Analytical Hierarchical Process Research Articles

Assessing ecopreneurship impact on composite sustainability in the food and beverage industry: A holistic AHP approach integrating economic and environmental dimensions.

The implementation of ecopreneurship is evolving to address environmental sustainability challenges posed by industrial pollution. Scholars are exploring ways to enhance ecopreneurship implementation globally. However, there's a gap in understanding how ecopreneurship affects sustainability across economic and environmental dimensions. This study aims to explore the impact of ecopreneurship on sustainability, considering both economic and environmental indicators. The investigation was conducted through Analytical Hierarchical Process (AHP) methodology covering 102 ecopreneurs operating in the Tanzania food and beverage industry. The results of the study showed that implementation of ecopreneurship results in the attainment of sustainability through composite scores of 79.5%. In conclusion, this study underscores the significant impact of ecopreneurship implementation on composite sustainability, as evidenced by notable reductions in environmental emissions and improvements in economic performance within the studied industries. The findings highlight the importance of integrating environmental considerations into entrepreneurial endeavors for achieving sustainability goals. It is recommended that stakeholders actively promote and incentivize the adoption of ecopreneurship practices, invest in eco-friendly technologies, foster collaboration, implement effective monitoring mechanisms, and integrate sustainability into business strategies. The study recommends stakeholders' efforts directed towards enhancing the implementation of ecopreneurship practices in line with the United Nations Sustainable Development Goals (SDGs, 2015–2030), specifically goal nine (9) on pollution prevention through the implementation of Eco practices-based solutions to foster innovations and sustainable industrialization. Thus, strategizing the implementation of ecopreneurship practices is considered to have an essential contribution to addressing the challenges of sustainability and therefore enriching the realization of the global sustainable development agenda.

Application of GIS in Evaluating Suitable Clam Farming Site in Vembanad Lake, India

The present study identifies and quantifies appropriate sites for black clam farming in the Vembanad Lake using a geographical information system (GIS). The black clam, Villorita cpyrinoides, is the most important clam landed in India. Despite being a candidate species for aquaculture, the trials on farming of clams are very limited. The aim of this study was to select the most suitable sites for clam farming in Vembanad Lake based on the use of GIS-based models. For this, the importance of soil quality, water quality and infrastructure facilities were accessed using an analytical hierarchical process. The site suitability map was prepared using each attribute and divided into four classes, such as most suitable, moderately suitable, least suitable and poor. Water quality is found to play a major role (54%) compared to soil quality (24%) and infrastructure facilities (22%) for development of clam farms in Vembanad Lake. Salinity registered the highest importance (0.21) for the water quality suitability map as compared to other parameters like pH and dissolved oxygen. The total area covered under this study was 6471 ha out of which, 3121 ha (48%) was identified as most suitable and of the remaining 3350 ha, 1804 ha (28%) was identified as moderately suitable, 946 ha (15%) was identified as least suitable and 600 ha (9%) was identified as the poor site for clam farming in Vembanad Lake.

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

In the context of a dry port, sustainable operations involve developing and implementing policies and procedures that reduce adverse effects on the environment, advance economic viability, and strengthen social responsibility. Several factors contribute to achieving environmental, economic, and social sustainability, making it critical to identify the factors influencing the sustainability of dry port operations. This research aims to identify decisive factors associated with economic, social, and environmental sustainability, and to develop a framework for measuring sustainability in dry port operations. The research utilises exploratory factor analysis (EFA) and the analytical hierarchical process (AHP) to develop a measurement framework for assessing the sustainability of Ethiopian dry ports. EFA is an effective method with which to identify factors that contribute to sustainable dry port operations. To provide a frame for the critical sustainability performance metrics for dry ports, the AHP approach was used. Data were collected from 300 stakeholders using surveys to identify key factors, and 20 senior experts were involved in validating and rating the most influential factors determining dry port sustainability. This research asserts the most pertinent factors guiding dry port sustainability operations, resource allocation, and decision-making. From an environmental sustainability perspective, critical factors include minimising business-partner impacts, reducing waste, addressing climate change, providing environmental planning education to teams, and implementing measures to protect the national environment. From a social perspective, the factors identified include a resettlement policy, employment opportunities for the community, workplace safety, stakeholder consultation, and top-management guidance. From an economic standpoint, critical factors include value-added services, reduced transportation costs, decreased time, enhanced productivity in the trade supply chain, and profit orientation. The research provides valuable insights with which to guide the development of practices and policies aimed at ensuring sustainable dry port operations, a critical domain of the trade supply chain.

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

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

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 biosynthesised 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 Aluminium 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 favourable nanofluid, followed by Copper-Oxide and Cerium-Oxide, while Aluminium-Oxide received the lowest priority in optimizing PV panel performance.

Assessing classification system for Landslide susceptibility using Frequency Ratio, Analytical hierarchical process and geospatial technology mapping in Aizawl district, NE India

Landslides are a severe geological hazard in northeastern India, particularly in Mizoram's Aizawl district. To assess this hazard, we conducted a landslide susceptibility mapping study using the frequency ratio (FR) and analytical hierarchy process (AHP) methods. Twelve factors, including slope, elevation, aspect, curvature, topographic wetness index, topographic ruggedness index, rainfall erosivity factor, geology, land use land cover, normalized difference vegetation index (NDVI), and distance to road, were considered. The final susceptibility maps were created by combining these factors, each weighted according to its importance. Since no standard classification guidelines existed, we compared different systems like GIS-based geometric interval (GIC), natural breaks (NBC), and quantile interval (QIC) with mean-standard deviation (MSC). The area was divided into five susceptibility zones: very high, high, moderate, low, and very low. We evaluated the accuracy of these systems using success rate curve (SRC) and quality sum ratio (Qs) techniques. SRC results showed similar performance for GIC, NBC, QIC, and MSC classifications. The AHP method also yielded comparable results. After analyzing the AUC under SRC and Qs, we selected the MSC classification system as the optimal approach for landslide susceptibility mapping. Notably, the distance to roads factor had the highest predictive value, highlighting its importance in identifying slope instability for effective management strategies. As landslides pose significant risks in northeastern India, and our study using FR and AHP methods, along with MSC classification, provides valuable insights for mitigating this geological hazard.

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.

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.

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.