Landfill site selection using multi criteria decision making: Influential factors for comparing locations

Rapid population growth integrated with poor governance and urban planning is highly challenging resulting key for the selection of unsuitable landfill sites, particularly in developing counties. Therefore, the aim of this study is to investigate the suitable solid waste landfill sites in the capital of the country as a case study, by the integration of Geographical Information System (GIS) with fuzzy logic, analytical hierarchy process (AHP), and weighted linear combination (WLC) method based on multi-criteria decision-making (MCDM). We chose thirteen (13) criteria (9 factors and 4 constraints) and grouped them into two main categories (environmental and socioeconomic) to achieve the objectives. The AHP was employed to evaluate the relative importance of the factors followed by standardization of criteria factors based on fuzzy set theory. Subsequently, all criteria factors were combined based on AHP and fuzzy logic-WLC method in order to obtain land suitability map. Finally, the sites were identified by the intersection of two combined suitability index layers. The obtained results depicted that the integration of fuzzy logic, AHP, and WLC technique with GIS can produce satisfactory results for the suitable locations of solid waste landfill sites over complex topographic regions. Overall, the land suitability obtained based on fuzzy-WLC is more refined and smooth because of its better segregation and its potential to consider full tradeoff between factors and average risk. The AHP was identified (47km2) as high suitable while fuzzy-WLC generated 36km2 as suitable area. Finally, the intersection of both suitability index map shows numerous suitable landfill sites available in Islamabad city; however, the surface areas of the sites are small at individual level (less than 15ha).

Previous researches outlined the advantages of the Analytical Hierarchy Process (AHP) and Analytic Network Process (ANP) methods in solving Multi-Attribute Decision Making (MADM) problems. The advancement of the above methods was continually developed as an effort to cover up various weaknesses. Mainly related to the consistency and linguistic variables in translating the expert opinions. Thus, it initialized the emergence of Fuzzy AHP (F-AHP) and Fuzzy ANP (F-ANP). Due to the restricted operation of these algorithms in smartphone selection, this research attempted to investigate the effectiveness of both methods in providing the analysis of criteria weight, the final recommendation weight, the product recommendation weight, and the execution time in DSS-SmartPhoneRec application development. A survey of one hundred respondents of University students identified the dominant criteria in selecting the smartphone, namely price, Random Access Memory (RAM), processor, internal memory, and camera. Hence, five alternative products were then chosen as the appropriate smartphones’ recommendations based on the respondent’s preferences. As an automatic tool, a DSS-SmartPhoneRec application was built to analyze and compare between F-AHP and F-ANP methods in resolving the smartphone selection cases. It revealed that the level of consistency of criteria weight, the final weight of recommendation, and the weight that the product-based F-ANP was 40% greater than F-AHP. In terms of execution time, F-AHP had a shorter time than F-ANP. Meanwhile, the comparison of products recommendation from DSS-SmartPhoneRec and a manual test showed that F-ANP was 16% more in line with the respondents’ predilection. In a nutshell, the DSS-SmartPhoneRec administered the devote smartphone recommendations based on the user’s expectation. The comparison analysis furnished a learning outcome for the users in determining the appropriate MADM method tailored to the type of cases.

In this study, an intelligent approach is presented to help any simulation practitioner to evaluate simulation software alternatives and determine the best satisfying one based on his/her needs. On the other hand, this evaluation process is a typical multiple criteria decision making (MCDM) problem in the presence of evaluation criteria and a set of possible alternatives, and there are many methods in the literature, which have been used to successfully carry out this difficult and time-consuming process. In this paper, one of these methods, the analytic network process (ANP) method integrated with alpha-cut fuzzy logic is used because it can accommodate the variety of interactions, dependencies and feedback between higher and lower level elements, rather than analytic hierarchy process (AHP). In addition, an alpha-cut fuzzy extension of ANP uses uncertain human preferences as input information in the decision-making process. Instead of using the classical eigenvector prioritisation method in AHP, only employed in the prioritisation stage of ANP, an alpha-cut fuzzy logic method providing more accuracy on judgements is applied. The resulting alpha-cut fuzzy ANP enhances the potential of the conventional ANP for dealing with imprecise and uncertain human comparison judgements.

The selection of suitable landfill sites is a critical challenge in rapidly urbanizing regions, where improper siting may cause long-term environmental degradation, public health risks, and land-use conflicts. This study integrates geological resource potential into a GIS-based Spatial Multi-Criteria Evaluation (SMCE) framework for landfill site selection in Lumbang District, Probolinggo Regency, East Java, Indonesia. The methodological approach combines the Indonesian National Standard for landfill siting (SNI 03-3241-1994) with the Analytical Hierarchy Process (AHP) to determine parameter weights, ensuring both regulatory compliance and scientific transparency. Four key parameters—lithology, groundwater depth, slope gradient, and rainfall intensity—were evaluated, supported by exclusion criteria such as proximity to settlements, rivers, and geological hazards. The final suitability index was calculated using a Weighted Linear Combination (WLC), producing a comprehensive landfill feasibility map. Results indicate that volcanic breccia formations with medium to deep groundwater conditions and gentle slopes (0–20%) offer the highest suitability, with Boto Village identified as the most feasible location. Importantly, the integration of geological resource potential as an exclusion criterion differentiates this study from previous approaches, preventing future conflicts over mineral and groundwater use. Despite limitations in DEM resolution, rainfall data, and resource inventories, the model demonstrates the necessity of incorporating environmental geology into landfill planning. The findings provide a robust decision-support tool for local authorities, offering policy recommendations to update the national landfill siting standard and strengthen sustainable waste management strategies in volcanic landscapes.

Given the growing competition in domestic and international agricultural product markets, choosing a business strategy compatible with requirements of marketing resources can guide agro-food firms to maintain and enhance competitive advantages. However, this is not as simple as it seems because the decision-making criteria expressed in a fuzzy manner and the relationship between them can be hierarchical or network-based. Therefore, the main goal of this study was to select the most suitable business strategy and to prioritize marketing resources for one of the major agro-food firms in Iran. To ensure the robustness of the results, both fuzzy analytic hierarchy process (AHP) and fuzzy analytic network process (ANP) were applied to prioritize business strategies and marketing resources. The results of both methods revealed that the differentiation strategy had the highest priority in terms of the experts' viewpoints. The results also showed that managerial and customer relationship capabilities were the most important criteria in selecting the differentiation strategy. According to the findings of the study, for the successful implementation of the differentiation strategy, company managers are recommended to take the following three main elements into huge consideration: financial conditions, paying attention to customer’s needs and requirements, and the introduction of new products and services.

One of the most important recent challenges in solid waste management throughout the world is site selection of sanitary landfill. Commonly, because of simultaneous effects of social, environmental, and technical parameters on suitability of a landfill site, landfill site selection is a complex process and depends on several criteria and regulations. This study develops a multi-criteria decision analysis (MCDA) process, which combines geographic information system (GIS) analysis with a fuzzy analytical hierarchy process (FAHP), to determine suitable sites for landfill construction in Iranshahr County, Iran. The GIS was used to calculate and classify selected criteria and FAHP was used to assess the criteria weights based on their effectiveness on selection of potential landfill sites. Finally, a suitability map was prepared by overlay analyses and suitable areas were identified. Four suitability classes within the study area were separated, including high, medium, low, and very low suitability areas, which represented 18%, 15%, 55%, and 12% of the study area, respectively.

Landfill site selection is a multi-criteria decision problem and has strategic value for various countries. The conventional method for landfill site selection is not enough to address the vague nature of linguistic assessment. An integrated Fuzzy AHP method was used to resolve this issue. Suitability maps produced by conventional Analytical Hierarchy Process (AHP) and integrated Fuzzy AHP methods were compared and validated, which adds a new dimension in the waste management field. Various environmental, socio-economic, and infrastructure factors were used as predictors, which included groundwater level, soil type, slope, land cover, distance to settlements, railways, roads, surface water sources, airport, power lines, wells, and protected areas. Relative Operation Characteristic (ROC) analyses gave results of 0.77 and 0.86 for the suitability maps of AHP and Fuzzy AHP, respectively. The landfill site suitability maps produced from this study could help urban planners, engineers, decision-makers, and land-use developers to make suitable decisions. Furthermore, the resulted integrated model showed the best suitability that could be implemented to resolve the fundamental landfill siting problems in other cities of Pakistan.

A shortage of land for waste disposal is one of the serious problems that faces urban areas in developing countries. The Sulaimaniyah Governorate, located in the north of Iraq, is one of the major cities in the Kurdistan Region of the country, covering an area of 2400 km2 with a population of 856,990 in 2016. Currently, there is no landfill site in the study area that meets scientific and environmental criteria, and inappropriate solid waste dumping is causing negative environmental impacts. The process of landfill site selection is considered a complex process and is restricted by numerous factors and regulations. This paper proposes multi-criteria decision-making (MCDM) methods in a model for landfill site decision. The model assumes the input of two groups of factors that need to satisfy the optimal values of weight coefficients. These groups of constants are natural factors and artificial factors, and they included thirteen selected criteria: slope, geology, land use, urban area, villages, rivers, groundwater, slope, elevation, soil, geology, road, oil and gas, land use, archaeology and power lines. The criteria were used in the geographic information system (GIS), which has a high capacity to process and analyze various data. In addition, multi-criteria decision-making (MCDM) methods followed by the weighted linear combination (WLC) method were used to derive criteria weightings using a matrix of pair-wise comparison. Finally, all the multi criteria decision methods were combined to obtain an intersection of the suitability index map for candidate landfill sites. Seven appropriate sites for landfill were suggested, all of which satisfied the scientific and environmental criteria which were adopted in this study.

PurposeThe paper aims to build a customized hybrid multi-criteria model to identify the top three utilities of drones at both personal and community levels for two use cases: firefighting in high-rise buildings and logistic support.Design/methodology/approachA hybrid multi-criterion model that integrates fuzzy analytical hierarchy process (AHP), Best Worst, fuzzy analytical network process (ANP), fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to compute the criteria weights. The weights are validated by a novel ensemble ranking technique further whetted by experts at the community and personal levels to two use cases.FindingsDrones' fire handling and disaster recovery utilities are the most important to fight fire in high-rise buildings at both personal and community levels. Similarly, drones' urban planning, municipal works and infrastructure inspection utilities are the most important for providing logistics support at personal and community levels.Originality/valueThe paper presents a novel multi-criteria approach, i.e. ensemble ranking, by combining the criteria ranking of individual methods – fuzzy AHP, Best-Worst, fuzzy ANP and fuzzy DEMATEL – in the ratio of optimal weights to each technique to generate the consolidated ranking. Domain experts also validate this ranking for robustness. This paper demonstrates a viable methodology to quantify the utilities of drones and their capabilities. The proposed model can be recalibrated for different use case scenarios of drones.

The use of logistics outsourcing or third-party logistics (3PL) is becoming a global trend. For suiting the needs of outsourcing companies, here raises a critical issue of how companies can evaluate and select a favorable logistics service provider. However, choosing a proper logistics service provider is a kind of multicriteria decision-making (MCDM) problem, which requires considering a large number of complex factors. The analytic network process (ANP) is a relative new MCDM theory of measurement used to deal with complex decision issues with dependence and feedback However, the approach is incapable of capturing the vagueness and fuzziness during value judgment elicitation. In view of this, a method to select a proper 3PL provider is proposed by applying an fuzzy ANP (FANP). The merits of the FANP lie in, not only could it handle the complexity of the problem structure, but also it may soundly deal with the judgments derived from intrinsically imprecise information. Finally, a real case application is given to validate the feasibility of the proposed approach.

Network screening and countermeasure selection are two crucial steps in the highway improvement process. In network screening, potential improvement locations are ranked and prioritized based on a specific method with a set of criteria. The most common practice by transportation agencies has been to use a simple scoring method, which, in general, weighs and scores each criterion and then ranks the locations based on their relative overall scoring. The method does not deal well with criteria that are qualitative in nature, nor does it account for the impacts of correlation among the criteria. The introduction of Analytic Hierarchy Process (AHP) provides agencies with a method to include both quantitative and qualitative criteria. However, it does not address the issue on correlation. This dissertation explores the use of both Analytic Network Process (ANP) and Fuzzy Analytic Network Process (FANP) for their potential capabilities to address both issues. Using urban four-lane divided highways in Florida for bicycle safety improvements, both ANP and FANP were shown to provide more reasonable rankings than AHP, with FANP providing the best results among the methods. After the locations are ranked and prioritized for improvements, the next step is to evaluate the potential countermeasures for improvements at the selected top-ranked locations. In this step, the standard practice has been to use Crash Modification Factors (CMFs) to quantify the potential impacts from implementing specific countermeasures. In this research, CMFs for bicycle crashes on urban facilities in Florida were developed using the Generalized Linear Model approach with a Zero-Inflated Negative Binomial (ZINB) distribution. The CMFs were tested for their spatial and temporal transferability and the results show only limited transferability both spatially and temporally. The CMFs show that, in general, wider lanes, lower speed limits, and presence of vegetation in the median reduce bicycle crashes, while presence of sidewalk and sidewalk barrier increase bicycle crashes. The research further considered bicycle exposure using the bicycle activity data from the Strava smartphone application. It was found that increased bicycle activity reduces bicycle crash probabilities on segments but increases bicycle crash probabilities at signalized intersections. Also, presence of bus stops and use of permissive signal phasing at intersections were found to increase bicycle crash probabilities.

Determining road handling priority is considered as a complicated multicriteria decision making problem. In so doing, the Analytic Hierarchy Process (AHP) has been widely used to weight the importance. Fuzziness and vagueness however, are typical in many decision-making problems, so that fuzzy sets could be integrated with the pairwise comparison as an extension of the AHP. This study uses Fuzzy Analytic Hierarchy Process (FAHP) and TOPSIS method in determining regencial road handling priority for road links under severe circumstances in Badung regency in Bali province. Data are taken from a previous study, which had also been conducted for Badung regencial road handling priority using the AHP and SK.NO.77/KPTS/Db/1990 method. The weights of main and sub criteria are determined using FAHP and subsequently the ranking of road links is determined using TOPSIS method. The AHP method gave somewhat different result to 'SK.NO.77/KPTS/Db/1990' method. On the other hand, FAHP and TOPSIS method produce the same result to 'SK.NO.77/KPTS/Db/1990' method. This is probably best explained by the fact that they similarly considered traffic volumes as the most significant factor. FAHP and TOPSIS method however, are preferred to the AHP and SK.NO.77/KPTS/Db/1990 method in determining regencial road handling priority in Badung regency.Abstrak. Prioritas penanganan jalan merupakan salah satu tugas berat dan penting yang dihadapi oleh pengambil keputusan pada pemerintah daerah. Pada kenyataannya, penentuan penanganan jalan dapat dilihat sebagai permasalahan pengambilan keputusan yang melibatkan banyak kriteria yang bersifat kompleks. Metode proses hirarki analitik (AHP) telah banyak digunakan untuk menentukan bobot kriteria di dalam penentuan prioritas penanganan jalan. Akan tetapi karena keragu-raguan merupakan hal yang lazim terjadi di dalam pengambilan keputusan, maka teknik fuzzy dapat dikombinasikan ke dalam metode AHP. Pada studi ini penentuan prioritas penanganan jalan kabupaten untuk kondisi rusak berat di Kabupaten Badung, Bali dilakukan dengan metode Fuzzy AHP (FAHP) dan TOPSIS. Data penelitian digunakan dari studi sebelumnya di Kabupaten Badung yang menggunakan metode AHP dan SK.NO.77/KPTS/Db/1990. Metode FAHP digunakan untuk pembobotan kriteria sedangkan metode TOPSIS digunakan untuk penentuan urutan ruas jalan yang akan mendapat penanganan. Metode AHP memberikan hasil yang sedikit berbeda dengan metode SK.NO.77/KPTS/Db/1990. Sementara itu FAHP dan metode TOPSIS memberikan hasil yang sama dengan metode SK.NO.77/KPTS/Db/1990. Hal ini kemungkinan karena kedua metode tersebut menggunakan volume lalu lintas sebagai faktor yang paling berpengaruh pada penelitian ini. FAHP dan metode TOPSIS lebih disarankan untuk digunakan di dalam penentuan prioritas penanganan jalan di Kabupaten Badung.

The rapid growth of educational technology highlights the essential need for personalized learning in blended environments. This study uses Multi-Criteria Decision Making (MCDM) methodologies, including the Analytic Hierarchy Process (AHP), Fuzzy AHP, and Analytic Network Process (ANP), to evaluate and prioritize personalization strategies in EdTech platforms. The research identifies data-driven adaptive learning as the most critical strategy (36.46%), followed by AI-powered content recommendations (15.46%) and personalized learning paths (15.11%). It reveals that personalization strategies are interconnected, creating dynamic feedback loops that reinforce one another, enabling continuous learning optimization. The study provides a holistic framework for educational technologists, policymakers, and designers. This approach bridges technological innovation with pedagogy, emphasizing adaptive, data-informed systems that respond dynamically to learner needs, ensuring the balance between innovation and educational quality.

Appropriate tree fertilization with essential nutrients is considered as one of the major factors in enhancing the quality and quantity of horticultural crops. The most efficient way to fertilize trees is to dig holes around the trunks and fill them with appropriate chemical and organic fertilizer. Doing this operation with mechanized methods reduces costs and increases productivity compared to traditional methods. In the present study, multi‐criteria decision‐making (MCDM) methods, including deterministic analytical hierarchy process (AHP) and fuzzy analytical hierarchy process (FAHP), technique for order of preference by similarity to ideal solution (TOPSIS), fuzzy TOPSIS (FTOPSIS), and analytic network process (ANP), were used to score and select the appropriate fertilizing method for apple trees based on the growers and expert's perspectives. The criteria, including fertilizing operation cost, crop yield, the percentage of tree damages, ease of entering and moving fertilizing equipment in tree rows, field capacity (with or without machinery), comfort and safety of fertilizing operations, after‐sales service, access to the required machinery and implements, crop selling price, and crop quality, were used in the above‐mentioned methods. The fertilization methods (Hole digging) considered in the present study were traditional fertilization (Shovel), orchard Trencher, motor hole digger, fixed centerline tractor‐mounted hole digger, and off‐set tractor‐mounted hole digger. Based on the results, the priority of mechanized fertilizing methods was determined as tractor‐mounted hole diggers (AHP weight of 0.286, FAHP weight of 0.285, TOPSIS relative proximity of 0.65, and FTOPSIS relative proximity of 0.64), fixed centerline tractor‐mounted hole diggers (AHP weight of 0.219, FAHP weight of 0.158, TOPSIS relative proximity of 0.56, and FTOPSIS relative proximity of 0.62), motor hole diggers (AHP weight of 0.171, FAHP weight of 0.079, TOPSIS relative proximity of 0.46, and FTOPSIS relative proximity of 0.31), and orchard trenchers (AHP weight of 0.12, FAHP weight of 0.057, TOPSIS relative proximity of 0.19, and FTOPSIS relative proximity of 0.20), respectively. Based on the ANP method, off‐set and fixed centerline tractor‐mounted hole diggers had the highest priority (weights of 0.43 and 0.27), followed by trencher (weight of 0.16), motor hole diggers (weight of 0.09), and the traditional method (weight of 0.04). Results showed that applying orchard tractors equipped with mounted diggers, especially off‐set types, can play an important role in enhancing the quantity and quality of apples produced, as well as reducing the costs of fertilizing operations.

Identification of influential nodes has emerged as one of the major challenges, especially after their use in the rapid propagation of information, epidemics, and so on in social media. Most of the previous works in this field deal with the homogeneous interactions that are not pertinent in the determination of the accurate context of the nodes due to their noisy and sparse nature. Hence, heterogeneous interactions need to be explored for the identification of influential nodes in the network. To consider heterogeneous interactions available within the network, a multilayer network (ML) has been designed in this work. Each layer of the network represents a particular type of interaction, e.g., upload, comment, retweet, reply, and mention. A heterogeneous degree ranking (HDR)-based influential nodes’ detection and ranking are proposed for the designed ML. Furthermore, multiple-criteria decision-making (MCDM) methods, such as the analytic hierarchy process (AHP), the technique for order of preference by similarity to ideal solution (TOPSIS), fuzzy AHP, fuzzy TOPSIS, and the analytic network process (ANP), are explored for the proposed ML for identification and ranking of the influential nodes. The susceptible–infected–recovered (SIR) model is used to evaluate the proposed work. In addition to this, statistical analysis is performed using the Pearson correlation, Kendall’s correlation, Spearman’s correlation, and the Friedman test on the ranks generated by different methods, which shows that the results generated by different proposed methods are consistent. Furthermore, the performance of the proposed method is compared with state-of-the-art approaches.