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The exponential growth of online information has LED to significant challenges in navigating data overload, particularly in the tourism industry. Travelers are overwhelmed with choices regarding destinations, accommodations, dining, and attractions, making it difficult to select options that best meet their needs. Recommender systems have emerged as a promising solution to this problem, aiding users in decision-making by providing personalized suggestions based on their preferences. Traditional collaborative filtering (CF) methods, however, face limitations, such as data sparsity and reliance on single rating scores, which do not fully capture the complexity of user preferences. This study proposes a hybrid multi-criteria trust-enhanced CF (HMCTeCF) algorithm to improve the accuracy and robustness of tourism recommendations. HMCTeCF improves the quality of recommendations by integrating multi-criteria user preferences with trust relationships among users and between items. Experimental results using real-world datasets, including Restaurants-TripAdvisor and Hotels-TripAdvisor, demonstrate that HMCTeCF outperforms benchmark CF-based recommendation methods. It achieves higher prediction accuracy and coverage rate, effectively addressing the data sparsity problem. This innovative algorithm facilitates a more personalized and enriching travel experience, particularly in scenarios with limited user data. The findings highlight the importance of considering multiple criteria and trust relationships in developing robust recommendation systems for the tourism industry. [ABSTRACT FROM AUTHOR]

Selecting the appropriate desalination and renewable energy technologies is crucial for the success of desalination projects, as each technology offers distinct advantages and disadvantages tailored to specific project requirements. This research investigates the application of both the analytic hierarchy process and fuzzy logic techniques to develop four decision-making models: two for selecting the optimal desalination technology and two for selecting the optimal renewable energy technology in coastal communities. For desalination technology selection, the analytic hierarchy process model is structured into four hierarchical levels: the main goal, criteria, sub-criteria, and alternatives. The criteria level encompasses four groups, while the sub-criteria level comprises 26 factors. The alternatives considered are reverse osmosis, electrodialysis, and multi-stage flash. In parallel, the analytic hierarchy process model for renewable energy technology selection is similarly structured, with four criteria groups and 24 sub-criteria factors. The alternatives evaluated include photovoltaic, concentrated solar power, and wind energy. Additionally, fuzzy logic models are developed for both desalination and renewable energy technology selection. These models enhance the decision-making framework by incorporating the uncertainty and vagueness that are inherent in real-world scenarios. The integration of analytic hierarchy process and fuzzy logic methodologies provide a robust approach to identifying optimal technologies, thereby supporting sustainable development in Egypt's water–energy nexus. The research outcomes highlight the effectiveness of integrating analytic hierarchy process and fuzzy logic in decision-making processes, offering decision-makers systematic and reliable approaches for selecting the most suitable technologies to achieve sustainability in water–energy nexus projects. The results of the research indicate that the best alternative for desalination was reverse osmosis, and for renewable energy was photovoltaics. [ABSTRACT FROM AUTHOR]

Whether a slope is naturally occurring or constructed, its stability is a critical situation. During the designing part of a construction, engineers can use a variety of tools in order to dimension slope stability and predict seismic displacement. Some of the available methods are pseudo-static, Newmark and numerical analysis with finite elements. The main part of the study is the evaluation of those slope displacement methods according to engineering criteria in purpose of reaching the most profitable and reliable technology. One of the main advantages of choosing the better technique is the prediction of seismic displacements in the most accurate way, especially when a slope contains oil and gas pipelines. The fewer slope displacement incidents the best energy saving results. Meaning that we can save fuels by avoiding destructive situations. Analytical hierarchy process can provide such an evaluation after applying engineering criteria for the aforementioned techniques. The important contribution of this study to the world of knowledge is the ranking of those seismic displacement methods engineering terms, since it is the first time that is applied in such way. [ABSTRACT FROM AUTHOR]

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Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator's torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot's workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly impacted the financial performance of various sectors across the globe, including the Indian pharmaceutical industry. This study aims to evaluate the financial performance of ten Indian pharmaceutical companies listed in the S&P BSE Healthcare Index over two distinct periods: before COVID-19 (2018–2020) and during the pandemic (2020–2022). A hybrid multicriteria decision-making (MCDM) approach, integrating the Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), is employed to assess companies based on five key financial dimensions and several performance indicators. Results indicate that profitability, valuation, and growth ratios were the most critical dimensions, with weights of 0.21 each, followed by liquidity (0.19) and efficiency (0.18). Furthermore, among the companies evaluated, Divis Labs and Abbott India emerged as top performers, both during and before the pandemic, with Divis Labs registering closeness coefficients of 0.871 and 0.814 during 2020–2021 and 2021–2022. The findings highlight the financial resilience of these companies, offering valuable insights for stakeholders in formulating strategies to sustain financial stability during future crises.

Abstract This study addresses the problem of recommending restaurants in emerging tourist destinations, taking into account factors vital in these locations, such as location, safety, price and services. The novel recommendation model is based on the well-known logical scoring of preferences (LSP) methodology. The system considers individual preferences across a hierarchy of criteria. The user can customize the recommender by providing suitability scores and aggregation operators for each criterion. The first contribution is the identification of relevant criteria for the selection of restaurants in emerging destinations and the definition of a new scoring system to manage user preferences regarding types of food. The second contribution of this study is the selection of appropriate conjunctive/disjunctive aggregation operators. The recommender system has been tested in a use case in Riohacha (Colombia), obtaining promising results in a wide range of user profiles.

Abstract Porcine pasteurellosis is an infectious disease caused by Pasteurella multocida (P. multocida), which seriously endangers the healthy development of pig breeding industry. Early detection of disease transmission in animals is a crucial early warning for humans. Therefore, predicting risk areas for disease is essential for public health authorities to adopt preventive measures and control strategies against diseases. In this study, we developed a predictive model based on multi-criteria decision analysis (MCDA) and assessed risk areas for porcine pasteurellosis in the Chinese mainland. By using principal component analysis, the weights of seven spatial risk factors were determined. Fuzzy membership function was used to standardize all risk factors, and weight linear combination was used to create a risk map. The sensitivity of the risk map was analyzed by calculating the mean of absolute change rates of risk factors, as well as calculating an uncertainty map. The results showed that risk areas for porcine pasteurellosis were predicted to be locate in the south-central of the Chinese mainland, including Sichuan, Chongqing, Guangdong, and Guangxi. The maximum standard deviation of the uncertain map was less than 0.01and the ROC results showed that the prediction model has moderate predictive performance with the area under the curve (AUC) value of 0.80 (95% CI 0.75–0.84). Based on the above process, MCDA was combined with WebGIS technology to construct a system for predicting risk areas of porcine pasteurellosis. Risk factor data was directly linked to the developed model, providing decision support for disease prevention and control through monthly updates.

Porcine pasteurellosis is an infectious disease caused by Pasteurella multocida (P. multocida), which seriously endangers the healthy development of pig breeding industry. Early detection of disease transmission in animals is a crucial early warning for humans. Therefore, predicting risk areas for disease is essential for public health authorities to adopt preventive measures and control strategies against diseases. In this study, we developed a predictive model based on multi-criteria decision analysis (MCDA) and assessed risk areas for porcine pasteurellosis in the Chinese mainland. By using principal component analysis, the weights of seven spatial risk factors were determined. Fuzzy membership function was used to standardize all risk factors, and weight linear combination was used to create a risk map. The sensitivity of the risk map was analyzed by calculating the mean of absolute change rates of risk factors, as well as calculating an uncertainty map. The results showed that risk areas for porcine pasteurellosis were predicted to be locate in the south-central of the Chinese mainland, including Sichuan, Chongqing, Guangdong, and Guangxi. The maximum standard deviation of the uncertain map was less than 0.01and the ROC results showed that the prediction model has moderate predictive performance with the area under the curve (AUC) value of 0.80 (95% CI 0.75–0.84). Based on the above process, MCDA was combined with WebGIS technology to construct a system for predicting risk areas of porcine pasteurellosis. Risk factor data was directly linked to the developed model, providing decision support for disease prevention and control through monthly updates. [ABSTRACT FROM AUTHOR]

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain. [ABSTRACT FROM AUTHOR]

Uncontrolled wildfires pose a significant threat, potentially causing extensive damage to biodiversity, soil quality and human resources. It's crucial to swiftly detect and predict these wildfires to minimize their catastrophic consequences. To address this, our research introduces a wildfire prediction model that ranks cities based on risk leveraging multi-criteria decision-making (MCDM) to systematically assess conflicting factors in decision-making. This model integrates wildfire risks into a city's resilience strategy, utilizing fuzzy set theory to manage imprecise data and uncertainties. As part of this approach, we compile a new dataset encompassing weather patterns, vegetation types, terrain features and population density across various Californian cities. Ultimately, the model assesses and ranks the wildfire risk for each city in California. [ABSTRACT FROM AUTHOR]

Assessment of rock mass quality significantly impacts the design and construction of underground and open-pit mines from the point of stability and economy. This study develops the novel Gromov-Hausdorff distance for rock quality (GHDQR) methodology for rock mass quality rating based on multi-criteria grey metric space. It usually presents the quality of surrounding rock by classes (metric spaces) with specified properties and adequate interval-grey numbers. Measuring the distance between surrounding rock sample characteristics and existing classes represents the core of this study. The Gromov-Hausdorff distance is an especially useful discriminant function, i.e. a classifier to calculate these distances, and assess the quality of the surrounding rock. The efficiency of the developed methodology is analyzed using the Mean Absolute Percentage Error (MAPE) technique. Seven existing methods, such as the Gaussian cloud method, Discriminant method, Mutation series method, Artificial neural network (ANN), Support vector machine (SVM), Grey wolf optimizer and Support vector classification method (GWO-SVC) and Rock mass rating method (RMR) are used for comparison with the proposed GHDQR method. The share of the highly accurate category of 85.71% clearly indicates compliance with actual values obtained by the compared methods. The results of comparisons showed that the model enables objective, efficient, and reliable assessment of rock mass quality. [ABSTRACT FROM AUTHOR]

Today, with the increase in population, technological developments, industrialization and urbanization, problems related to waste management (WM) have become increasingly important to a sustainable and global clean environment. The gradual deterioration of the quality of environmental elements and the increase in environmental problems have caused societies to focus more on environmental problems. Waste management is a form of management that includes the prevention, non-prevention, reuse, recovery, and disposal of domestic, medical, hazardous, and non-hazardous wastes. This study aims to prioritize critical success factors (CSFs), via the Analytical Hierarchy Process (AHP), in industrial waste management and to determine the most important critical success factor. The four main criteria and 23 sub-criteria were scored by the AHP method according to the opinions of five environmental engineers. After determining critical success factors, survey questions were prepared to make employees rank these factors. While the "national/local waste management strategies and policies" factor was the most important critical success factor according to environmental engineers, the most important critical success factor for employees was "enterprise waste management strategies and policies". In addition, differences in the priorities of CSFs were found in the opinions of employees in different sectors. [ABSTRACT FROM AUTHOR]

Climatic conditions are the main factor influencing the suitability of agricultural land for crop production. Therefore, the evaluation of climate change impact on crop suitability using the best possible methods and data is needed for successful agricultural climate change adaptation. This study presents the application of a multi-criteria evaluation approach to assess climate suitability for maize production in Poland, for a baseline period (BL, 1981–2010) and two future periods 2041–2070 (2050s) and 2071–2100 (2080s) under two RCP (Representative Concentration Pathways) scenarios: RCP4.5 and RCP8.5. The analyses incorporated expert knowledge using the Analytical Hierarchy Process (AHP) into the evaluation of criteria weights. The results showed that maturity and frost stress were the most limiting factors in assessing the climatic suitability of maize cultivation in Poland, with 30% and 11% of Poland classified as marginally suitable or not suitable for maize cultivation, respectively. In the future climate, the area limited by maturity and frost stress factors is projected to decrease, while the area of water stress and heat stress is projected to increase. For 2050 climate projections, water stress limitation areas occupy 7% and 8% of Poland for RCP4.5 and RCP8.5, respectively, while for 2080 projections, the same areas occupy 12% and 32% of the country, respectively. By 2080, heat stress will become a limiting factor for maize cultivation; according to our analysis, 3% of the Polish area under RCP8.5 will be marginally suitable for maize cultivation because of heat stress. The overall analyses showed that most of Poland in the BL climate is in the high suitability class (62%) and 38% is moderately suitable for maize cultivation. This situation will improves until 2050, but will worsen in the 2080s under the RCP8.5 scenario. Under RCP8.5, by the end of the century (2080s), the highly suitable area will decrease to 47% and the moderately suitable area will increase to 53%. [ABSTRACT FROM AUTHOR]

The selection of a navy ship is essential to guarantee a country's sovereignty, deterrence capabilities, and national security, especially in the face of possible conflicts and diplomatic instability. This paper proposes the integration of concepts related to multi-criteria decision making (MCDM) methodology and machine learning, creating the Simple Aggregation of Preferences Expressed by Ordinal Vectors—Principal Components (SAPEVO-PC) method. The proposed method proposes an evolution of the SAPEVO family, allowing the inclusion of qualitative preferences, and adds concepts from Principal Component Analysis (PCA), aiming to simplify the decision-making process, maintaining precision and reliability. We carried out a case study analyzing 32 warships and ten quantitative criteria, demonstrating the practical application and effectiveness of the method. The generated rankings reflected both subjective perceptions and the quantitative performance data of each ship. This innovative integration of qualitative data with a quantitative machine learning algorithm ensures comprehensive and robust analyses, facilitating informed and strategic decisions. The results showed a high degree of consistency and reliability, with the top and bottom rankings remaining stable across different decision-makers' perspectives. This study highlights the potential of SAPEVO-PC to improve decision-making efficiency in complex, multi-criteria environments, contributing to the field of marine science. [ABSTRACT FROM AUTHOR]

Energy production, supply and consumption are global issue with many economic, environmental and social implications. Mentioned issue is even more expressed in remote rural areas, in particular in developing countries, as are the countries of the Western Balkans (WB). Renewable energy sources (RES) could represent optimal energy alternative for sustainable performing of agricultural and other activities, as well as for improving the current state of living conditions in rural communities. The main goal of research is to mark the most suitable RES alternative (six alternatives) for wider implementation in rural space of WB. The applied methodology framework implies experts' opinion (engagement of eight experts) and the use of multi-criteria decision-making methods (MCDM), (specifically fuzzy-rough LMWA and fuzzy-rough CRADIS methods) under the predefined criteria (nine criteria). Derived results show that the implementation of the solar energy plants could play an optimal solution, while as the relatively unsuitable alternative could be marked the use of energy potential of watercourses. Gained final result, i.e. ranking order of the considered alternatives is additionally verified by the appliance of other MCDM methods, while the sensitivity analysis was also performed. [ABSTRACT FROM AUTHOR]

Recommender systems are essential tools for many e-commerce services, such as Amazon, Netflix, etc. to recommend new items to users. Among various recommendation techniques, collaborative filtering has shown tremendous performance by using the rating patterns of users. Traditional collaborative filtering, matrix factorization, and deep matrix factorization are the most representative collaborative filtering techniques. However, despite their extensive utility, the selection of the method which provides better recommendation performance is still a major concern in multi-criteria recommender systems. Most recommender systems (RSs) work only on the single criterion rating, i.e., the overall rating. Single-criterion collaborative filtering (CF) generates less reliable recommendations because it suffers from correlation-based similarity problems. However, predictions based on multiple criteria have proven more accurate. This paper compares traditional collaborative filtering, matrix factorization and deep matrix factorization in recommender systems on multi-criteria datasets. We describe details of these techniques in various aspects of recommendation quality, such as how those methods handle cold-start problems, which typically happen in collaborative filtering. We performed several experiments extensively over two real-world datasets to evaluate the performance of each method in terms of qualitative and quantitative measures and observe that deep matrix factorization techniques is superior to all other techniques. [ABSTRACT FROM AUTHOR]

Multiple criteria decision-making (MCDM) has experienced significant growth in recent years, owing to its capacity to integrate even contradictory criteria. This study conducted a comprehensive literature review of MCDM for assessing, selecting, and retrofitting buildings. The bibliometric search used a search algorithm in specialized databases. A filtering and expansion process was done by reviewing references, and 91 relevant articles were selected. The analysis revealed that in a group of studies, socioeconomic criteria were used to assess the vulnerability of buildings. On the other hand, some research integrated the three dimensions of sustainability (economic, social, and environmental) along with safety considerations when identifying optimal retrofit alternatives. Classic MCDMs are prevalent in research within this field. Among the most used methods, the Analytic Hierarchy Process (AHP) was employed for criteria weighting, Simple Additive Weighting (SAW) for constructing vulnerability indices, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for building retrofitting. This literature review contributes to the path toward a holistic renovation of the existing building stock, providing recommendations for future research to improve decision-making solutions for integrating the safety and sustainability of existing buildings. [ABSTRACT FROM AUTHOR]

Web‐based appointment systems are emerging in the healthcare industry with mass data, but homogenized online information and poor evaluation criteria lead to blindness in selecting doctors. To select an appropriate doctor when making appointments online, a comprehensive decision support model is proposed. First, one class of multi‐criteria is built from reviews by text mining technologies. For quantitative analysis, interval‐valued neutrosophic numbers (IVNNs) are utilized to describe reviews, and related integration operators of IVNNs are employed. Second, another class of multi‐criteria is established by the website‐given labels. A disease similarity measure‐based transformation method is proposed to redefine the doctors' specialization, making the evaluation values more discriminable. Finally, a personalized doctor ranking result is derived by integrating the two classes of multi‐criteria values with a preference parameter. A case study of Wedoctor.com is conducted to validate the proposed model, and the comparison result indicates that the model can effectively support users' decision‐making. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate the institutional and tax environment for businesses in 21 countries of the European Union (EU), using a multi-criteria analysis and, in particular, the PROMETHEE II method, based on thirteen evaluation criteria and the ranking of the European economies. Through the research, the importance of factors related to the efficiency of the tax administration, the tax capacity of the country, the growth rate of the economy, the quality of institutions, the level of corruption, the tax burden, the time of tax compliance and the political stability of the country is studied, in the formation of an attractive environment for the development of entrepreneurship in the EU economies. Moreover, the results of the analysis revealed the existence of significant fiscal and institutional differences between the EU economies under investigation, regarding the development of entrepreneurship. These differences are due to the size of the economy, the institutions, the history, the economic crises of each country and a wide range of dissimilar policy recommendations, which reflect asymmetric policy responses. The study concludes by offering theoretical and political recommendations to improve the institutional and fiscal environment in EU economies so that entrepreneurship can play an important role in improving the economic and social situation of a country. [ABSTRACT FROM AUTHOR]

Abstract Urban tourism promotes the economic growth of a nation around the year through direct and indirect incomes. In recent years, the digital economy has impacted the growth of urban tourism through hassle-free money transactions and expenditures. This article, therefore, introduces a Multi-Criteria Fuzzy-based Decision-Making Method (MCFDMM) for validating the impact of the digital economy impact over tourism. The study introduces a new framework, DLFDSS-RRM, that uses deep learning and fuzzy decision support systems for residence right management, enhancing resource allocation, security, and resident satisfaction in urban residential communities. The criteria such as expenses, positive response, and repeated payments are validated by the tourists across their travel plan. These conditions satisfying the tourist’s expectations are estimated based on their reviews of economic conditions are validated. The validation is performed against the growth of the country from urban tourism. The fuzzy process validates the growth of the country between two successive financial quarters based on the above conditions. In the condition analysis, the fuzzy process identifies the least derivatives contributing to minimal economic growth. This is reversed using the hiking condition that occurs in any quarter and hinders economic growth. Therefore, the process is validated using the metrics growth rate, condition satisfaction, analysis rate, analysis time, and unrelated assessment. The comparative analysis across various models reveals growth rates ranging from 0.263 to 0.4055, condition satisfaction percentages from 53.747 to 74.351, and analysis rates from 0.275 to 0.4662.

Multi-criteria priority ranking involves determining priorities through calculations for a multitude of tasks as per potentially conflicting criteria, to aid decision-makers in efficient allocation of resources and prioritization of tasks based on their criticality. This process is essential for optimizing resource allocation and improving overall work efficiency. Over the past two decades, the field of priority ranking has seen the development of numerous mature classic methods through continuous modifications and expansions. Furthermore, new sorting methods rooted in advancing technologies in the era of big data have also emerged. To assist decision-makers in evaluating and selecting the most suitable multi-criteria prioritization methods for priority ranking needs in specific application scenarios, this paper first elucidates differences across various multi-criteria prioritization methods regarding criteria substitutability, advantages and disadvantages, data input/output requirements, and applicable scenes. It further categorizes existing multi-criteria task prioritization methods into four main types: full aggregation methods, inferiority ranking methods, reference level methods, and data processing-based prioritization methods. Additionally, it discusses in detail the basic principles, implementation steps, latest research findings, and pros and cons of representative methods within each category. Finally, this paper summarizes current research hotspots, including the analytic hierarchy process, fuzzy theory, and AI-assisted ranking technologies, while suggesting future research pathways covering AI-assisted big data ranking optimization, ranking stability enhancement, and intelligent preference identification.

On a global scale, flooding is the most devastating natural hazard with an increasingly negative impact on humans. It is necessary to accurately detect flood-prone areas. This research introduces and evaluates the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) integrated with GIS in the field of flood susceptibility in comparison with two conventional multi-criteria decision analysis (MCDA) methods: analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The Spercheios river basin in Greece, which is a highly susceptible area, was selected as a case study. The application of these approaches and the completion of the study requires the creation of a geospatial database consisting of eight flood conditioning factors (elevation, slope, NDVI, TWI, geology, LULC, distance to river network, rainfall) and a flood inventory of flood (564 sites) and non-flood locations for validation. The weighting of the factors is based on the AHP method. The output values were imported into GIS and interpolated to map the flood susceptibility zones. The models were evaluated by area under the curve (AUC) and the statistical metrics of accuracy, root mean squared error (RMSE), and frequency ratio (FR). The PROMETHEE model is proven to be the most efficient with AUC = 97.21%. Statistical metrics confirm the superiority of PROMETHEE with 87.54% accuracy and 0.12 RMSE. The output maps revealed that the regions most prone to flooding are arable land in lowland areas with low gradients and quaternary formations. Very high susceptible zone covers approximately 15.00–19.50% of the total area and have the greatest FR values. The susceptibility maps need to be considered in the preparation of a flood risk management plan and utilized as a tool to mitigate the adverse impacts of floods. [ABSTRACT FROM AUTHOR]

The infrastructure sector is paying increasing attention to sustainability. In particular, certain events have proven how preventive local interventions can save both user lives and the same infrastructures, thus highlighting the importance of maintenance. The ineffectiveness of concrete repairs is one of the main issues in civil engineering. Merely 50% of restoration operations on concrete buildings is considered to be effective in Europe, although rehabilitation costs cover nearly half of the annual construction investments. This study investigates a potential strategy to improve the sustainability of infrastructure restoration solutions. A simplified examination of CO2 emissions, intervention costs, social factors, structural performances, and other factors considered relevant for this research, is followed by a comparison and ranking of the potential rehabilitation solutions. Four approaches have been selected to design retrofit interventions on a real column of the Sardinian Brabau bridge in Italy: i) complete column replacement; ii) substitution of the damaged longitudinal rebars with machined bars and the casting of a new concrete cover in ultra-high performance fibre-reinforced concrete; iii) longitudinal and transverse fibrereinforced polymer wrapping; iv) concrete jacketing at the base. Through the application of selected criteria, a methodological and procedural approach is developed. It provides a tool for assessing the sustainability of infrastructure maintenance works throughout the entire life cycle in the subsequent phases of this research. [ABSTRACT FROM AUTHOR]

Background: The ongoing technical innovation is fully involving transportation sector, converting the usual mass-transit system toward a sustainable mobility. Make-or-buy decision are usually adopted to assess different solutions in terms of costs-benefits to put in place strategic choices regarding in-house production or from an external supplier. This can also be reflected on maintenance operations, thus replicating a similar approach to transport companies involved. Method: A decision-making model by means of a multi-criteria analysis can lead make-or-buy choices adapted to maintenance. A brief introduction into the actual mobility context is provided, evaluating global and national trends with respect to the mobility solutions offered. Then, a focus is set on maintenance approaches in mobility sector and the need of a make-or-buy decision process is considered. The decision-making path is developed through a multi-criteria framework based on eigenvector weighing assessment, where different Key Performance Indicators (KPIs) are identified and exploited to assess the maintenance approach at stake. Results: A comparison among different scenarios considered helped in identify the solution offered to the transport operator. In particular, for the case study of interest a −35% decrease in maintenance specific cost and −44% in cost variability were found. Reliability of the fleet was kept at an acceptable level compared to the reference in-house maintenance (≥90%) while an increase in the Mean Time Between Failure was observed. Conclusions: For the purposes of a small company, the method can address the choice of outsourcing maintenance as the best. Finally, a general trend is then extrapolated from the analysis performed, in order to constitute a decision guideline. The research can benefit from further analysis to test and validate that the selected approach is effective from the perspective of transport operator. [ABSTRACT FROM AUTHOR]

Background: Healthcare 4.0 has transformed supply chain management in the healthcare sector, but there is a lack of comprehensive frameworks to evaluate the impact of Healthcare 4.0 technologies on sector operations, particularly in developing countries. Methods: This study introduces a multi-criteria framework that synergically combines the techno-economic implications of Healthcare 4.0 technologies to improve healthcare supply chain management. The proposed approach innovatively integrates fuzzy VIKOR and Entropy methods to handle data vagueness and uncertainty, using data collected from healthcare supply chain specialists in Lagos, Nigeria. Results: The developed framework identifies the most and least critical technical and economic parameters for Healthcare 4.0 implementation in healthcare supply chain management. It also determines the suitability of different Healthcare 4.0 technologies for supply chain management in the healthcare sector. Conclusions: The main innovation of this study lies in the development of a comprehensive and context-specific framework for evaluating Healthcare 4.0 technologies in healthcare supply chains. The framework offers a new perspective on technology evaluation and provides practical insights for decision-makers. The findings contribute to advancing knowledge and practice in this field, promoting the proper adoption of Healthcare 4.0 technologies in healthcare, particularly in developing countries. [ABSTRACT FROM AUTHOR]

Space technology advancements have enabled the acquisition of marine data that support the research on wave energy as an alternative to reduce fossil fuel dependency and mitigate climate change. Malaysia's ocean renewable energy potential lacks attention from local authorities due to insufficient in-situ data, posing challenges in investigating ocean characteristics, such as wave heights. This study investigated Malaysia's wave energy potential using extensive significant wave height data from multiple altimetry missions. The former assessed site suitability using the Analytical Hierarchy Process (AHP) multicriteria analysis, incorporating marine constraints, namely socioeconomic, physical, and environmental factors. The multicriteria findings were integrated into a Geographical Information System (GIS) to improve the site suitability analysis and generate a localized suitability index for wave energy. Validation of satellite altimeter data with in-situ measurements showed a strong correlation and low RMSE. AHP analysis indicated good consistency in the criteria analysis, with a consistency ratio of 0.045, which falls below the limit of 0.1. The coastal and offshore regions of the Malaysian seas are suitable for harnessing wave energy with energy ranges up to 4.21 kW/m. Therefore, this study provides valuable information to stakeholders and the government to increase their interest in wave energy. [ABSTRACT FROM AUTHOR]

Modern cities face growing challenges in terms of food, the environment, and quality of life, making the opportunity to transform urban spaces into gardens increasingly relevant. The purpose of this paper is to present and detail the system developed within the U-GARDEN project, focusing on the creation and implementation of a multi-criteria application, including real-time monitoring of environmental conditions through the integration of IoT technology. This aims at efficiently managing various types of urban gardens, facilitating a sustainable approach, and optimizing plant development in these green spaces. The results obtained demonstrate the effectiveness of the developed system, providing users with the ability to identify and efficiently manage suitable locations for creating urban gardens, as well as monitoring real-time parameters relevant to optimizing environmental conditions and plant development. The QGIS-type application, enriched with a multi-criteria formula, proves to be a scalable and adaptable tool, contributing to the promotion of sustainable urban development by involving the community in managing green spaces. The system developed within the U-GARDEN project represents not only an innovative technological solution but also a practical and efficient tool for communities and other stakeholders, offering a sustainable way to transform cities into greener and more productive spaces.

Abstract Egypt’s urban communities face many threats, including, pluvial floods, heat waves, and lack of publicly accessible urban green spaces. Nature-based solutions such as constructed wetlands (CWs) present a promising solution that could offer a wide range of ecosystem services (ES). However, the adoption of CWs is challenged by the lack of local planning guides and uncertainty about potential benefits. There are various models and tools available for quantifying and valuing ES, however, many of them are either highly complex or require extensive data and expertise. The aim of this paper is to develop a GIS-based multi-criteria decision model to select CW sites based on the supply and demand of ES. The model is to operate on three main stages: (i) demand: based on the need for risk reduction or benefit provisioning, (ii) potential sites (PSs): identify and score suitable sites for establishing a CW, and (iii) supply: define the service benefiting areas (SBA). An experimental approach is used, where the model is tested on New Damietta, an Egyptian Mediterranean city, proving the model is a reliable decision-making tool during preliminary urban planning stages due to its practicality, flexibility, and reasonable data requirements.

This paper is devoted to the study of a multi-criteria scheduling problem on unrelated processors with machines’ learning effect, with the goal of minimizing makespan, machine cost and maximal flow-time simultaneously, which is an NP-hard problem. An improved particle swarm optimization algorithm equipped with the overloaded operators, as well as a procedure of Levy flight, is proposed to generate the Pareto-optimal solutions. The experimental results show that the Levy flight strategy can effectively improve the performance of the algorithm, which can generate more non-dominated solutions, and slightly reduce the execution time of the process.

In an era where digital information is abundant, the role of recommender systems in navigating this vast landscape has become increasingly vital. This study proposes a novel deep learning-based approach integrating multi-context and multi-criteria data within a unified neural network framework. The model processes these dimensions concurrently, significantly improving the precision of personalized recommendations. Context-aware and multi-criteria recommender systems extend traditional two-dimensional user-item preference methods with context awareness and multiple criteria. In contrast to traditional methods, our approach intricately weaves together multi-context and multi-criteria data within its architecture. This concurrent processing enables sophisticated interactions between context and criteria, enhancing recommendation accuracy. While context-aware systems incorporate contextual information such as time and location when making recommendations, multi-criteria-based approaches offer a spectrum of evaluative criteria, enriching the user experience with more tailored and relevant suggestions. Although both approaches have advantages in producing more accurate and personalized referrals, context information and multi-criteria ratings have not been employed together for producing recommendations. Our research proposes a novel deep learning-based approach for the multi-context, multi-criteria recommender system to address this gap. In contrast to traditional approaches that process context-aware recommender systems and multi-criteria recommender systems separately, our deep learning model intricately weaves together multi-context and multi-criteria data within its architecture. This integration is not staged; both dimensions are concurrently processed through a unified neural network framework. The model facilitates a sophisticated interaction between context and criteria by embedding these elements into the core of the network’s multiple layers. This methodology enhances the system’s adaptability and significantly improves its precision in delivering personalized recommendations, leveraging the compounded effects of contextual and criteria-specific insights. The proposed model shows superior performance in predictive tasks, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) on the TripAdvisor and ITMRec datasets compared to other state-of-the-art recommendation techniques. Context-aware multi-criteria ratings data demonstrate the robustness and accuracy of the model.

Multiple Criteria Decision Making (MCDM) is an approach used to evaluate and select alternatives based on several criteria or factors. The purpose of this study is to apply the Multi-Attributive Border Approximation Area Comparison (MABAC) method in determining the winner of the coffee roasting competition using criteria namely fragrance, flavor, aftertaste, acidity, body, sweetness, balance, and cup to profile. With the help of the MABAC method, it will make it easier for the competition committee to determine the winner of the coffee roasting competition. The ranking results using the final results of the MABAC method for rank 1 were obtained with a final score of 8,525 on behalf of participant William, rank 2 was obtained with a final score of 5,525 on behalf of participant Firman, and rank 3 was obtained with a final value of 2,525 on behalf of participant Aditya.

Selecting the appropriate desalination and renewable energy technologies is crucial for the success of desalination projects, as each technology offers distinct advantages and disadvantages tailored to specific project requirements. This research investigates the application of both the analytic hierarchy process and fuzzy logic techniques to develop four decision-making models: two for selecting the optimal desalination technology and two for selecting the optimal renewable energy technology in coastal communities. For desalination technology selection, the analytic hierarchy process model is structured into four hierarchical levels: the main goal, criteria, sub-criteria, and alternatives. The criteria level encompasses four groups, while the sub-criteria level comprises 26 factors. The alternatives considered are reverse osmosis, electrodialysis, and multi-stage flash. In parallel, the analytic hierarchy process model for renewable energy technology selection is similarly structured, with four criteria groups and 24 sub-criteria factors. The alternatives evaluated include photovoltaic, concentrated solar power, and wind energy. Additionally, fuzzy logic models are developed for both desalination and renewable energy technology selection. These models enhance the decision-making framework by incorporating the uncertainty and vagueness that are inherent in real-world scenarios. The integration of analytic hierarchy process and fuzzy logic methodologies provide a robust approach to identifying optimal technologies, thereby supporting sustainable development in Egypt’s water–energy nexus. The research outcomes highlight the effectiveness of integrating analytic hierarchy process and fuzzy logic in decision-making processes, offering decision-makers systematic and reliable approaches for selecting the most suitable technologies to achieve sustainability in water–energy nexus projects. The results of the research indicate that the best alternative for desalination was reverse osmosis, and for renewable energy was photovoltaics.

Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations.

Diversification of smallholder rice-based cropping systems has the potential to increase cropping system intensity and boost food security. However, impacts on resource use efficiencies (e.g., nutrients, energy, and labor) remain poorly understood, highlighting the need to quantify synergies and trade-offs among different sustainability indicators under on-farm conditions. In southern coastal Bangladesh, aman season rice is characterized by low inputs and low productivity. We evaluated the farm-level impacts of cropping system intensification (adding irrigated boro season rice) and diversification (adding chili, groundnut, mungbean, or lathyrus) on seven performance indicators (rice equivalent yield, energy efficiency, partial nitrogen productivity, partial potassium productivity, partial greenhouse gas footprint, benefit-cost ratio, and hired labor energy productivity) based on a comprehensive survey of 501 households. Indicators were combined into a multi-criteria performance index, and their scope for improvement was calculated by comparing an individual farmer's performance to top-performing farmers (highest 20%). Results indicate that the baseline system (single-crop aman season rice) was the least productive, while double cropped systems increased rice equivalent yield 72-217%. Despite gains in productivity, higher cropping intensity reduced resource use efficiencies due to higher inputs of fertilizer and energy, which also increased production costs, particularly for boro season rice. However, trade-offs were smaller for diversified systems including legumes, largely owing to lower N fertilizer inputs. Aman season rice had the highest multi-criteria performance index, followed by systems with mungbean and lathyrus, indicating the latter are promising options to boost food production and profitability without compromising sustainability. Large gaps between individual and top-performing farmers existed for each indicator, suggesting significant scope for improvement. By targeting indicators contributing most to the multi-criteria performance index (partial nitrogen productivity, energy efficiency, hired labor energy productivity), results suggest further sustainability gains can be achieved through future field research studies focused on optimizing management within diversified systems.Supplementary informationThe online version contains supplementary material available at 10.1007/s13593-022-00795-3.

In recent times, recommender systems have acquired significant popularity as a solution to the issue of information overload. These systems offer personalised recommendations to users. The superiority of multi-criteria recommender systems over their single-criterion counterparts has been demonstrated, as the former are able to provide more precise recommendations by taking into account multiple dimensions of user preferences when rating items. The prevalent recommendation technique, matrix factorization of collaborative filtering, is hindered by the data sparsity problem of the user-item matrix. On the other hand, it is noteworthy that deep learning techniques have demonstrated significant potential in various research domains, including but not limited to image processing, pattern recognition, computer vision, and natural language processing. In recent times, there has been a surge in the utilisation of deep learning techniques in recommender systems, yielding promising outcomes. This study presents a novel approach to multi-criteria recommender systems through the utilisation of deep learning algorithms to mitigate the data sparsity issue. Specifically, deep autoencoders are utilised to uncover complex, non-linear, and latent relationships between users' multi-criteria preferences followed by matrix factorization technique, ultimately leading to more precise recommendations. The proposed model is evaluated by conducting the experiments on the multi-criteria dataset of Yahoo! Movies. According to the outcomes, the proposed approach outperforms the state of the art recommendation methods by generating more accurate and personalized recommendations. Also, it reduces the data sparsity up to 11% from the multi-criteria dataset. [ABSTRACT FROM AUTHOR]

Water resources play a crucial role in economic development, human health, and biodiversity. Mismanagement of water resources can lead to various disasters, with floods being one of the most frequent occurrences in river watershed. The rapid advancement of geospatial information systems enables the early prediction of flood hazard zones in the watershed by utilizing information about natural resources. This research focuses on determining flood hazard zones in the Tuntang Watershed through a combination of remote sensing, Geographic Information Systems (GIS), and the Analytic Hierarchy Process (AHP). Nine parameters are used to describe aquifer recharge zones, and each parameter is weighted according to its water characteristics and potential, determined through the AHP method based on expert opinions gathered in a Focus Group Discussion (FGD). The priority order obtained through AHP is as follows: precipitation, distance to the river, Land Use Land Cover (LULC), slope, Normalized Difference Vegetation Index (NDVI), elevation, curvature, Topographic Wetness Index (TWI), and soil type. The final flood hazard zone map is divided into five categories: very low, low, moderate, high, and very high. The results indicate that certain areas are very hazardous and present flood prone areas, namely Grobogan District with an area of 102.17 km2 (8.59 %), Demak with an area of 49.75 km2 (4.18%), and Semarang with an area of 45.23 km2 (3.80 %). [ABSTRACT FROM AUTHOR]

Multi-criteria recommender systems provide efficient solutions to deal with information overload problem by producing personalized recommendations considering multiple criteria. Even though multicriteria recommender systems provide more accurate and personalized recommendations to their users compared with traditional recommender systems, sparsity becomes a major problem for such systems due to the increasing number of criteria. Due to the lack of corated items among users, finding out neighbors and producing accurate predictions become harder. Especially similarity-based multi-criteria recommendation approaches are significantly affected by the sparsity problem. Thus, aiming to minimize the negative impacts of that problem, a hybrid similarity-based multi-criteria recommendation method, that utilizes complex, low-dimensional and latent features obtained from both reviews and criteria ratings by autoencoders, is proposed in this work. The empirical results performed on a real data set with a sparsity percentage of 99.7235% show that the proposed work can provide more accurate predictions compared with other neighborhood-based multi-criteria approaches. [ABSTRACT FROM AUTHOR]

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Software component selection is critical in software engineering due to its vital role in reducing software development cost and time. This study analyzes software component selection research studies on methodologies, criteria, and multi-criteria decision-making (MCDM) techniques. The key study findings are: first, comprehensive standardized criteria for software component selection are lacking, with ambiguous terminology used in research. Second, current ad hoc selection processes need streamlining to reduce time, cost, and effort. Thus, an integrated approach is required to aid decision-makers. The review suggests developing automated tools or decision support systems combining multiple criteria decision methods to improve selection accuracy and efficiency. Standardized criteria catalogs can also assist software developers in the evaluation. The findings highlight that despite extensive academic research, component selection in practice remains sub-optimal. By informing future research and tool development, this review can benefit practitioners to systematically select the most appropriate software components meeting software requirements. [ABSTRACT FROM AUTHOR]

Personnel evaluation and selection is an essential part of modern business. Appropriate candidate selection can significantly contribute to companies in terms of increased profit, good culture, reputation, reduced costs, etc. This paper addresses the personnel evaluation and selection problem at the University of Pardubice, Faculty of Transport Engineering (UPCE). Since this is a typical ranking alternative problem where multiple criteria affect the decision, the Z-numbers-based Alternative Ranking Order Method Accounting for the two-step Normalization (AROMAN) is applied. Four Ph.D. candidates are assessed, and the most appropriate is selected to be employed by the UPCE. The Z-numbers fuzzy AROMAN method ranks Ph.D. candidate number four as the most appropriate alternative. To investigate the stability and sensitivity of the Z-numbers fuzzy AROMAN method, the values of parameters β and λ used in the mathematical calculations of the method were changed. The results of sensitivity analysis revealed that the obtained solution is stable. To confirm the robustness of the proposed approach, a comparative analysis is performed. Simple Additive Weighting (SAW), Weighted Product Model (WPM), and Z-number fuzzy TOPSIS were applied. Besides, we applied the fuzzy inferior ratio method as well. The results confirm the high robustness of the proposed Z-numbers fuzzy AROMAN method. [ABSTRACT FROM AUTHOR]

In group decision making (GDM), there may exist some problems that need to assign alternatives to some predefined ordered categories, which are called ordinal classification-based GDM problems. To obtain classification results that can be accepted by most decision makers (DMs), it is necessary to implement a consensus reaching process for ordinal classification-based GDM problems. In this paper, we study consensus reaching models for a new type of ordinal classification-based GDM problem, in which DMs do not provide criteria weights and category cardinalities but provide indirect and imprecise heterogeneous preference information. To do so, a consistency verification method is first proposed to check whether each DM's preference information is consistent and then a minimum adjustment optimization model is developed to modify DMs' inconsistent preference information. Afterwards, we establish some optimization models to obtain each DM's possible categories for alternatives. Followed by this, we define the consensus levels of DMs and devise some optimization models to assist DMs in adjusting alternatives' classification results and DMs' preference information at the same time. Furthermore, a maximum support degree-based method is provided to determine the consensual classification result for alternatives. Finally, a numerical application and some sensitivity analysis are provided to justify the proposed models. [ABSTRACT FROM AUTHOR]