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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]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]

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]

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]

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]

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]

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]

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. [ABSTRACT FROM AUTHOR]

Copyright of Fides et Ratio is the property of Universidad La Salle de Bolivia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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. [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]

Following the rapid increase in flood events in recent years, flood risk assessment and mitigation have become a necessity. The purpose of this study is to develop updated and accurate flood risk and hazard maps in the El-Ham watershed within the large Hodna basin (Algeria). In order to achieve this objective and to select weights of contributing criteria to flood risk, geographical information system and multi-criteria analysis along with the application of analytical hierarchy process technique have been used. Factors considered to be the cause of flooding are slope, annual maximum daily precipitation, drainage density, elevation, land use/cover and soil type. In addition to the hazard, population density has been also used to determine the risk of flooding in El-Ham watershed. The percentages of weights obtained for each parameter are 39% for slope, 25% for drainage density, 18% for rainfall, 9% for elevation, 6% for land use/cover and 3% for soil type. For validation of these weights, a consistency ratio is then calculated. Main outcomes present a high flood potential, especially in the North-Eastern, North-Western and Central part of the study area. However, this high potential decreases further when one goes toward the South. This research suggests that the chosen criteria are sufficient to obtain a reliable and simplified flood risk map. Any additional parameters would add more details to the risk map using hydraulic and/or hydrological models to generate flood hazard maps such as RRI and HEC-RAS models. [ABSTRACT FROM AUTHOR]

Copyright of Revista EIA is the property of Escuela de Ingenieria de Antioquia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a generic component for Analytic Hierarchy Process (AHP)-based decision support in risk management. The component was originally dedicated to railway transportation issues; however, its generality enabled it to extend its functionality for other domains too. To show the generality of the module and possibility of its application in other domains, an environmental case was run. Its goal was to select methods for planning the post-mining heap revitalization process, especially decision-making focusing on the selection of the most advantageous revitalization option on the basis of the Analytic Hierarchy Process and different, non-financial factors, e.g., social, environmental, technological, political, etc. Taking into account expert responses, the suggested solution was related to energy production. [ABSTRACT FROM AUTHOR]

The method of pairwise comparisons is frequently applied for ranking purposes. This article aims to rank top women tennis players based on their win/lose ratios. Incomplete pairwise comparison matrices (PCMs) were constructed from data obtained from the WTA (Women's Tennis Association) homepage. The database contains head-to-head results from the period between 1973 and 2022 for 28 players who had the position No. 1 in the official rankings of WTA. The weight vector was calculated from the incomplete PCM with the logarithmic least squares method and the eigenvector method. The results are not surprising: Serena Williams, Steffi Graf, and Martina Navratilova stand in the first three positions, and Martina Hingis, Kim Clijsters, and Justine Henin follow them. We also tested the frequently used probability-based Bradley-Terry method and found high rank-correlation values. Using graph representations, the results gave us a deeper insight into the properties of incomplete PCMs. Special attention was given to the nontransitive triads. A data modification was necessary to remove ties in order to apply the commonly used tests. The results indicate that ordinally nontransitive triads are not significant in the data we analysed. [ABSTRACT FROM AUTHOR]

Portfolio selection for Stock evaluation and selection by an investor is multi-criteria decision-making (MCDM) problem of finding the best portfolio among an efficient set of portfolios, which should be tackled by using the appropriate techniques. The Analytic Hierarchy Process (AHP) is amongst the most widely used MCDM methods, which is often used in operation management. The main feature of AHP is, that, it firstly reduces the complex decision problems in hierarchical structures of objectives, criteria, sub-criteria, and alternatives and then, uses a fundamental scale to construct pairwise comparisons. The Spherical Fuzzy Sets (SFS) have many advantages in handling the uncertainty and vagueness of ordinary and 2D Fuzzy Sets (FS)s. Therefore, taking the advantage of AHP and SFSs in this paper, we modify the Fuzzy Analytic Hierarchy Process (FAHP) into Spherical fuzzy AHP (SFAHP). We introduce the concept of Spherical Fuzzy Preference Relation (SFPR) and develop an automatic algorithm to construct a consistent SFPR from an inconsistent one. The validity of the proposed approach is tested through an illustrative application of portfolio selection on the Pakistan Stock Exchange to make a prototype of our results. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid expansion of internet technology, the number of online food delivery services has grown. Despite the ease, a poor online food delivery platform (OFP) produces all kinds of problems for individuals. A comprehensive and reasonable evaluation of online food delivery is of great significance. Therefore, this study establishes an OFP evaluation model based on the probabilistic double hierarchy linguistic EDAS (PDHL-EDAS) method for multiple attribute group decision-making (MAGDM). Then the CRITIC model is introduced to derive the objective weight and the cumulative prospect theory is leaded to obtaining the cumulative weight of PDHLTS. In furthermore, the benefits and applicability of the PDHL-EDAS method are illustrated by solving practical MAGDM problems concerning the evaluation of OFPs. Lastly, the proposed method is verified by comparison with other MAGDM methods, in addition to discussing potential future research directions. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Landslides occur when masses of rock, soil, or debris move down slopes due to various factors, such as heavy rainfall, earthquakes, or human activities, and they are among the most destructive hazards in the hilly regions posing serious threats to life and property. Identifying landslide prone areas is an essential step in minimizing losses and preparing mitigation plans. Therefore, in the present study, the landslide susceptibility mapping was conducted along Rishikesh–Badrinath national highway, using Analytical Hierarchy Process (AHP) method of Multi-Criteria Decision-Making (MCDM). Prior to performing landslide susceptibility mapping, a landslide inventory was prepared using high-resolution Sentinel-2 satellite imagery along with Google Earth image. In all total 156 landslides adjacent to the highway were identified and mapped in 2022. Afterwards, relative weights were assigned to a host of nine controlling and triggering factors using AHP. Subsequently, each factor was reclassified and converted to thematic layer and finally overlaid in the GIS environment for the generation of landslide susceptibility map. Furthermore, the generated landslide susceptibility map was validated with the landslide inventory of the region. Consequently, the validation revealed an AUC value of 0.81. Later, the created landslide susceptibility map was classified in five sub-classes of very low, low, moderate, high, and very high landslide susceptible zone. About 27% of the area was found to be highly landslide susceptible. This section is characterized with high slope and rainfall. The current study offers a comprehensive identification of landslide susceptible zones along Rishikesh–Badrinath national highway. The landslide susceptible zone map of this area may help in the preparedness and development of various mitigation plans for making disaster governance better. Additionally, landslide susceptibility map could be used as a guideline for the formulation of disaster management plan to avoid loss and reduce risk to landslide by the concerned authority. Moreover, this information can be used to raise awareness among the local people about the risk associated with the area. [ABSTRACT FROM AUTHOR]

A recommendation system (RS) can help overcome information overload issues by offering personalized predictions for users. Typically, RS considers the overall ratings of users on items to generate recommendations for them. However, users may consider several aspects when evaluating items. Hence, a multi-criteria RS considers n-aspects of items to generate more accurate recommendations than a single-criteria RS. This research paper proposes two deep encoder–decoder models based on shared learning for a multi-criteria RS, multi-modal deep encoder–decoder-based shared learning (MMEDSL) and multi-criteria deep encoder–decoder-based shared learning (MCEDSL). MMEDSL employs the shared learning technique by concentrating on the multi-modality concept in deep learning, while MCEDSL focuses on the training process to apply the shared learning technique. The shared learning captures useful shared information during the learning process since the multi-criteria may have hidden inter-relationships. A set of experiments were conducted to compare the proposed models with recent baseline approaches. The Yahoo! Movies multi-criteria dataset was utilized. The results demonstrate that the proposed models outperform other algorithms. In addition, the results show that integrating the shared learning technique with the RS produces precise recommendation predictions. [ABSTRACT FROM AUTHOR]

The automotive component companies needed to improve their quality to meet global standards continuously. A key success factor in achieving this process is enhancing the system's purchasing activities of vendor selection. SA Indonesia is a company that carries out business in automotive components for vendors chosen by weighting and assessing multi-criteria decision-making (MCDM). However, the company faces the lack of ability to consider interconnections between criteria that the firm values and other factors, such as price, delivery, flexibility, quality, and services. It is also important for companies to possess adequate vendor capability for sustainability and safety-health management, enabling them to determine the selection criteria. These attributes are rare in preliminary studies; therefore, companies needed utilize proper criteria to get the best vendors. Furthermore, this research integrates the AHP-ANP method to tackle the problem of selecting a vendor, using the ANP method assisted by a super decision (SD) software application. The results showed that the highest vendor selection criterion is price, while the lowest is safety. In addition, based on the analysis conducted on five vendor alternatives assessed using the ANP method, the highest vendor rank is JFESS Indonesia. Future studies can be developed by including information technology (IT) security in the selection of vendor attributes, bearing in mind the challenges ahead with the progress of developing a procurement integration system in a procurement management system, based on enterprise resource planning (ERP), in supporting digitalization in the manufacturing 4.0 era. But with an overall secure enterprise data security design. [ABSTRACT FROM AUTHOR]

Desertification assessment is vital to develop actions plans aimed to prevent desertification issues. Utilizing Geographic Information Systems (GIS) to manipulate and analyze data retrieved from various sources of data including satellite images and thematic maps, as well as the use of remote sensing through satellite images to retrieve environmental information on the earth's surface, are crucial tools to understand related environmental dangers, which is used to monitor desertification and investigate different desert conditions. In this study, different physical and environmental factors associated with land desertification including vegetation cover, water, drainage systems, geomorphology, and soil moisture, were extracted and evaluated using various remote sensing techniques. In addition, multi-temporal analysis of Landsat TM data is used in this study to examine the state of vegetation and growth of irrigated area between 1983 and 1997. A GIS tool is utilized to aggregate and analyze different factors including soil, land use, and climate. A decrease in vegetation cover was clearly observed based on these analysis results. A severe desertification of the natural vegetation cover is the primary cause of this predicament. Finally, many maps describing the actual situation of study area is obtained following the GIS layers examination, however, the most important maps are soil erosion, vegetation degradation, salinization and desertification. [ABSTRACT FROM AUTHOR]

Due to the environmental policy adopted by the European Union (EU), EU countries are obliged to reduce greenhouse gas emissions. They reduce emissions largely through the energy transformation and switching to renewable energy sources (RES). Therefore, it is important to assess the progress of the energy transformation of individual EU countries. This is related to the aim of the article, which is a temporal analysis of the energy transformation process towards the transition to RES and reducing the use of fossil fuels in energy production. To achieve this goal, a new Temporal/Dynamic Multi-Criteria Decision-Making (T/DMCDM) method called Temporal PROSA was developed, based on the PROMETHEE and PROSA family of methods. The Temporal PROSA method, unlike many other T/DMCDM methods, enables the aggregation of data from many periods into a single final assessment, as well as the direct transfer of information from the examined periods to the overall result. As a result of the research, EU countries that dominated in terms of progress in energy transformation towards RES in the years 2004–2021were identified. Based on the data and methodology used, it was indicated that these countries are primarily Sweden and Portugal, and recently also Denmark and Finland. On the other hand, countries such as Belgium, Bulgaria, Cyprus, Luxembourg, and Poland made the least progress between 2004 and 2021. [ABSTRACT FROM AUTHOR]

Background an Objectives Alfalfa plant with the scientific name Medicago sativa L is considered the most important fodder plant in the world and it is a very high quality fodder suitable for all kinds of livestock. Alfalfa fodder plant is known as the queen of fodder plants in terms of nutritional value and palatability due to the variety of species compared to other fodder plants. In Iran, the area under alfalfa cultivation is 340,767 hectares and its production amount is 3,551,850 tons, and in Ardabil Province, the area under alfalfa cultivation is 9,065 hectares with annual production of 63,105 tons. Examining weather data and their effect on plants is one of the most important factors in increasing productivity. Therefore, each area has potential and limitations regarding crop cultivation that is compatible with a specific climate. The main goal of this research is to locate alfalfa fodder cultivation in Ardabil Province using water and meteorological criteria during alfalfa growth period. The difference between the present research and other studies in this field is that it addresses the efficiency of four methods during the alfalfa fodder growth period. Methodology Ardabil Province is located in the northwest of Iran, and its location is in the latitude of 37 degrees and 45 minutes to 39 degrees and 42 minutes of north latitude and the geographical longitude of 47 degrees and 3 minutes to 48 degrees and 55 minutes of east longitude. In this research, the data of annual precipitation, average annual temperature, minimum annual temperature and maximum annual temperature from six synoptic stations during the statistical period (1990 to 2020) and the height and slope of the land, as well as software; ARC GIS, Export Choice and Super Decision have been used. In order to weight and locate alfalfa crops, the methods of Analytical Hierarchy Process (AHP), Analytical Network Model (ANP), Fuzzy Hierarchical Process (FAHP) and Weighted Method (WLC) have been used. Findings In the AHP method, the results with Export Choice software analysis showed that annual precipitation with a weight of 0.377, average temperature with a weight of 0.258, and maximum temperature with a weight of 0.112 were recognized as the most important criteria during the alfalfa growth period. The results of the FAHP method showed that precipitation with a weight of 0.367, average temperature with a weight of 0.259, and minimum temperature with a weight of 0.105 are effective in the stages of alfalfa fodder cultivation. In the ANP method with the analysis of Super Dicision software, the results showed that temperature criteria (average, minimum and maximum) with a weight of 0.404, annual precipitation with a weight of 0.289 and topography (height and slope) with a weight of 0.056 are the most important effective parameters, respectively. During the growth period, fodder is alfalfa. By combining the studied criteria during the growth period of alfalfa fodder with the WLC method in the GIS environment, the location of alfalfa cultivation in Ardabil Province was done. Conclusion The results showed that in all studied methods, precipitation has the most important effect during the alfalfa growth period; but only 3% of the area of the province is suitable for alfalfa cultivation in terms of rainfall, so rainfall is the most limiting criterion for alfalfa cultivation in Ardabil province. The results of weight criteria of AHP, FAHP, ANP methods showed that precipitation, average temperature and minimum temperature with the highest weight are respectively the most important influencing parameters in alfalfa fodder cultivation. The results of combining the criteria with the WLC method in the GIS environment showed that the arable lands of Ardabil Province have very suitable (22%), suitable (31%), slightly suitable (23%) and unsuitable (24%) for alfalfa fodder cultivation. [ABSTRACT FROM AUTHOR]

Context: The selection and use of appropriate multi-criteria decision making (MCDM) methods for solving complex problems is one of the challenging issues faced by decision makers in the search for appropriate decisions. To address these challenges, MCDM methods have effectively been used in the areas of ICT, farming, business, and trade, for example. This study explores the integration of machine learning and MCDM methods, which has been used effectively in diverse application areas. Objective: The objective of the research is to critically analyze state-of-the-art research methods used in intelligent decision support systems and to further identify their application areas, the significance of decision support systems, and the methods, approaches, frameworks, or algorithms exploited to solve complex problems. The study provides insights for early-stage researchers to design more intelligent and cost-effective solutions for solving problems in various application domains. Method: To achieve the objective, literature from the years 2015 to early 2020 was searched and considered in the study based on quality assessment criteria. The selected relevant literature was studied to respond to the research questions proposed in this study. To find answers to the research questions, pertinent literature was analyzed to identify the application domains where decision support systems are exploited, the impact and significance of the contributions, and the algorithms, methods, and techniques which are exploited in various domains to solve decision-making problems. Results: Results of the study show that decision support systems are widely used as useful decision-making tools in various application domains. The research has collectively studied machine learning, artificial intelligence, and multi-criteria decision-making models used to provide efficient solutions to complex decision-making problems. In addition, the study delivers detailed insights into the use of AI, ML and MCDM methods to the early-stage researchers to start their research in the right direction and provide them with a clear roadmap of research. Hence, the development of Intelligent Decision Support Systems (IDSS) using machine learning (ML) and multicriteria decision-making (MCDM) can assist researchers to design and develop better decision support systems. These findings can help researchers in designing more robust, efficient, and effective multicriteria-based decision models, frameworks, techniques, and integrated solutions. [ABSTRACT FROM AUTHOR]

We use multi-objective optimization and numerical simulations to optimize the shear strength of a reinforced concrete T-beam. The optimization process involves four factors that are related to the Young’s modulus of elasticity and density of both the steel reinforcement and the concrete. The factors, which have a limited range, are utilized in the construction of the regression equation that forecasts the reinforced concrete T-beam’s ductility and elastic shear strain. Using ABAQUS finite element programs, 27 models were prepared for numerical analysis and simulation using the well-known sampling technique Box-Behnken design. To find the coefficients that correspond to the regression equations, MATLAB codes are utilized to solve complex matrices using the least squares method. Checking the regression equation's reliability to compare the outcomes of the numerical simulations and the regression equations, a reliability check for the regression equation has been implemented. Due to the simultaneous R² values of 1 and 1 for ductility and elastic shear strain, the reliability check was 100%. The optimization of the reinforced concrete T-beam’s shear strength capacity can be easily determined, according to multi-objective optimization results, and the design of this structural system is highly controllable. [ABSTRACT FROM AUTHOR]

In this era of vast online healthcare information, patients often find it challenging to choose the most appropriate doctors from a myriad of options available on different communities. The sheer volume of choices can be overwhelming, underscoring the importance of personalized doctor recommendations. Unfortunately, many existing communities rely on generic doctor recommendations through a global ranking system, potentially neglecting the unique needs and preferences of individual patients. To address these challenges, this study introduces a novel multicriteria user-item trust-enhanced collaborative filtering (MCUITeCF) approach, specifically designed to facilitate patients in locating doctors who match their unique preferences. This proposed approach capitalizes on multi-criteria ratings and integrates user-item trust relationships, aiming to enhance the quality of recommendations, while tackling the common issues of data sparsity and the cold-start problem. An examination of the proposed approach, using a realworld healthcare multi-criteria dataset, the RateMDs dataset, reveals its effectiveness in overcoming challenges associated with data sparsity and cold-start problems. The results demonstrate improved prediction accuracy and coverage compared to benchmark approaches, namely MC user-based CF, MC item-based CF, MC semantic-based CF, MC trust-based CF, and trust-semantic enhanced MC CF. Specifically, the results indicate that the MCUITeCF approach improves the average MAE by 66% compared to all of the benchmark approaches when tested on the RateMDs dataset. When dealing with data sparsity, the MCUITeCF approach improves the average MAE by 41% and prediction coverage by 23% compared to all of the benchmark approaches. In scenarios involving cold-start items, the MCUITeCF outperformed specific benchmark methods such as MC item-based CF, MC semantic-based CF, and Trust-Semantic enhanced MC CF, registering a 21% drop in average MAE and a 28% rise in prediction coverage. Similarly, for cold-start user situations, MCUITeCF excelled by decreasing the average MAE by 29% and a substantial increase in prediction coverage by 20% compared to MC user-based CF, MC trust-based CF, and trust-semantic enhanced MC CF benchmark approaches. [ABSTRACT FROM AUTHOR]

The tension between city logistics and its impact on sustainable urban development is evident. Often, local environmental decisions overlook the effects on urban freight logistics, lacking consideration for stakeholders. To address this, utilizing multi-criteria analysis becomes relevant for informed urban planning and management decision making. In this context, this paper conducts a systematic literature review from 2012 to 2022, focused on implementing the multi-criteria analysis methodology to evaluate alternatives for solutions in urban freight logistics. The PRISMA tool was used in the review to select publications and categorize the information obtained to address the research questions. Results display the most prominent authors and publications, authors' country affiliations, annual publication frequency, research objectives, used frameworks, involved actors, defined evaluation criteria, types of alternatives for solutions considered, and MCDM methods applied. The main finding is that the most commonly used MCDM methods were AHP hybrid followed by MAMCA. In addition, no clear correlation between the pursued objectives and the MCDM methods employed by the researchers is identified. It is important to note that all publications with the highest number of citations use fuzzy methods in their analyses. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multi-criteria decision-making (MCDM) is one of the main decision-making problems which aims to determine the best alternative by considering more than one criterion in the selection process. MCDM has manifold tools and methods that can be applied in different fields from finance to engineering design. This scientific article presented a multi-criteria decision-making model for selecting supplier proposals. The developed model is based on the analysis of several criteria that may influence the choice of supplier. The criteria were classified based on their impact on the organization's business processes. The study showed that the model allows accurate and reliable decisions in this area. [ABSTRACT FROM AUTHOR]

Recommendation systems help customers to find interesting and valuable resources in the internet services. Their priority is to create and examine users' individual profiles, which contain their preferences, and then update their profile content with additional features to finally increase the users' satisfaction. Specific characteristics or descriptions and reviews of the items to recommend also play a significant part in identifying the preferences. However, inferring the user's interest from his activities is a challenging task. Hence it is crucial to identify the interests of the user without the intervention of the user. This work elucidates the effectiveness of textual content together with metadata and explicit ratings in boosting collaborative techniques. In order to infer user's preferences, metadata content information is boosted with user-features and item-features extracted from the text reviews using sentiment analysis by Vader lexicon-based approach. Before doing sentiment analysis, ironic and sarcastic reviews are removed for better performance since those reviews inverse the polarity of sentiments. Amazon product dataset is used for the analysis. From the text reviews, we identified the reasons that would have led the user to the overall rating given by him, referred to as features of interest (FoI). FoI are formulated as multi-criteria and the ratings for multiple criteria are computed from the single rating given by the user. Multi-Criteria-based Content Boosted Hybrid Filtering techniques (MCCBHF) are devised to analyze the user preferences from their review texts and the ratings. This technique is used to enhance various collaborative filtering methods and the enhanced proposed MCKNN, MCEMF, MCTFM, MCFM techniques provide better personalized product recommendations to users. In the proposed MCCBHF algorithms, MCFM yields better results with the least RMSE value of 1.03 when compared to other algorithms. [ABSTRACT FROM AUTHOR]

Routing and parking problems are considered a real challenge in the populated cities. An intelligent routing and parking algorithm is needed to facilitate locating the most appropriate parking spots to vehicles in a way that efficiently utilizes parking resources, reduces the traffic congestion, and minimizes trip driving time. This work proposes a hierarchical optimal algorithm to solve the routing and parking problem (HOPRA) using multi-criteria decision concept and historical traffic data. The problem is divided into three parts: finding the preferred parking lot, identifying the parking entrance (in case there are multiple), and then selecting the preferred route to the destination point. The optimal parking lot is defined based on an objective function that depends on the availability rates of the parking lots. After finding the parking lot and the parking entrance, a multi-objective function is used to specify the preferred route to the selected parking lot. This function depends on the travel distance, the traffic rate of a specific route, and the driver's preferences. We use the Open Street Maps from the AnyLogic software in the implementation stage. We compare HOPRA against the Shortest Route (SR) algorithm that was implemented also in AnyLogic with historical traffic data obtained from HERE technologies for a sample city (Al-Madinah City in Saudi Arabia). The results show that the HORPA algorithm outperforms the SR algorithm in balancing the traffic congestion on the roads and reducing the driving time of the trip. [ABSTRACT FROM AUTHOR]

Copyright of Water Conservancy Science & Techonlogy & Economy is the property of Water Conservancy Science & Technology & Economy Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)