Analytical Hierarchical Process Research Articles

Developing a Measurement Framework for Ethiopian Dry Port Sustainability: An Empirical Study

Developing a Measurement Framework for Ethiopian Dry Port Sustainability: An Empirical Study

Optimizing Requirements Prioritization for IoT Applications Using Extended Analytical Hierarchical Process and an Advanced Grouping Framework

Effective requirement collection and prioritization are paramount within the inherently distributed nature of the Internet of Things (IoT) application. Current methods typically categorize IoT application requirements subjectively into inessential, desirable, and mandatory groups. This often leads to prioritization challenges, especially when dealing with requirements of equal importance and when the number of requirements grows. This increases the complexity of the Analytical Hierarchical Process (AHP) to O(n2) dimensions. This research introduces a novel framework that integrates an enhanced AHP with an advanced grouping model to address these issues. This integrated approach mitigates the subjectivity found in traditional grouping methods and efficiently manages larger sets of requirements. The framework consists of two main modules: the Pre-processing Module and the Prioritization Module. The latter includes three units: the Grouping Processing Unit (GPU) for initial classification using a new grouping approach, the Review Processing Unit (RPU) for post-grouping assessment, and the AHP Processing Unit (APU) for final prioritization. This framework is evaluated through a detailed case study, demonstrating its ability to effectively streamline requirement prioritization in IoT applications, thereby enhancing design quality and operational efficiency.

Identifying key barriers to joining an energy community using AHP

Energy communities (ECs) offer a promising solution for achieving sustainable and decentralized energy systems. However, the successful establishment and operation of ECs requires overcoming barriers that can hinder stakeholder participation. Existing research has primarily focused on incentives and motivations to join ECs, thus neglecting a comprehensive understanding of the key barriers affecting EC stakeholders in European Union (EU) countries. This paper aims to fill this research gap by identifying and ranking the barriers to joining ECs in the EU context with focus on Spain and Italy. To accomplish this, a framework of barriers was developed based on 20 in-depth interviews with diverse stakeholders of established ECs and a survey (n = 56). The barriers identified were categorized into four types: (a) financial, (b) regulatory and bureaucratic, (c) technical and practical, and (d) social and cultural. The analytical hierarchical process (AHP) methodology was employed to estimate and rank these barriers. The findings highlight that the most significant barrier categories are regulatory and bureaucratic, and financial. Specifically, regulatory complexity and legal limitations emerge as the top-ranked barriers among the obstacles identified to joining ECs by research participants.

A hybrid MCDM optimization for utilization of novel set of biosynthesized nanofluids on thermal performance for solar thermal collectors

Recent advancements in solar technology have spurred researchers to develop a precise and cost-effective method for monitoring solar radiation across diverse environmental conditions. This study focuses on optimizing performance parameters by replacing conventional nanofluids, produced through chemical and physical processes, with biosynthesized counterparts. Biosynthesized nanoparticles, derived from biological sources like microorganisms or plants, offer a promising avenue for enhancing solar panel efficiency. Through the construction and testing of an integrated photovoltaic thermal system, varying operating parameters have been explored to maximize photovoltaic efficiency. The highest overall efficiency, reaching approximately 62 %, was achieved with biosynthesized Graphene oxide nanofluid under higher Direct Normal Irradiance (DNI) levels, while the lowest was observed with Aluminum oxide nanofluid at lower DNI values. Employing Multi-Criteria Decision methods such as Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), nanofluids were ranked based on criteria including Exergy loss, Surface temperature, Overall efficiency, and Electrical efficiency, with weights of 49.58 %, 28.43 %, 13.45 %, and 8.54 % respectively. TOPSIS prioritized Graphene Oxide as the most favorable nanofluid, followed by Copper-Oxide and Cerium-Oxide, while Aluminum-Oxide received the lowest priority in optimizing PV panel performance.

MECHANISM FOR INFORMATION SUPPORTING THE FINANCIAL AND ECONOMIC SECURITY OF INFORMATION AND TELECOMMUNICATION ENTERPRISES UNDER THE INFLUENCE OF MODERN CYBER THREATS

The main purpose of the article is to develop a modern mechanism for information support of financial and economic security under the influence of the most significant cyber threats. As part of the study, the importance and weight of this type of threat to financial and economic security as a cyberspatial action has been proven. The object of the study is open socio-economic systems engaged in information and telecommunications activities on the territory of Ukraine under martial law. It is substantiated that in a state of war, the increased influence of modern cyber threats significantly reduces the level of financial and economic security. The research methodology involves the use of a diverse number of methods, the main of which are: the method of system analysis, expert analysis, the Delphi method, modelling and the analytical-hierarchical process. As a result of the study, changes were identified in the dynamics of key performance indicators of institutions in the field of information and telecommunication services, which have a direct impact on ensuring financial and economic security. The need to improve information support in order to improve the level of security has been proven. A modern mechanism for information support of financial and economic security has been developed, which, unlike similar ones, focuses on the principles of countering cyber threats. The key most significant cyber threats to institutions in the field of information and telecommunication services today, under martial law, have been identified. Their ordering made it possible to better understand which measures should be applied first and which should not. The proposed approach to streamlining cyber threats forms the information basis for effectively ensuring financial and economic security in modern conditions.

Empirical analysis of decision recommendation models for various processes from a pragmatic perspective

Decision recommendation models allow researchers and process designers to identify and implement high-efficiency processes in ambiguous situations. These models perform multipara metric analysis on the given process sets to recommend high-quality decisions that assist in improving process-based efficiency levels. A wide variety of models have been proposed by researchers for the implementation of such recommenders, and each of them varies in terms of their functional nuances, applicative advantages, internal operating characteristics, contextual limitations, and deployment-specific future scopes. Thus, it is difficult for researchers and process designers to identify optimal models for functionality-specific use. Therefore, they tend to validate multiple process models, which increase deployment time, cost and complexity levels. To overcome this ambiguity, a detailed survey of different decision process recommendation models is discussed in this text. Fuzzy logic, analytical hierarchical processing (AHP), the technique for order performance by similarity to ideal solution (TOPSIS), and their variants are highly useful for the recommendation of efficient decisions. Based on this survey, readers will be able to identify recently proposed decision recommendation models and functionality-specific models for their deployments. To further assist in the model selection process, this paper compares the reviewed models in terms of their computational complexity, recommendation efficiency, delay needed for recommendation, scalability and contextual accuracy. Based on this comparison, readers will be able to identify performance-specific models for their deployments. This paper also evaluates a novel decision recommendation rank metric (DRRM), which combines these parameters, to identify models that can optimally perform w.r.t. multiple process metrics. Referring to this parameter comparison, readers will be able to identify optimal recommendation models for enhancing the performance of their decision recommendations under real-time scenarios.

GIS-Based Analytical Hierarchy Process Modelling and Mapping of Erosion Vulnerability in the Coastal Areas of Rivers State, Nigeria

The problem of coastal erosion in rivers State Nigeria is a significant issue that has far-reaching consequences for the environment and local communities. Despite the efforts of previous research there remains a lack of comprehensive understanding of the factors contributing to erosion vulnerability and their relative importance, hindering effective decision-making and management practices aimed at mitigating the effects of coastal erosion in Rivers State. Therefore, this study aimed at a GIS-based analytical hierarchy process modeling and mapping of coastal erosion vulnerability in Rivers State, Nigeria. The objectives are to establish and classify the geophysical factors according to the levels of coastal erosion risk, calculate the reliability index of the classified geophysical factors, determine the coastal vulnerable areas across Rivers State using analytical hierarchical process and to produce a coastal vulnerability index map defining the extent of erosion vulnerability in Rivers State. The methodology comprises of the acquisition of primary and secondary data, image pre-processing, image classification, DEM processing, classification and standardization of factors, development of pairwise comparism, and weighted linear combination analysis. The study revealed three distinct coastal erosion vulnerability zones: high, moderate, and low vulnerability. The high vulnerability zone encompassed a total expanse of 545.29 square kilometers, constituting 6.38% of the study area. In contrast, the moderate and low vulnerability zones covered 1941.33 square kilometers and 6052.51 square kilometers, respectively, making up 22.73% and 70.89% of the total area. Bonny (139.28 sq km) was ranked as the most vulnerable due to its role as an oil and gas hub. Degema (111.28 sq km) ranked second and requires urgent erosion control. Okrika and Andoni (71.73 sq km and 62.20 sq km) were third and fourth respectively. It is recommended that an advocate for the systematic approach to coastal vulnerability zoning be introduced in the study. The categorization of areas into high, moderate, and low vulnerability zones provides a standardized framework for assessing coastal regions' susceptibility to erosion. This approach can be applied to other regions to facilitate consistent vulnerability assessments.

Applying multi-criteria decision analysis in the bidding process of an own social security regime institute

Public administration is responsible for managing public resources and offering various types of services to the population. Therefore, to ensure that the maintenance of state bodies is carried out in a responsible manner, both for the acquisition of goods and for the contracting of services, it is necessary to carry out bidding processes that follow constitutional principles. In this way, the use of Multi-Criteria Decision Analysis (MCDA) methods can help decision makers to solve selection problems in public institutions. In this context, the objective of this paper is to select, from a pre-defined list of ten alternatives, the three cars considered most suitable to meet the needs of an Own Social Security Regime Institute, allowing the opening of a bidding process more reliable. To carry out this selection, three MCDA methods were used to aid the choice: Analytical Hierarchical Process (AHP), PROMETHEE II and Weighted Sum Method (WSM). The results demonstrated that the three methods applied together managed to select the three best cars for carrying out the bidding process and that the use of these tools has great potential for the area of public management.

Safety assessment of marine high-end equipment based on evidential reasoning approach under fuzzy uncertainty

Marine high-end equipment reflects a country’s comprehensive national strength. The safety assessment of it is very important to avoid accident either from human or facility factors. Attribute structure and assessment approach are two key points in the safety assessment of marine high-end equipment. In this paper, we construct a hierarchical attribute structure based on literature review and text mining of reports and news. The hierarchical attribute structure includes human, equipment, environment and management level. The correlations among these attributes are analyzed. The assessment standards of attributes are described in details. Different evaluation grades associated with attributes are transformed to a unified one by the given rules. As for the assessment approach, the evidential reasoning approach is applied for uncertain information fusion. Group analytical hierarchical process is used to generate attribute weights from a group of experts, where process aggregation method and result aggregation method are combined in a comprehensive way. The importance of expert is computed by the uncertainty measure of expert’s subjective judgment. A drilling platform is finally assessed by the proposed attribute structure and assessment approach to illustrate the effectiveness of the assessment framework.

Evaluation of management practices in rice–wheat cropping system using multicriteria decision-making methods in conservation agriculture

In this study, we employed two multiple criteria decision-making (MCDM) methods, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and the Analytic Hierarchic Process (AHP), to determine the best management choice for the cultivation of wheat with a regime of conservation agriculture (CA) practices. By combining alternative tillage approaches, such as reduced tillage and zero tillage, with the quantity of crop residues and fertilizer application, we were able to develop the regime of CA practices. The performance of the regimes compared to the conventional ones was then evaluated using conflicting parameters relating to energy use, economics, agronomy, plant protection, and soil science. TOPSIS assigned a grade to each alternative based on how close it was to the ideal solution and how far away it was from the negative ideal solution. However, employing AHP, we determined the weights of each of the main and sub-parameters used for this study using pairwise comparison. With TOPSIS, we found ZERO1 (0% residue + 100% NPK) followed by ZERO4 (50%residue + 100% NPK), and ZERO2 (100% residue + 50% NPK) were the best performing tillage-based alternatives. To best optimize the performance of wheat crops under various CA regimes, TOPSIS assisted the decision-makers in distinguishing the effects of the parameters on the outcome and identifying the potential for maneuvering the weak links. The outcomes of this investigation could be used to improve management techniques for wheat production with CA practices for upscaling among the farmers.

GIS-Based Multi-criteria Decision-Making Techniques and Analytical Hierarchical Process for Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment: Case Study of Biskra City, Southern Algeria

GIS-Based Multi-criteria Decision-Making Techniques and Analytical Hierarchical Process for Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment: Case Study of Biskra City, Southern Algeria

Abolishing Single-Use Plastic Water Bottles in Dubai Hotels as a Voluntary Act—Scenarios and Environmental Impacts

Dubai, a popular vacation spot, has launched an initiative to reduce reliance on single-use plastic water bottles. Tourists in Dubai widely utilize PET (Polyethylene Terephthalate) water bottles, and significant quantities of greenhouse gases (GHG) are released during the production and disposal of PET bottles. In response to Dubai’s initiative, some hotels eliminated PET bottles and substituted them with environmentally favorable alternatives. These hotels are considered adopters of the initiative, while other hotels that might follow are imitators. Thus, innovation diffusion theory (IDT) is used in this work to forecast the transition of hotels to non-PET bottles. The diffusion of this new behavior is simulated using a system dynamic (SD) model, where factors pushing imitators to abolish PET bottles are found using the Delphi method and hotel surveying. Moreover, the importance of each identified factor is found using an analytical hierarchical process (AHP). Since hotels are divided into several categories based on their service quality, the analysis shows that hotels are affected by other hotels in their category or better categories. Using this conceptual understanding, Bass and generalized Bass modeling are used in the SD model to study how imitating hotels will follow the adopters. Best-, average-, and worst-case scenarios are studied to help decision-makers understand what to expect in the future. For the best- and average-case scenarios, the SD simulation shows that all hotels will potentially have abolished PET bottles in 25 years. However, only 16% of hotels will have cancelled PET bottles in 25 years if the worst-case scenario occurs; thus, decision-makers need to intervene to expedite the process.

Comprehensive Performance Evaluation of Earthquake Search and Rescue Robots Based on Improved FAHP and Radar Chart

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for an evaluation method that quantifies their performance and facilitates the selection of rescue robots with optimal overall capabilities. In this paper, twenty-two evaluation criteria are proposed based on a comprehensive review of existing evaluation criteria for rescue robots across various domains. The evaluation criteria are tested using the test modules developed by the National Earthquake Response support service, obtaining the corresponding values for each criterion. Then, the weights of the criterion layer and comprehensive evaluation index are determined based on the analytical hierarch process and trapezoidal fuzzy number complementary judgment matrix, and a new consistency test method is proposed. The qualitative evaluation and quantitative analysis are effectively combined to overcome the subjective influence of expert decision-making. Additionally, the performance of three earthquake search and rescue robots is comprehensively evaluated and ranked using the improved radar chart method as an empirical example. Finally, the robustness of the ranking results is examined using a weight sensitivity analysis. The results of the sensitivity analysis demonstrate the effectiveness and feasibility of the proposed method, thereby providing valuable insights for developing multi-objective optimization control strategies and structural designs for earthquake search and rescue robots.

Analysis of barriers to investment and mining in cryptocurrency for traditional and tech-savvy investors: A fuzzy approach

Recently, cryptocurrencies have attracted a lot of interest. It is acquired and traded over the blockchain system and is believed to be the future money, upending the global financial system. However, investment and mining of cryptocurrencies are still in its infancy stage due to many hurdles. Cryptocurrency adoption (CA) is uneven across investors of diverse backgrounds because different factors drive cryptocurrency adoption in different ways. The study identifies nine barriers and forty-seven sub-barriers in CA through a literature review and other qualitative methods. The fuzzy Delphi method (FDM) is used for the initial screening of identified sub-barriers. Barriers related to sustainability issues have been filtered out in the initial screening. Further, the ranking of barriers and sub-barriers has been performed for tech expert investors as well as conventional investors by using the fuzzy analytical hierarchical process (F-AHP). Results of the study indicate that for traditional investors, risk is the most influential barrier whereas government regulation is the most critical barrier for tech-savvy investors. The study reveals that environmental concerns and awareness are the least important barriers for traditional and tech-savvy investors, respectively.

Developing quality indicators for cancer hospitals in China: a national modified Delphi process

ObjectiveAlthough demand and supply of cancer care have been rapidly increasing in recent decades, there is a lack of systemic quality measurement for cancer hospitals in China. This study aimed...

Assessment of water shortage in underground aquifer in Abuja during dry season

Water shortage is a complex issue with severe implications for human well-being, agriculture, and ecosystems. Climate change, population growth, and inadequate water management practices contribute to this global problem. This study is to identify the causes of ground water scarcity and determine groundwater potential zones in the Abuja community. The research area's borehole coordinates, Landsat 8 satellite imagery, AsterDEM 30m resolution DEM data, and vertical electrical sounding (VES) data were all used to do this. Unsupervised classification of Landsat imagery was used to create a layer of land use and cover, image filtering techniques were used to improve the visualization of lineament extraction, and density analysis was used to quantify lineament density. AsterDEM data was subjected to surface analysis to acquire elevation data, while VES data were interpolated to produce spatial distributions of the resistivity and thickness parameters. To identify groundwater potential zones, multiple criterion analysis using the analytical hierarchical process (AHP) was utilized to reclassify variables such as land use, elevation, lineament density, resistivity, and thickness. To create the zones, these characteristics were weighted and layered. According to the findings, resistivity levels varied from 6.25 m to 676 m, and the geologic ground structure's thickness ranged from 0.27 m to 92.37 m. Elevation varied from 41 meters to 720 meters, while lineament density ranged from 0 to 0.04. 74% of the land was categorized as vegetated, 21% as built up, 2.7% as water bodies, and 2.3% as rock formations. Within Abuja, zones of groundwater potential were identified as being very low (5%), low (26.7%), marginally high (26.3%), moderately high (28.5%), and very high (13.4%)

Delineation of groundwater prospective zones using multivariate and spatial analysis techniques in Henan Province North China Plain

Groundwater is an essential resource that meets all of humanity’s daily water demands, supports industrial development, influences agricultural output, and maintains ecological equilibrium. The objective behind this research is to find groundwater prospective zones and their sustainable development in Henan Province, located in the North China Plain, with the help of analytical hierarchical process (AHP) and frequency ratio (FR) models. Consequently, a groundwater inventory map was created, utilizing 70% of the data for training and 30% for testing purposes. The resulting maps were overlaid with groundwater levels and the locations of wells within the study area. The performance and validation of the models were assessed using the Area Under Curve (AUC) curve, which indicated an accuracy of 70% for the AHP model and 69% for the FR model. The study indicates that areas with suitable gradients and rainfall patterns generally have high groundwater prospective zonation (GWPZ), i.e., Zhumadian and Nanyang (ZN). However, AHP demonstrated a higher predictive capability in comparison to FR the broader context. Whereas GIS-RS methodology aligns with existing wells and field data that validate it in range of good and excellent. In conclusion, this study provides valuable insights for effective groundwater exploration and sustainable water supply planning in Henan Province.

Analysis of the socio-economic parameters of farms applying soil restoration strategies in Bulgaria and Austria

This article presents the assessment of the degree of influence of socio-economic parameters of farms applying soil restoration strategies over the supply chain cooperation, public-private partnerships, and landscape alliances in Austria and Bulgaria. The aim of the assessment is to highlight new socio-economic opportunities, which can be a target for the public policies. The study is based on the pilot farms analysis of the TUdi1 project survey results. The analysis of the socio-economic parameters is made with an Analytical Hierarchical Process (AHP). The results are analyzed on both county and soil restoration strategy base. The analysis shows that Fertilization strategies have impact on supply chain cooperation. The goal aims to involve all suppliers and the most important socio-economic opportunities are certainty of demand, access to finance, and uncertainty of income. In terms of fertilization and remediation strategies, the analysis shows that the most important socio-economic opportunities are related to certainty of demand and access to finance. On the other hand, the implementation of Remediation strategy requires higher consensus in the local society and more activities, which explains the stronger interaction of this criteria with the goals public-private partnership and landscape alliances. In this case, the most important socio-economic opportunities are certainty of demand, access to finance, political support, and training and equipment.

Development of an automated method for flood inundation monitoring, flood hazard, and soil erosion susceptibility assessment using machine learning and AHP–MCE techniques

BackgroundOperational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms, such as Google Earth Engine and robust methodology (Otsu’s algorithm).ObjectivesThe current study employs Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam State, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART), and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. Furthermore, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping.SummaryThe highest prediction accuracy was observed for the RF model (overall accuracy [OA] > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Over 26% of the study area indicated high flood hazard-prone areas, and approximately 60% showed high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as it helps to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness ofemergency response efforts. Periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, and climate resilience studies.

Assessment of flood risk using Hierarchical Analysis Process method and Remote Sensing systems through arid catchment in southeastern Tunisia

Flood hazard mapping is a fundamental tool for estimating vulnerability in flood-prone areas and assessing flood risk in arid areas, enabling better management. This study specifically focuses on analyzing flood risk in the Gabes catchment, South-Tunisia, which is marked by exceptional flash floods attributed to climate change, notably in the past fifty years. For this aim, a combination of Geographic Information Systems (GIS) coupled to Remote Sensing Techniques and the Analytic Hierarchy Process (AHP) method was applied in this work to identify flood-prone regions within the Gabes catchment. The selected physical parameters are rainfall, slope, slope curvature, elevation, land cover, distance from river, drainage density, and soil type. The relative significance of each parameter was analyzed within a pairwise matrix. Eventually, a regional flood risk map was performed that shows the global susceptibility index classification of various areas within the catchment. Notably, flood risk zones were predominantly concentrated in the coastal areas, accounting for 57.54% of Gabes catchment. The major findings of this study, focusing on potential flood risks in arid regions, offer crucial insights for decision-makers, providing essential data for flood preparedness and mitigation strategies. Nevertheless, it is still required to validate the obtained results with field records and flood inventory maps.