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The objective of this paper is to present, discuss and apply the principles and techniques of the Analytic Hierarchy Process (AHP) in the prioritization and selection of projects in a portfolio. AHP is one of the main mathematical models currently available to support the decision theory. When looking into how organizations decide over which projec...
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Citations
... Areas of higher elevation were examined for their potential to offer natural drainage, thereby diminishing the likelihood of waterlogging and accumulation of leachate, thus bolstering the overall stability of landfill operations over time because separation of comparatively high elevation areas subtracted to find out low-lying areas subjectively necessitating suitable site finding process. Conversely, lower-lying areas were scrutinized for their susceptibility to flooding and fluctuations in the water table, which could heighten the risk of contamination and disruption to the local ecology [26,77]. Shallow water depth areas are considered less suitable for landfilling, as they pose a higher risk of contamination. ...
The production of urban solid waste is one of the most dangerous environmental health risks on the planet. By selecting suitable landfill sites, it is possible to resolve the issue of health risks and environmental pollution driven by the unscientific disposal of solid waste. The current research is centered on determining suitable landfill locations for solid waste disposal within the Balurghat municipality through the integration of Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) approach. Ten factors were taken into consideration according to their weightage including elevation (0.022), Lithology (0.027), Slope (0.032), Land use and land cover LULC (0.040), Depth of water (0.057), Population density (0.075), Distance from sensitive places (0.107), Distance from road (0.154), Distance from river (0.205) and Land value (0.282). After conducting thorough field visits, an analytical hierarchy process (AHP) under the MCDM approach using GIS reveals acceptable sites that were noted and referenced. Final results are validated through a receiver operating characteristic (ROC) curve using randomly selected 80 potential landfill-suitable site location points with higher accuracy, and a total of 93% of the area under the curve (AUC) has been found worthy to validate the spatial result. The present investigation revealed that only 0.65% of land is very highly suitable, 31.06% of land is highly suitable, 52.54% of land is moderately suitable and 14.74% of land is not suitable for landfilling in Balurghat Municipal area and surroundings respectively. The highly suitable site belongs to the Mamna, Katna, Kuaran, Amtali, Noksha, Bannapara, and Malancha villages of the study area which is a very negligible area. So, the planners should adopt alternative strategies like source minimization, composting, sanitary landfills and awareness programmes for sustainable solid waste management. Hence, the Balurghat Municipality administration, urban planners, and administrative authorities stand to gain valuable insights from the findings of this study as they endeavour to enhance the management of urban solid waste in the area.
... A systematic but practical tool is needed to help make rational decisions. One of the most effective methods that help address complex decisionmaking is the analytic hierarchy process (AHP) (Andalecio 2010;Vargas 2010;Jagoda et al. 2020). ...
... The method known as AHP has caught the interest of many academics, due to its good mathematical nature and the ease with which the essential input data may be obtained [15]. Its simplicity is defined by comparing alternative pairs based on specific criteria [16]. Furthermore, the emergence of various software tools has popularized and simplified this procedure. ...
The high rate of stunting in children in East Java Province is a problem that has become a major focus in the national scope. Based on these problems, the researcher intends to increase the intake of animal protein from fish for fishermen’s families, by prioritizing fish processing in coastal areas of East Java and providing education and knowledge for fishermen in fish processing methods in coastal areas of East Java Province. An analysis was carried out using the macro ergonomic and Analytical Hierarchy Process (AHP) method to determine the priority of processed food from fish ingredients to reduce stunting rates. The criteria in this analysis are Community Preferences, Community Knowledge, Ergonomic process, Commodity Production, and Fish Processing Tools. The results of the analysis found that priority choices in percentage include Fish Nuggets, Fish Sticks, Fish Meatballs, and Fish Crackers. The final result of this study was the priority of processed fish food ingredients to reduce stunting rates in children in coastal areas of East Java Province. With these results, it can help SMEs and coastal communities to process fish food ingredients with the aim of developing communities and coastal areas and increasing fishermen’s family interest in consuming processed fish ingredients to reduce stunting.
... The AHP method has piqued the interest of numerous researchers, mainly due to its sound mathematical nature and the fact that the required input data is relatively easily obtainable [5]. Its simplicity is marked by comparing pairs of alternatives according to specific criteria [6]. Furthermore, this method has gained popularity and is user-friendly due to the development of various software programs. ...
Fish Smoking is a traditional method that has long been used in fisheries. The use of fish smoking devices can have a negative impact on human health which results in the entry of smoke into the eyes, lungs, and skin. In order not to be harmful to human health, ergonomic and healthy fish smoking devices are used. This study analyzes the prioritization of Small Medium Enterprises (SMEs) groups in the use of fish smoking devices to reduce the risk of health problems. The approach method used in this analysis uses the Ergonomics and Analytical Hierarchy Process (AHP) method to determine the priorities of SMEs groups in the use of fish smoking devices. The criteria in this analysis are Ergonomics (Safe, Comfortable, Healthy), Health, Education Level, Community Participation, Motivation, Habits, and Resistance. The results of the analysis found that priority choices in percentage include E, A, B, D, and C regencies. It is hoped that with the holding of Diffusion Technology Results of Innovation Products (DTHPI), the use of modern fish smoking devices that are more ergonomic, healthy, and practical can help improve the welfare of SMEs.
... El procedimiento de valoración se realizó por pares según la jerarquía 1-9 propuesta por (Saaty, 1977), donde 1 representa que ambos criterios o elementos son igual de importantes sobre la problemática de decisión y 9 considera que cada uno de los elementos comparados es absolutamente más importante; mientras que los números pares representan una diferencia razonable entre los elementos comparados y los impares representan una negociación entre estos (Celemín, 2014;Vargas, 2010). ...
... The Analytic Hierarchy Process (AHP), developed by Thomas L. Saaty (Saaty, T.L., 2008;Arnold F. Shapiro et al., 2017), provides a structured approach to complex decision-making by hierarchically organizing choices into goals, criteria, sub-criteria, and alternatives through pairwise comparisons using a defined scale (Saaty, T.L., 2008;Arnold F. Shapiro et al., 2017;Jagdish Bhadu et al., 2023;Vargas et al., 2010) (table 4). ...
... lement Cij signifies the relative importance of criterion Ci compared to criterion Cj. Notably, the reverse relationship is true for Cji, and for cases where i and j are not equal (i ∕ = j), Cii is equivalent to 1, ensuring consistency in the comparisons (Saaty, T.L., 2008;Sunah Moon, 2020;Arnold F. Shapiro et al., 2017;Jagdish Bhadu et al., 2023.;Vargas et al., 2010;Julio Benítez et al., 2011). ...
... To evaluate the matrix's coherence, AHP employs a consistency index that involves calculating the Eigenvalue and Eigenvector of the matrix (Equation 2) (Saaty, T.L., 2008; Sunah Moon, 2020; Arnold F. Shapiro et al., 2017;Jagdish Bhadu et al., 2023.;Vargas et al., 2010;Julio Benítez et al., 2011). ...
Rapid urbanization and population growth present pressing challenges for effective solid waste management in burgeoning cities. This study addresses the critical issue of landfill site selection in Kinshasa City, employing a systematic and objective approach through the integration of ArcGIS Multi-Criteria Decision-Making (MCDM) and the Analytic Hierarchy Process (AHP). The research utilizes diverse spatial datasets, encompassing factors such as proximity to residential areas, accessibility, land use, environmental constraints, and proximity to water bodies, to generate suitability maps. The findings reveal that there are large areas within Kinshasa City that exhibit high suitability for landfill establishment, and that the existing landfill in Kinshasa City is situated within areas deemed unsuitable based on the selected criteria in this study, underscoring the urgency of deeper analysis for future landfill siting, prioritizing the minimization of environmental impact, health risks for nearby communities, and ensurance of a long-term sustainability of waste management practices in Kinshasa City. These findings not only offer practical solutions but also open avenues for future research and refinement in urban waste management strategies.
The innovative combination of GIS technology, Multi-Criteria Decision-Making, and the identification of unsuitable existing landfill locations positions this study at the forefront of waste management research in Kinshasa city, with potential applicability in rapidly growing urban areas worldwide.
... For each driver of the Analytical Hierarchical Process (AHP) (Vargas, 2010 andSaaty andThomas L., 2003) was generated a proper factor, on the basis of a weighting for those qualities before mentioned across the archaeological park. Equal importance Two factors contribute equally to the objective 3 ...
In the last decade, research has been conducted to identify targeted searches and electromagnetic and aerial applications
applied to landscape heritage and ecosystems. Italy has progressed its jurisdictional authority by introducing normatives,
e.g., from “Carta Italiana del Restauro” (1932) to “art.10 D.gls. 42/2004”, of archaeological sites and parks, such as Segestae
Park in Sicily. To this end, research has been conducted to detect the reintroduction of autochthonous Mediterranean shrubs
using Sentinel 2-A fire index-based methods. Cadastral data of forestry and agricultural usage are incorporated, along with
Normalized Difference Vegetation Index (NDVI), Burned Area Index (dBAI), Mid-infrared Burn Index (dMIRBI) and Relativized
Burn Ratio (RBR). A topological subdivision based on digital elevation model (DEM) is implemented, while a deterrence fire
model and Environmental Analysis Hierarchy Process (AHP) pre-feasibility provide a fine-tuned health framework for
Mapping and Assessment of Ecosystems and their Services, with Environmental Impact Assessment and Strategic
Environmental Assessment
... AHP was developed in the 1970s by Thomas L. Saaty and has been studied extensively since then. It is currently applied for decision-making in several complex scenarios (Vargas, 2010). The use of AHP begins by decomposing the problem into a hierarchy of criteria that are more easily analyzed and independently comparable. ...
... This comparison can use concrete data from alternatives or human judgments as underlying information (Saaty, 2008). According to Vargas (2010), AHP transforms comparisons, often empirical, into numerical values processed and compared. The weight of each factor allows the estimation of each of the elements within the defined hierarchy. ...
This paper presents the application of a framework for identifying risks in the Ultrasonic Testing of critical parts. This topic is significant because failing to inspect critical parts with UT in the industry correctly may lead to operational failure. Catastrophic accidents can happen if risks are not identified and responses are not provided. In the European Congress for Reliability and Safety held in 2022 in Dublin, a framework proposal was presented based on the Analytic Hierarchy Process (AHP) and Bayesian Belief Network (BBN). This study complements the proposal presented in ESREL 2022, focusing on applying the framework. As a methodological approach, a survey was prepared to elicit experts’ probabilities. These were uploaded into BBN software to combine the risk factors contributing to an inspection failure. AHP was used to define to prioritize the impact of risk categories. The combination of probability and impact identified the most significant risk categories. As a result, the method revealed the most significant risk factors in UT. The conclusion is that the model proved adequate to reduce the risk of hardware failure significantly. As a contribution, the proposed method is an invaluable source of information for
safety engineers and decision-makers in companies. It can be generalized to other industries and fields of work that wear UT.
Find this paper in this link:
https://www.rpsonline.com.sg/proceedings/esrel2022/html/toc.html
... The risk factors that are configurable are assigned weights by employing the AHP [75][76][77][78][79][80][81][82][83], which is one of the most commonly used multi-criteria decision-making (MCDM) methods for computing weights of factors involved in decision making. MCDM [81,[84][85][86][87] is used when a decision involves taking multiple criteria into account in order to rank or choose between the alternatives. ...
Open innovation (OI) is key to sustainable product development and is increasingly gaining significance as the preferred model of innovation across industries. When compared to closed innovation, the protection of intellectual property (IP) that is created in open innovation is complex. For organisations engaging in OI, a sound IP management policy focusing on IP risk reduction plays a significant role in ensuring their sustained growth. Assessing the risks that are involved in IP management will enable firms to devise appropriate IP management strategies, which would ensure sufficient protection of an IP that is created in an OI model. Studies indicate that the risks which are associated with IP and risk management processes also vary with company segments that range from start-ups to micro, small, medium, and large organisations. This paper proposes an open innovation IP risk assessment model to compute the open innovation intellectual property risk score (OIIPRS) by employing an analytic hierarchy process. The OIIPRS indicates the IP risk levels of an organisation when it engages in open innovation with other organisations. The factors contributing to IP risk are identified and further classified as configurable IP risk factors, and the impact of these factors for the various company segments is also factored in when computing the OIIPRS. Further, an OI IP risk maturity model (OIIPRMM) is proposed. This model depicts the IP risk maturity of organisations based on the computed OIIPRS on an IP risk continuum, which categorises firms into five levels of IP risk maturity. The software firms can make use of the OIIPRMM to assess the level of IP risk and adopt proactive IP protection mechanisms while collaborating with other organisations.
... This normalization is done by dividing each coefficient in the matrix by the sum of the coefficients in the corresponding column. After normalization, the arithmetic mean of the values in each row of the resulting matrix is considered as an approximate value of the eigenvector, which corresponds to the relative weight of each indicator (Vargas, 2010). ...
... According to Vargas (2010), the AHP process has characteristics that ensure the consistency of its results. To verify the consistency of the weights assigned to the criteria and alternatives, the inconsistency index (λmax), the consistency index (CI), and the consistency ratio (CR) are calculated, according to equations 1, 2, and 3, respectively: (2005) The inconsistency index (λmax) measures the difference between the largest eigenvalue and the number of indicators, while the consistency index (CI) measures the ratio between the inconsistency index and a consistent random index (RI), which varies between 0 and 1. ...
... To verify the consistency of the weights assigned to the criteria and alternatives, the inconsistency index (λmax), the consistency index (CI), and the consistency ratio (CR) are calculated, according to equations 1, 2, and 3, respectively: (2005) The inconsistency index (λmax) measures the difference between the largest eigenvalue and the number of indicators, while the consistency index (CI) measures the ratio between the inconsistency index and a consistent random index (RI), which varies between 0 and 1. The consistency ratio (CR) is the ratio between the consistency index (CI) and the consistent index for the number of indicators used (CI), which also varies between 0 and 1. CR values above 0.1 indicate acceptable consistency, while values below 0.1 indicate inconsistency in the results (Vargas, 2010). The value of RI is a fixed value for a given number of evaluated indicators, n, according to Table 3 (generic). ...
In recent years, the world of Civil Engineering has been changing considerably (Liu et al., 2017; Zhou et al., 2020). Within the many obstacles, barriers, and opportunities, significant challenges should be considered for the future of the Civil Engineering, with the contribution of Geoinformatics, several scientific areas associated with Civil Engineering experience rapid technological evolution (Baig et al., 2022; Lousada, Cabezas, et al., 2022; Ray et al., 2023). Thus, evolution is related to several areas, such as structures, civil constructions, geotechnics, foundations, hydraulics, communication routes, regional and urban planning, sustainability, climate change and environmental impact, among others (Chau et al., 2015; Govindan et al., 2016; Han & Thakur, 2015; Jensen, 2020). Considering these challenges, we can work to portray the future of Civil Engineering.
Civil engineering has been greatly influenced by the evolution of Urban Politics and Geoinformatics, as these two fields have had a significant impact on the design, planning, and management of urban infrastructure (Bernhäuserová et al., 2022; Dilworth, 2020; Pineo et al., 2020).
Urban Politics has played a vital role in shaping the development of cities and urban areas (Robinson, 2016). Political decisions made at the local, regional, and national levels have a significant impact on the design and construction of infrastructure projects (Lieberman, 2002; Lousada, Cabezas, et al., 2022). Civil engineers must consider the political environment in which they operate, as well as the political priorities of elected officials and policymakers, when designing and implementing infrastructure projects (Fang et al., 2023; Jensen, 2020; Ray et al., 2023).
Geoinformatics, which involves the collection, analysis, and interpretation of geospatial data, has also had a significant impact on Civil Engineering. Geoinformatics technologies such as geographic information systems (GIS) and remote sensing have revolutionized the way that civil engineers plan and design infrastructure projects. These tools allow engineers to gather and analyze a vast amount of spatial data, which can be used to optimize infrastructure designs and improve the efficiency of construction and maintenance activities (Lousada et al., 2021; Lousada, Gonçalves, et al., 2022; Lousada & Castanho, 2022).
Geoinformatics, also known as Geographic Information Systems (GIS), is a powerful tool for supporting Urban Politics and Civil Engineering development. GIS is a computer-based system that integrates various types of geographic data, such as maps, satellite imagery, and demographic information, to analyze, visualize, and interpret spatial relationships (Baig et al., 2022; Ray et al., 2023; Singh et al., 2011).
In the context of Urban Politics, GIS can be used to support decision-making processes related to land-use planning, transportation, and infrastructure development. For example, GIS can be used to analyze the spatial distribution of population density, income levels, and transportation networks to identify areas that are underserved by public transportation. This information can then be used to guide the development of new transportation routes or to prioritize the allocation of resources for infrastructure development (Dilworth, 2020; Lieberman, 2002; Robinson, 2016).
In the field of Civil Engineering, GIS can be used to support the design and construction of buildings, roads, bridges, and other infrastructure. GIS can be used to analyze the suitability of different sites for construction projects, taking into account factors such as topography, soil type, and environmental conditions. GIS can also be used to monitor construction progress and to identify potential risks or hazards (Chau et al., 2015; Kotis et al., 2023; Lousada et al., 2021; Lousada, Gonçalves, et al., 2022).
Overall, Geoinformatics is a valuable tool for supporting Urban Politics and Civil Engineering development. By providing a comprehensive and integrated view of geographic data, GIS can help decisionmakers to make more informed and effective decisions, leading to more sustainable and resilient urban environments (Lieberman, 2002; Ray et al., 2023).
