evaluating public bus transport service quality, thereby offering a significant contribution to the sustainable development of Budapest's transportation system.

Evaluating the technical efficiency of Saudi Arabian telecommunications companies using window DEA and AHP techniques

PurposeThis study aims to evaluate the efficiency of the three telecommunications companies in Saudi Arabia: Saudi Telecom Company (STC), Mobily and Zain over the period of 2010–2019. This evaluation is a step toward improving the performance of the Saudi telecommunications sector.Design/methodology/approachThree multicriteria decision-making (MCDM) techniques were used to calculate technical efficiency. These techniques include the traditional data envelopment analysis (DEA), window DEA and analytical hierarchy process (AHP). The three inputs used were total assets, operating expenses and capital expenditures, whereas the two outputs were sales revenue and total stockholders’ equity.FindingsSTC was ranked first using the three techniques, followed by Zain, and then Mobily. According to the DEA window analysis, these three companies were all efficient only in 2012. The efficiency was high in the initial years, 2010–2013, when it was above 0.90, and it dropped below 0.90 in the subsequent years, 2014–2019. In addition, the efficiency of STC remained high, with an average of 0.990. However, the average efficiencies of Zain and Mobily during this period were 0.807 and 0.804, respectively.Originality/valueThis is the first study to use the three MCDM techniques to evaluate the performance of telecommunications providers. The results show that window DEA is better than the other two techniques at evaluating performance over time, as it has a higher discrimination power than either the traditional DEA or AHP.

Evaluation on technical status of concrete cable-stayed bridge based on improved FAHP

Because of the large number of components and complex evaluation indexes of long-span bridges, it is necessary to adopt the evaluation method suitable for complex structures. Since the hypothetical membership degree of fuzzy theory and the layer weight of traditional analytic hierarchy process (AHP) may have subjective bias, the evaluation precision may be influenced if it is applied independently in bridge evaluation and thus the fuzzy analytic hierarchy process (FAHP) is more applicable to large-span concrete cable-stayed bridge. The traditional analytic hierarchy process using triangular fuzzy number (FAHP) has the shortcomings of computational errors and limited range, so the improved FAHP is adopted. The improved FAHP method introduces the reliability into the judgment matrix, which can not only solve the existing problems, but also reflect the influence of the fuzziness of the man-made subjective judgment on the result. Based on the detection results for a concrete cable-stayed bridge in Jilin Province, the corresponding hierarchy of index system is established, the trapezoidal function is used to calculate membership degree, and the weight of each index relative to target layer is calculated. Improved FAHP is finally applied to assess the security levels of various parts of this bridge and make comprehensive evaluation on the whole bridge.

Research on Operational Safety Risk Assessment Method for Long and Large Highway Tunnels Based on FAHP and SPA

Compared to the construction phase, the assessment of tunnel safety risks during the operational stage has not been thoroughly conducted, and the related research work is relatively lagging. To deepen the research and application of theoretical methods for assessing operational tunnel safety risks, and to improve the level of hazard investigation and prevention in long and large highway tunnels, this paper establishes a tunnel section assessment index system from four perspectives: tunnel condition, traffic characteristics, operational environment, and operational management. A combined qualitative and quantitative approach is employed, and a practical classification standard for the indicators is proposed. Based on the traditional Analytic Hierarchy Process (AHP) and fuzzy analysis theory, a new form of questionnaire survey, known as the expert system analysis method, is introduced to collect expert opinions. A novel approach for determining fuzzy numbers based on expert opinions is proposed. Consequently, a combined model for assessing operational tunnel safety risks in long and large highways is established by integrating the Fuzzy Analytic Hierarchy Process (FAHP) and the Set Pair Analysis (SPA). This model effectively combines subjective weighting with objective evaluation, thereby enhancing the accuracy of operational tunnel safety risk assessment for long and large highways. The safety risks of three long and large tunnels within the G1523 Yongguan Expressway were assessed using the evaluation method proposed in this paper. A comparative analysis was conducted between the results obtained using the proposed Fuzzy Analytic Hierarchy Process (FAHP) and Set Pair Analysis (SPA) method and the traditional Analytic Hierarchy Process (AHP) evaluation. The results indicate that the operational tunnel safety risk assessment method based on FAHP and SPA exhibits greater rationality and accuracy compared to the traditional AHP approach.

A novel resilient and sustainable supplier selection model based on D-AHP and DEMATEL methods

The natural disasters that cause a sudden violent change in the earth and the disruptions made by humans disrupt operations in the supply chain, causing negative influences such as lost productivity, revenue, competitive advantage, and profitability for the company. Therefore, suppliers need to be capable of being resilient to any disruption and providing sustainable supply. Supplier selection is a multi criteria decision making (MCDM) problem that allows suppliers to evaluate different and often conflicting qualitative and quantitative criteria. Both resilient and sustainable supplier selection problems contain many qualitative criteria. Experts make subjective judgments based on qualitative criteria in the decision-making process. The D number is a method used to find an efficient and feasible solution to uncertain information. In this paper, the DAHP method, which integrates the traditional Analytic Hierarchy Process (AHP) method and the D numbers, is used for the resilient and sustainable supplier selection problem. The Decision Making Trial and Evaluation Laboratory (DEMATEL) method is integrated into DAHP to determine criteria and subcriteria weights. The proposed integrated DAHP-DEMATEL model is implemented in an automobile manufacturing company to demonstrate the effectiveness of the method. The Triantaphyllou method is applied to perform sensitivity analysis. The traditional AHP-DEMATEL hybrid method was applied to the same problem, and sensitivity analysis was performed. The sensitivity analysis results of the hybrid method were compared with the results of the applied AHP-DEMATEL method. It can be said that the proposed model is less affected by subjective evaluations and is a robust method.

Study on a comprehensive indicator and online classification of early warning of low frequency oscillation in power system

With the development of smart grid technology in China, the study of low frequency oscillation early warning has become an important element of power system stability research. Aiming at the problems of single warning indicator with poor accuracy and lack of online warning system in the early warning strategy of low frequency oscillation in power system, a comprehensive low frequency oscillation early warning algorithm based on the evaluation method of kernel matrix hierarchical analysis of fuzzy comprehensive is proposed. Firstly, this method screens out the original data collected by Phasor Measurement Unit (PMU), and extracts the key data volume to form multiple early warning indicators, and generates the trapezoidal fuzzy number judgment matrix. Secondly, by calculating the kernel matrix and consistency test, it is determined whether or not the generated fuzzy matrix is reasonable and calculate the relative weights of the evaluation indicators. The affiliation function is constructed for each early warning indicator. A comprehensive indicator is calculated for multiple indicators, and the reasons of low frequency oscillation in power system are classified by the Random Forest (RF) algorithm. Then, the low-frequency oscillation system is constructed, and the algorithm convergence is proved by the Lyapunov indirect method. The algorithm can automatically determine whether low frequency oscillation occurs or not and classify different types of low frequency oscillations online. Finally, the correctness and validity of this paper’s method is verified by a New England 10-machine 39-node system. Compared with the traditional Analytic Hierarchy Process (AHP) method, the proposed method in this paper greatly improves the accuracy of early warning and meets the need of online warning.

Objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation: A case study based on integrated RF and DPSIR models

The evaluation of land comprehensive carrying capacity (LCCC) is a popular topic in regional sustainable development and land science research. However, these evaluations are often not objective due to the complexity and nonlinear characteristics of the LCCC evaluation indicators. This study provides a new framework for using machine learning methods to objectively evaluate LCCC. The Driver-Pressure-State-Impact-Response (DPSIR) indicator conceptual framework was used for the spatial visualisation of indicators and was then combined with the random forest (RF) model to optimise the LCCC evaluation. The results showed the following: (1) The accuracy of the integrated RF and DPSIR model was better than that of traditional support vector machine (SVM) and principal component analysis (PCA) methods, indicating that RF model was more suitable for LCCC evaluations. (2) The contribution of the DPSIR indicators after RF optimisation was significantly different from that of the traditional analytic hierarchy process (AHP), the contribution of the influence subsystem (I) (48.7%) was enhanced, while the contribution of the drive subsystem (D) (6.8%) was weakened, demonstrating that after RF optimisation, the DPSIR was more conducive for handling complex nonlinear systems and objectively reflecting indicator contributions. (3) The objective attributes of the DPSIR indicators were optimised through the processing of spatial visualisation models, indicating that RF was suitable for processing evaluations with a multidimensional spatial heterogeneity index system. The LCCC evaluation model can be applied to other carrying capacity case studies and operational processes and provide a scientific method for handling the multidimensional nonlinear evaluation index system and objective evaluation of LCCC.

Consideration of reciprocal judgments through Decomposed Fuzzy Analytical Hierarchy Process: A case study in the pharmaceutical industry

The number of academic research considering improvements/applications in the multiple criteria decision-making (MCDM) field has been increasing in the literature day by day. Among these works, the most used MCDM method is undoubtedly the analytic hierarchy process (AHP). Decomposed Fuzzy Sets (DFS) have been recently proposed to the literature in order to measure the inconsistency in expert judgments by providing optimistic and pessimistic point of view. The main objective of this paper is to extend AHP method by using DFS. DFS makes pairwise comparisons in AHP more reliable by considering the individual answers given by the decision makers to the reciprocal questions under vagueness and impreciseness. The proposed method, Decomposed Fuzzy Analytic Hierarchy Process (DF-AHP), is applied to determine the importance degrees of evaluation criteria in the pharmaceutical industry in order to illustrate the applicability of the approach. Based on the results, the “quality” is determined to be the most significant criterion in this industry. The results obtained from DF-AHP are compared with the results obtained from both traditional AHP and Pythagorean fuzzy AHP. The comparative analysis has also shown the most significant criterion is the same with the proposed method but slightly differences in the rank of the sub-criteria. The research can help businesses better understand the critical risks in the pharmaceutical industry.

Optimize Ranking and Load Shedding in Microgrid Considering Improved Analytic Hierarchy Process Algorithm and Power Stability Index

This paper proposes a load shedding model for the island microgrid based on the ranking of loads and the power stability index (PSI). Loads are ranked based on the improved analytic hierarchy process (AHP) algorithm. Real‐time measurement systems have the function of collecting data for very important, important, and less‐important loads at each bus load. From this data, the improved AHP method is applied to rank the loads. The advantage of this method is that the subjectivity is eliminated and not depending on the expertise of the system operator when implementing the traditional AHP method. Besides, the minimum amount of load shedding power is calculated, taking into account both primary and secondary control methods. The objective is to minimize the impact on power consumers and ensure that the frequency returns to an acceptable range. In addition, when implementing load shedding, voltage quality, and stability are considered. The PSI serves as a crucial parameter for assessing the voltage stability of microgrid buses. This index is combined with load ranking weights to obtain combined weights for the load shedding plan. Consequently, the proposed load shedding plan prioritizes minimizing damage to customers, improving voltage quality and stability, and ensuring frequency is within permissible limits. The 16‐bus microgrid system is applied to compare with traditional methods and to prove the efficiency of the suggested technique.

3D Quality Evaluation of Rock Mass in Urban Underground Space Based on Improved Fuzzy Analytic Hierarchy Process

Urban underground space (UUS) is an important resource to address the shortage of land, save resources and reduce environmental pollution, but it is irreversible. Therefore, it is necessary to evaluate its suitability before development. Rock mass is not only the carrier of UUS utilization, but also a key evaluation index, as it directly affects the evaluation results. Therefore, it is necessary to evaluate the 3D quality of rock mass from the perspective of UUS development. In this paper, a 3D quality evaluation model of rock mass in UUS is established based on an improved fuzzy analytic hierarchy process (IFAHP). Firstly, the subjective weight is calculated by analytic hierarchy process (AHP), and the objective weight is calculated by super-standard multiple method (SSMM). Then a new weight combination method (grey correlation analysis) is adopted. This method organically combines the subjective and objective weights, and the results show that the combined weight is more reasonable. Then the combination weight and fuzzy comprehensive evaluation method are combined with MapGIS to establish a 3D evaluation model. The case analysis shows that the evaluation results of the national standard BQ method are conservative, the traditional fuzzy analytic hierarchy process (TFAHP) is smaller, and the IFAHP is more robust. The case analysis also shows that the 3D evaluation has the advantages of strong intuition, good visualization and high precision, which is better than the traditional 2D evaluation. Moreover, the 3D evaluation model can not only be applied to this case, but also to other projects, and can provide decision-making for the development of UUS.

Comprehensive evaluation method of correlation index between meteorological conditions and load based on random matrix theory

The power system load is obviously affected by external factors, and the meteorological factor is one of the most important factors that change the load power. In order to improve the power grid’s adaptability to changing weather and to enhance its observable and measurable ability to load, this paper incorporates the random matrix theory, improves the traditional analytic hierarchy process, and proposes the fusion analytic hierarchy process (FAHP) as a new evaluation system construction method. A comprehensive evaluation study of meteorological factors and grid correlation indicators is carried out based on meteorological data and power grid active load data. Firstly, the augmented matrix method is leveraged to construct the data source matrix to determine the meteorological factor indicators and correlation analysis indicators. Secondly, the random matrix spectrum analysis theoretical Ring law and Pearson coefficient are established to complete the correlation analysis. Finally, FAHP is used to construct a comprehensive evaluation system of meteorological factor indicators. The proposed method can avoid the subjective factors of the traditional analytic hierarchy process, improving the accuracy and objectivity of weight distribution. It can effectively describe the correlation between active load and meteorological characteristics of the local area, providing strong support for subsequent index feature extraction and prediction of changes in load data under similar climatic characteristics.

Analysis and Evaluation of Enterprise Performance Appraisal Index Based on Fuzzy AHP Model

The decisive and decision-making challenges of business investment are for the good development of enterprises. In order to have a better market advantage of chemical granules for enterprises, the analytic hierarchy process (AHP) is constructed to analyze the company's performance. Under the environment of state-owned enterprises and foreign enterprises competing for market resources together, building a performance evaluation system suitable for the development stage of the company can adjust the planning strategy and analyze its own disadvantages in time. When analyzing the effective combination of performance appraisal and enterprise development, as well as the effective communication between employees and enterprise managers, feedback information is obtained through the experimental results. Under this analysis condition, this paper describes the feasibility and soundness of the AHP model system for enterprises, and what favorable decision-making methods can be provided for later development. Based on the selection of performance evaluation indicators, AHP analysis can clearly get the weight of each indicator and the correlation between them, in order to improve and build a new system of evaluation indicators. This paper concludes the following: (1) based on the analysis of enterprise performance appraisal indicators, using the AHP model evaluation method to calculate the indicators, the optimized indicators for the performance appraisal of the comprehensive and efficient significantly improved. (2) Comparing the traditional AHP calculation with the optimized model calculation, the results show that under the optimized calculation, the index is more concise and the fluctuation is reduced, which reduces the burden on administrators. (3) In the membership degree of index evaluation grade, the membership degree with an excellent grade is 0.9, which shows that the index plays a very important role in evaluation. (4) According to the development indicators of enterprises in recent years, it is found that the growth rate of resources is growing slowly, the demand for the talent market is still in a stable state as always, and its financial resources have increased by 120% in 2016.

Research on Performance Management Evaluation of Public Sports Venues Based on GFAHP

The importance of public stadiums has become increasingly prominent, but the efficiency of its performance management operation mechanism is low, which affects the overall operation efficiency of the whole stadium. However, due to the diversity and incompleteness of its data types, the traditional analytic hierarchy process is difficult to construct its index system. This article introduces the grey fuzzy analytic hierarchy process. Based on the analysis of its performance management problems, this article constructs the index system and makes an application case analysis. The results show that the GFAHP method (group fuzzy analytic hierarchy process) can ensure the integrity of information data. In data classification, the highest accuracy of sample classification is 40.13%, and the relative error rate is basically less than 6%. The performance effect is much higher than other classification algorithms. At the same time, the total score of the evaluation index system constructed by GFAHP is more than 3, the overall operation is good, and the risk evaluation effect of each influencing index factor is also good. Using the grey analytic hierarchy process to evaluate the operation and performance management of public stadiums and gymnasiums will help managers do a good job in risk control and quality dynamic adjustment, so as to improve the operation efficiency and benefit of stadiums and gymnasiums.

Deriving and comparing priority vectors for revised‐leachate pollution index (r‐LPI) using three fuzzy analytic hierarchy process

AbstractThe revised leachate pollution index (r‐LPI) developed using the fuzzy Delphi analytical hierarchy process (FDAHP) is a technique to quantify the contamination potential of landfill leachate. The steps involved in the formulation of the r‐LPI were the selection of parameters using the fuzzy Delphi method (FDM), computing relative weights of the parameters using the fuzzy analytic hierarchy process (FAHP), development of sub‐index curves, and finally, aggregation of sub‐indices. The calculation of the weights of the parameters has been one of the most crucial steps in the formulation of any index. The analytical hierarchy process (AHP) has been one of the most widely used methods for weight calculation. However, it was observed that the traditional AHP is inadequate to deal with the imprecise or ambiguous nature of linguistic evaluation. It also involves human subjectivity, which induces ambiguity and entails the use of decision‐making with uncertainty. The fuzzy AHP method was used in this study to overcome this difficulty. Three significant issues pertinent and essential to fuzzy AHP were addressed: contradictions in expert judgment, deriving priorities from inconsistent fuzzy judgment matrices, and group decision‐making. In this study, three FAHP methods: Chang's extent analysis (EA) method, fuzzy preference programming (FPP), and logarithmic fuzzy preference programming (LFPP) were used to calculate the weights of the r‐LPI parameters. The results of these methods were then compared, and it was determined that LFPP was the most suitable method for calculating the weights of the r‐LPI parameters.

Network attack evaluation model based on directed weighted complex network and fuzzy AHP method

At present, most of the network attack effect evaluation is based on mathematical theory. The calculation process is influenced by artificial judgment, and the research on the dependence and influence between indicators is not sufficient, so the index weight setting is too subjective. This paper proposes a network attack effect evaluation model based on directed weighted complex network, and constructs an index architecture based on attribute-attack type-atomic function-evaluation index four-layers subnetwork. The index importance calculation method is given by combining the complex network theory with the traditional fuzzy analytic hierarchy process. Finally, taking the password cracking attack as an example, it is proved that this method is helpful to explore the impact mechanism of indicators on attributes, fully considers the relationship between indicators, simplifies the index system and improves the evaluation efficiency.