Analytic Hierarchy Process Theory Research Articles

Study on Quantitative Assessment of CO2 Leakage Risk Along the Wellbore Under the Geological Storage of the Salt Water Layer

In the process of CO2 geological storage in the salt water layer, CO2 leakage along the wellbore will seriously affect the effective storage of CO2 in the target geological area. To solve this problem, based on the investigation of a large number of failure cases of CO2 storage along the wellbore and failure cases of gas storage wells in the injection stage of the wellbore, the influencing factors of CO2 leakage risk along the wellbore were investigated in detail. Based on the analytic hierarchy process (AHP) and extension theory, 17 basic evaluation indexes were selected from 6 perspectives to establish the evaluation index system of CO2 leakage risk along the wellbore. The established evaluation system was used to evaluate the leakage risk of a CO2 storage well in the X gas field of BZ Block. The results showed that the influencing factors of tubing had the smallest weight, followed by cement sheath, and the influencing factors of casing–cement sheath interface and cement sheath–formation interface had the largest weight, accounting for 23.73% and 34.32%, respectively. The CO2 storage well leakage risk evaluation grade was Ι, with minimal leakage risk. The CO2 storage effect was excellent. The evaluation system comprehensively considers the tubing string, cement sheath, and micro-annulus interface, which can provide a scientific basis for the risk assessment of CO2 leakage along the wellbore under the CO2 geological storage of the salt water layer.

Assessing organizational vulnerability of nuclear power plants using AHP-fuzzy sets method

The operational efficiency and developmental progress of nuclear power entities are significantly challenged by organizational vulnerability, which can lead to severe outcomes if neglected. This paper presents a systematic approach to identifying and quantifying the primary factors influencing organizational vulnerability within nuclear power plants (NPPs). An evaluative index is established, and an innovative hybrid methodology combining the Analytic Hierarchy Process (AHP) and fuzzy sets theory is applied to assess overall organizational vulnerability. A case study validates the approach, demonstrating low vulnerability and strong organizational reliability in the NPPs. The research contributes a valuable tool for enhancing safety and sustainability in the nuclear power sector.

Development and application of a comprehensive evaluation index system for groundwater quality evolution patterns

In recent years, driven by rapid socio-economic development and intensified human activities, the groundwater quality has exhibited a concerning trend of degradation. The challenge lies in integrating the impacts of both natural and anthropogenic factors to establish a scientific evaluation framework for the evolution of groundwater quality. This study adopts the model of driving forces – pressures – state – impacts – responses (DPSIR) proposed by the European Environment Agency, in conjunction with the Analytic Hierarchy Process (AHP) and Information Entropy Theory (IET), and the Water Quality Index (WQI) evaluation methods, to construct an evaluation index system for groundwater quality evolution that encompasses driving forces, state, and response systems. Initially, twelve indicators relevant to groundwater quality are quantified by screening across three systems, and a functional relationship between the categorization and scoring of each indicator is established. Subsequently, the weights for each system and indicator are obtained through the AHP, and the objective weights of the indicators are determined using the IET. The scores of each indicator are then comprehensively calculated. Finally, based on the defined types of groundwater quality evolution patterns, an integrated assessment of the evolution of groundwater quality over various time periods is conducted. Taking the Shijiazhuang region as a case study and analyzing the hydrochemical data of groundwater from 1985 to 2015, the results indicate a shift in the groundwater quality evolution pattern from one dominated by natural factors to one primarily influenced by human activities (The comprehensive score of the evaluation index system has increased from 1.84 to 3.25). Among these, the application of fertilizers emerges as the most important driving factors affecting groundwater quality. Particularly, nitrate and total hardness (TH) have emerged as the most salient indicators of quality degradation, with a significant escalation in their composite scores. At the outset, nitrate registered a score of 0.408, while TH scored 0.326; yet, these values have sharply ascended to 0.716 and 0.467, respectively, by the advanced stage. The study concludes with a discussion on the accuracy, strengths, limitations, and applicability of the evaluation index system. The establishment of this evaluation framework provides a scientific basis for the management and protection of groundwater resources and serves as a reference for identifying groundwater quality evolution patterns in other regions.

Advancing sustainability: An integrated decision support framework for fleet selection in open pit mining construction

The process of selecting and configuring equipment fleets for open-pit mining is complex, requiring consideration of both quantitative and qualitative criteria. This paper presents a novel comprehensive framework for selecting loading/hauling fleets in open-pit mining projects. Through an extensive review of the literature, a total of 21 selection criteria are initially identified. With guidance from local experts, these criteria are shortlisted into five primary groups: site conditions, managerial, social, and environmental criteria. The ranking of the identified selection criteria is carried out by employing the analytical hierarchy process (AHP) and multi-attribute utility theory (MAUT) methods, utilizing a questionnaire survey. The most crucial criteria in selecting loading/hauling equipment are identified as the equipment's operating cost, productivity, repair and maintenance costs, and soil condition. An optimization module is developed to optimize the selected fleet, while adhering to sustainability principles and project constraints. The optimization module considers social, economic, and environmental sustainability variables to determine the optimal number, size, type, and capacity of fleets needed to complete the work within the allotted time. As a case study, the proposed framework is validated by experts in these types of projects. The framework's ability to effectively select fleet units for sustainable loading/hauling fleet is confirmed through this validation method.

Objective Evaluation of Automatic Ambulance Performance and Weight Determination Through Analytic Hierarchy Process and Gray Correlation Theory

This paper proposes a comprehensive framework for testing and evaluating automatic ambulances, crucial for ensuring their reliability and safety in real-world scenarios. The framework includes designing test scenarios with varying complexity, covering environmental factors like road conditions, weather, and obstacles. An evaluation index system is introduced, comprising driving security, ride comfort, intelligence, and efficiency. Methodologies for calculating indicator weights, using the CRITIC and AHP methods, are presented to ensure fair evaluation. Additionally, evaluation methods including qualitative and quantitative techniques, such as grey correlation theory, are discussed. The test results show that the assessment results of the traditional fuzzy comprehensive evaluation method and the grey correlation theory evaluation method are highly consistent. The change in vehicle speed has less of an effect on accuracy during the real-time assessment process when the time interval is set to 0.1s, and the evaluation time of 0.098s can satisfy the requirement that the planning time of autonomous driving vehicles be shorter than 200 ms.

A framework of index system for gauging the sustainability of iranian provinces by fusing analytical hierarchy process (AHP) and rough set theory (RST)

Urban sustainability is essential to address the challenges posed by population explosion, excessive consumption, and environmental disasters. Controversial discussions between governments and academics in countries like Iran challenge sustainable development. This paper provides an integrated Analytical Hierarchy Process (AHP) and Rough Set Theory (RST) for determining attribute weights, and presents a new application of variance maximization in a mathematical model to determine more realistic integrated weights. An empirical study was conducted in 31 provinces of Iran using data from 2012 to 2019 to evaluate overall sustainability; sustainability in three pillars of social equity (SE), economic dynamism (ED), and environmental protection (EP); as well as to understand the importance of criteria in a localized system consisting of 27 sub-criteria. In terms of overall sustainability, Semnan and Tehran had the highest score with around 50 % and Sistan scoring the lowest at around 18 %. In terms of SE and ED, the regions were generally balanced. However, the eastern border provinces performed poorly in terms of EP. Zanjan had the greatest percentage of sustainable growth (29.25 %), while Sistan obtained the lowest (−19.75 %). The most important factors in strengthening SE, ED, and EP were healthcare, workforce, and environmental quality, respectively. This approach may assess sustainability from many national, provincial, and local dimensions, and its flexibility as well as the compatibility of the estimated findings with sensitivity analysis was proved. Then, strategies to improve urban sustainability, including increasing cooperation between provinces and regions and balanced growth in the pillars of sustainability, are presented.

A multi-criteria decision-making model for heavy construction equipment replacement in Saudi Arabia

Purpose This paper aims to propose a novel model that addresses the limitations of current practices, through considering quantitative and qualitative criteria in the decision-making process for equipment replacement. Design/methodology/approach Literature review and consultation with professionals in the heavy construction industry was conducted to identify the criteria influencing the replacement of construction machines. A questionnaire survey using analytic hierarchy process and multi-attribute utility theory was used to rank these criteria and establish their utility scores. Sensitivity analysis was performed to assess how adjustments in the weights of main criteria would impact equipment replacement decisions. Findings The identified criteria were classified into three categories: economic, technical and socioenvironmental, encompassing a total of 15 criteria. The findings indicated that salvage value/meeting payback period/maximizing profitability held the highest importance in the replacement process, followed by considerations like high repair and maintenance cost; working condition and economic conditions. Safety and social benefits scored the least among all criteria and categories. Research limitations/implications This study focuses on earth-moving equipment and involves experts from the Eastern Province of Saudi Arabia. The model introduces a novel methodology to aid decision-makers, particularly contractors and project managers, in determining when to replace heavy construction equipment, which results in resource efficiency and time saving. Originality/value The model integrates expertise and knowledge from experts to establish criteria for replacing construction equipment. This research aims to improve the functionality of the decision-making process regarding the acquisition or replacement of equipment throughout its lifespan.

Consistency Improvement in the Analytic Hierarchy Process

Consistency checking is one of the reasons for the Analytic Hierarchy Process (AHP) leadership in publications on multiple criteria decision-making (MCDM). Consistency is a measure of the quality of data input in the AHP. The theory of AHP provides indicators for the consistency of data. When an indicator is out of the desired interval, the data must be reviewed. This article presents a method for improving the consistency of reviewing the data input in an AHP application. First, a conventional literature review is presented on the theme. Then, an innovative tool of artificial intelligence is shown to confirm the main result of the conventional review: this topic is still attracting interest from AHP and MCDM researchers. Finally, a simple technique for consistency improvement is presented and illustrated with a practical case of MCDM: supplier selection by a company.

Thermoeconomic performance of supercritical carbon dioxide Brayton cycle systems for CNG engine waste heat recovery

The supercritical carbon dioxide (S–CO2) Brayton cycle is a potential technology for recovering waste heat from compressed natural gas (CNG) engines. To choose suitable cycle configurations is important considering the complex operating characteristics of CNG engines. The traditional evaluation models of the S–CO2 cycles configuration are relatively one-sided without the comprehensive considerations of evaluation metrics. On the basis of the analytic hierarchy process and information entropy theory, this paper proposes a multi-dimensional comprehensive evaluation method for S–CO2 cycles from three perspectives: thermodynamic, economic, and environmental performance. A thermoeconomic performance optimization is conducted using a non-dominated genetic algorithm Ⅱ. Unlike most of the existing researches that use a single stable operating condition, this paper considers the full operating conditions of engines. Results show that the recompression cycle has the best overall performance among the investigated cycles with an exergy efficiency and electricity production cost of 67.20 % and 0.45 $/kWh, respectively. The efficiency improvement of the CNG engine coupled with the WHR system can reach up to 6.31 %. This study can provide a new reference for the comprehensive multidimensional evaluation and performance characteristics and optimal performance acquisition under full engine operating conditions of the S–CO2 cycle.

Urban Tunnel Body Landscape Driving Comprehensive Evaluation Study Based on Biomass-Sensing Automobile Field Experiment

Previously, in regard to tunnel design and research, the focus was primarily on traffic capacity and safety requirements, with less consideration given to cave landscape design and its impacts on drivers. This study addressed this gap by proposing a comprehensive evaluation system for urban tunnel landscape driving based on the Analytic Hierarchy Process (AHP) theory. Considering the information and perception aspects of the driving process and the unique landscape characteristics of urban tunnels, we utilized the drivers’ perception of biomass as an index layer and performed a simulation using a machine learning algorithm. The proposed model was validated through vehicle field tests that were conducted in four urban tunnels along with a substantial amount of measured biomass data obtained during the experiments. The research demonstrated a strong correlation between the urban tunnel body landscape and the driving comprehensive index, particularly under relevant biomass conditions, which revealed the interactive relationship between urban tunnel body landscape design parameters and biomass. Furthermore, the study analyzed and proposed the impact degree of the urban tunnel body landscape on drivers’ biomass indicators, which offered valuable insights into designing tunnel body landscapes with consideration for biomass perception.

Research on Energy Efficiency Evaluation Model of Substation Building Based on AHP and Fuzzy Comprehensive Theory

The traditional energy-saving evaluation method for industrial buildings is intended for all industrial buildings; however, substation buildings belong to a special category of industrial buildings, and their energy consumption characteristics are different from those of general industrial buildings. Consequently, it is necessary to establish an energy-saving evaluation system for substation buildings according to the characteristics of their energy consumption. In view of the issue that the energy consumption characteristics of substation buildings are different from those of other industrial buildings, an analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) are used to establish a more comprehensive energy saving evaluation model that is more applicable to substation buildings. This paper determines 19 quantitative indicators and 13 qualitative indicators through the screening of relevant standards and norms, as well as the literature, and then determines the weight of each indicator by using AHP before finally establishing a secondary evaluation model based on FCE. In this paper, a substation in Shandong, China was selected as a case study to verify the proposed evaluation model, scoring 80.4 points, which falls within the “Good” grade. This method is of great significance for the future establishment of energy-saving evaluation system for substation buildings.

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.

An integrated decision support framework for selecting envelope and AC systems in hot-humid climate

This paper aims to introduce a framework that applies life cycle cost (LCC)-based optimization, life cycle assessment (LCA) and multi-criteria decision-making (MCDM) methods for the selection of optimum envelope materials and AC systems for housing projects. A total of 37 selection criteria and 28 material alternatives were first identified through a comprehensive literature review and interviews with local experts. “DesignBuilder”, a computer simulation software, was used to conduct the LCC-based optimization and LCA for the identified alternatives. Furthermore, uncertainties and risk associated with the selected alternatives were computed using Monte Carlo simulation via the Crystal Ball software and Efficient Frontier analysis via the Excel Spreadsheets. The identified selection criteria were then prioritized and assigned with utility scores based on the analytical hierarchy process (AHP) and multi-attribute utility theory (MAUT) methods. Seven scenarios with different objectives were set for an existing case study. The implementation of the framework showed that when taken into account the application of multi-criteria methods, the highest saving combination of materials is selected by the framework. The validation process was carried out through conducting interviews with local experts based on the results obtained from real case project. The experts agreed that the proposed framework can be beneficial to the industry as it takes into consideration not only quantitative criteria, but also qualitative criteria that are important to owners and designers.

Prioritization of Hazardous Zones Using an Advanced Risk Management Model Combining the Analytic Hierarchy Process and Fuzzy Set Theory

Risk management plays a vital role in ensuring the safety and efficiency of tunnel construction by considering various factors, including uncertainties associated with concurrent adverse sources. One key aspect of risk management is prioritizing hazardous zones to devise an optimal countermeasure plan within time and cost constraints. This study developed an advanced tunnel risk management model, combining the analytic hierarchy process (AHP) and fuzzy set theory (FST). The model derived the impact using AHP and the probability using FST. By selectively combining causal factors that met the selection criterion, the risk of each hazardous zone was determined, enabling the prioritization of identified hazardous zones. The model application results indicated that causal combinations associated with significant tunnel convergence posed a relatively high risk. Moreover, the hazardous zones where unstable ground formations were excavated by a gripper tunnel boring machine (TBM) were revealed as the most vulnerable locations. Consequently, adopting a shield TBM or implementing ground reinforcement is recommended. Overall, the developed model effectively prioritizes identified hazardous zones and provides an optimal countermeasure plan, contributing to the overall safety and efficiency of the operations.

A basic algorithm for generating an individualized numerical scale

Linguistic labels are an effective means of expressing qualitative assessments because they account for the uncertain nature of human preferences. However, to perform computations with linguistic labels, they must first be converted into numbers using a scale function. Within the context of the Analytic Hierarchy Process (AHP), the most popular scale used to represent linguistic labels numerically is the linear 1–9 scale, which was proposed by Saaty. However, this scale has been criticized by several researchers, and various alternatives have been proposed. There is a growing interest in scale individualization rather than relying on a generic fixed scale because the perceptions of the decision maker regarding these linguistic labels are highly subjective. The methods proposed in the literature for scale individualization focus on minimizing transitivity errors (i.e., consistency). In this paper, we propose a novel, easy-to-learn, easy-to-implement, and computationally less demanding scale individualization approach based on compatibility. We also developed an experimental setup and introduced two new metrics that can be used by researchers that contribute to the theory of AHP. To assess the value of scale individualization in general and the performance of the proposed novel approach, numerical and two empirical studies were conducted. The results of the analyses demonstrate that scale individualization outperforms the conventional fixed-scale approach and validates the benefits of the proposed novel heuristic.

Revolutionizing Repairability of Industrial Electronics in Oil and Gas Sector: A Mathematical Model for the Index of Repairability (IOR) as a Novel Technique

The oil and gas (O&G) field is the most sought-after industry in the Gulf Cooperation Countries (GCCs) and holds significant importance in the region’s economy. Therefore, this sector requires various industrial electrical, and electronics equipment (EEE) products to perform multiple tasks throughout the upstream, downstream, and midstream segments. However, as these EEE products approach their end of life (EoL), the sector faces the challenge of managing failed units. As a result, replacing or recycling failed EEE products can contribute to the growing problem of electronic waste (e-waste), which can have severe environmental consequences. In addition, while some EEE products can be repaired or remanufactured with low reliability, many others cannot be fixed due to various technical reasons. This paper’s primary goal is to propose a circular economy strategy and sustainable practices that promote the longevity of industrial EoL electronic products in the O&G sector through remanufacturing. We introduced and implemented a new mathematical score, the Index Of Repairability (IOR), which aims to assess the ease of EEE repairability in the O&G sector and improve their lifespan and durability based on four criteria: design, spare parts availability, software access, and documentation. This novel mathematical metric leverages the analytic hierarchy process (AHP) and set theory. Additionally, original equipment manufacturers (OEMs) can adopt and benefit from this innovative IOR by incorporating eco-design principles and designing more easily repairable industrial products for technicians, thereby reducing the negative impact of e-waste, enhancing stakeholder satisfaction, and minimizing downtime. Furthermore, governmental organizations can implement regulations and incentives to advocate for and mandate the use of the IOR by OEMs, ensuring that the electronics industry prioritizes repairability, remanufacturing, and sustainability.

Risk Assessment of Immersed Tube Tunnel Construction

Due to the complexity of risk factors in constructing immersed tube tunnels, it is impossible to accurately identify risks. To solve this problem, and the uncertainty and fuzziness of risk factors, a risk assessment method for immersed tube tunnel construction was proposed based on WBS-RBS (Work Breakdown Structure-Risk Breakdown Structure), improved AHP (analytic hierarchy process), and cloud model theory. WBS-RBS was used to analyze the risk factors of immersed tube tunnel construction from the aspects of the construction process and 4M1E, and built a more comprehensive and accurate construction risk index system. The weight of each index was calculated by the improved AHP of a genetic algorithm. The cloud model theory was used to build the cloud map of risk assessment for immersed tunnel construction and evaluate construction risk. Taking the Dalian Bay subsea tunnel project as an example, the risk assessment method of immersed tunnel construction was verified. The results showed that this method not only solved the problem of failing the consistency check in the higher-order judgment matrix but also improved the consistency pass rate by 33.3% and accurately reflected the risk assessment results. The assessment results show that the construction risk level of the Dalian Bay submarine-immersed tunnel is medium. The risk level of indicators “slope instability” and “water-stop damage” are high risk, while “pipe section cracking”, “low underwater alignment accuracy”, “uneven crimping of a water-stop”, and “uneven substrate treatment” are medium risk. This provides a reference for the risk assessment study of immersed tunnel construction.

A Dynamic Prediction Model of Financial Distress in the Financial Sharing Environment

The dynamic prediction of financial distress can monitor the financial status of an enterprise in real time and provide evidence for financial analysts. However, currently, there are few studies concerning the dynamic prediction of financial distress in the financial sharing environment, so in order to fill this research gap, this study established a dynamic prediction model of financial distress in the financial sharing environment. Firstly, this study employed the analytic hierarchy process (AHP) and entropy weight theory to determine an index system for the dynamic evaluation of financial distress in the financial sharing environment and gave the weight assignment method of the evaluation indexes. Then, based on the probabilistic neural network (PNN), this study constructed a dynamic prediction model of financial distress and used experimental results to verify the effectiveness and feasibility of the constructed model.

Study on the Exploitation Potential of Ecotourism in Leshan City based on a Multi-Hierarchy Grey Method

Leshan City is rich in ecological tourism resources. The evaluation of their potential for exploitation can provide scientific guidance for the development of ecological tourism resources in the city. Based on the analytic hierarchy process and grey theory, seven counties in Leshan City were selected as the basis for constructing the evaluation system of ecotourism resource exploitation potential from four aspects: tourism resource conditions, tourism resource exploitation conditions, tourism resource development benefits and tourism resource value. Through the comprehensive evaluation and sorting of the development potential of ecotourism resources in the city, the order of priority for the exploitation of ecotourism resources in the region was determined. According to the results of the model calculations, the exploitation potentials of ecotourism resources in the seven counties and districts in Leshan City could be divided into three levels. Among them, Muchuan County, Emeishan City, Ebian Yi Autonomous County and Mabian Yi Autonomous County are the first-level ecotourism resource exploitation zones; Jinkouhe District is at the second-level; and Jingyan County and Jiajiang County have the least exploitation potential as the third-level ecotourism resource exploitation zone. The results of this evaluation are approximately consistent with the actual situation of Leshan City, which indicates that the evaluation of the exploitation potential of ecotourism resources in this region based on the multi-hierarchy grey method can provide a theoretical basis for helping Leshan City to build characteristic ecotourism products, so that it can serve as a new development anchor for building a globally important tourist destination.

Optimizing exterior lighting illuminance and spectrum for human, environmental, and economic factors.

With the recent widespread adoption of LED lighting in outdoor areas, numerous concerns have been raised about the potential for harmful effects on humans, animals, plants, and the night sky. These stem from the high blue light content of some LED bulbs and an incentive to increase lighting levels caused by higher efficiency and lower costs. While new lighting installations are often described as environmentally friendly due to their energy efficiency, factors such as light pollution are often neglected or not given enough weight. This research focuses on optimizing the design of exterior lighting for human, environmental, and economic factors using a multi-criteria decision analysis. Based on data in the literature and survey research, illuminance and spectrum alternatives were scored relative to each other using the analytic hierarchy process and multi-attribute utility theory. The findings of this study support the use of artificial illumination at levels similar to a full moon (0.01 fc) and a warm white spectrum (2700K or 2200K), with amber LED becoming a better choice if its energy efficiency and cost effectiveness improve in the future. This methodology can be used in the future as a framework for lighting design optimization in different settings.