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Decision makers are often interested in assigning alternatives to preference classes under multiple criteria instead of choosing the best alternative or ranking all the alternatives. Firms need to categorize suppliers based on performance, credit agencies need to classify customers according to their risks, and graduate programs need to decide who to admit. In this article, we develop an interactive Bayesian algorithm to aid a decision maker (DM) with a multicriteria sorting problem by learning about her preferences and using that knowledge to sort alternatives. We assume the DM has a linear value function and value thresholds for preference classes. Our method specifies an informative prior distribution on the uncertain parameters. At each stage of the process, we compare the expected cost of stopping with the expected cost of continuing to consult the DM. If it is optimal to continue, we select an alternative to present to the DM and, given the DM's response, we update the prior distribution using Bayes' Theorem. The goal of the algorithm is to minimize expected total cost. We develop lower bounds on the optimal cost and study the performance of a heuristic policy that presents the DM alternatives with the highest expected cost of misplacement. [ABSTRACT FROM AUTHOR]

The recent PROMETHEE‐Scoring approach offers a method for generating cardinal scores through an outranking procedure utilizing Stochastic Multicriteria Acceptability and σ$$ \sigma $$–μ$$ \mu $$ analysis. In this paper, we illustrate some undesirable effects of this method and explore possible approaches to address them. First, we modify the original LP by inserting a new constraint that limits negative impacts caused by alternatives with low flow mean and standard deviation. Also, clustering is used to identify outliers in the decision matrix that may induce undesirable effects. A toy example demonstrates the issues and explores the proposed implementations. In addition, a numerical application is presented where a set of countries is ordered based on criteria from an economic freedom assessment. Both the numerical application and the toy example show that the propositions can improve the consistency of the results and prevent inconsistent outputs. The results include comparisons between the rank of alternatives when using purely the PROMETHEE‐Scoring method, the proposed improvements, and the expected results from SMAA‐PROMETHEE. [ABSTRACT FROM AUTHOR]

Creating routes across open areas is challenging due to the absence of a defined routing network and the complexity of the environment, in which multiple criteria may affect route choice. In the context of urban environments, research has found Visibility and Spider-Grid subgraphs to be effective approaches that generate realistic routes. However, the case studies presented typically focus on plazas or parks with defined entry and exit points; little work has been carried out to date on creating routes across open areas in rural settings, which are complex environments with varying terrain and obstacles and undefined entry or exit points. To address this gap, this study proposes a method for routing across open areas based on a Spider-Grid subgraph using queen contiguity. The method leverages a Weighted Sum–Dijkstra's algorithm to allow multiple criteria such as surface condition, total time, and gradient to be considered when creating routes. The method is tested on the problem of routing across two areas of Dartmoor National Park, United Kingdom. The generated routes are compared with benchmark algorithms and real paths created by users of the Ordnance Survey's Maps App. The generated routes are found to be more realistic than those of the benchmark methods and closer to the real paths. Furthermore, the routes are able to bypass hazards and obstacles while still providing realistic and flexible routes to the user. [ABSTRACT FROM AUTHOR]

Finding all the "gifted" students who would benefit from a gifted and talented service is a perpetual concern. In this article, we focus on how to effectively implement multiple criteria in identification. First, we provide some broad background before introducing three different ways to combine multiple data points (AND, OR, and MEAN) when identifying students for gifted services. Next, we discuss how effective use of combining multiple criteria—including using two-phase identification systems—contributes to schools saving time and money while also better identifying students. To do this, we use newly introduced criteria for evaluating gifted and talented identification systems. Finally, we provide several keys for success that can help schools accomplish their identification goals effectively. [ABSTRACT FROM AUTHOR]

Consuming too much fat, sugar, and salt is associated with adverse health outcomes. Food reformulation is one possible strategy to enhance the food environment by improving the nutritional quality of commercial products. However, food reformulation faces many hindrances. One way to alleviate some of these hindrances is to embrace a multicriteria approach that is based on a market inventory. In this objective, additional sensory screening and water content analyses allow going beyond nutrition and composition information on the packaging. However, due to feasibility reasons for later in-depth analyses, it is necessary to work with several reduced and manageable products. To the best of the authors' knowledge, in the literature, there is no sample selection approach taking into account multiple criteria as a base for future food reformulation. The overall aim of this paper is to propose a method to select the best representative products from the market base, for future reformulation by going beyond nutrition and composition information on the packaging. This approach considered therefore nutrition, composition, economic, water content, and sensory information with the example of the cookies market. The first step is the creation of an extensive cookie database including sensory and water content information. In total 178 cookies among the French market were identified, then focus was placed on 62 chocolate chip cookies only. Sensory screening and water content analyses of all 62 products were conducted. The second step is to make an informed subset selection, thanks to a cluster analysis based on 11 nutrition, composition, and water content variables. A representative subset of 18 cookies could be derived from the obtained clusters. The representativity was evaluated with statistical uni- and multivariate analyses. Results showed a broad variety of chocolate chips cookies with a large nutritional, compositional, water content, and sensory differences. These results highlighted the first paths for future reformulation in this product category and showed the importance to include physical product information beyond the information on the packaging. This complete database on the selected cookies constituted a solid base for identifying future reformulation levers, in order to improve the nutritional quality and health.

The relevance of the study is due to the difficulties inherent in the selection of tools designed for effective project management in the financial sector, as well as the increasing competition in the financial services market. Within the framework of this article, the features of the choice of a project management tool in the financial sector based on the use of quantitative methods that make the decision-making process significantly more meaningful and effective are disclosed.The aim of the study is to develop elements of a methodology for quantitative analysis of alternative tools, including recommendations on the choice of alternatives and criteria for their analysis, as well as the complex use of various methods to justify the final choice of an instrument.The research materials and methods reflected in the methodology of quantitative analysis of alternative project management tools in the financial sphere, and implemented in the course of the research, are based on supplementing the results obtained through the use of the classical basic level method – the method of geometric averages by methods of advanced level decision theory – the method of eigenvalues and vectors, as well as the ideal point method. In order to comprehensively analyze the project implementation support systems on the market and their instrumental capabilities, a twostage methodology is proposed in the research process, including both qualitative and quantitative assessment of available alternatives. The formalized assessment of the functionality of the pre-selected tools made it possible to justify the choice of the most preferred alternative. Particular attention is paid to the issues of attracting additional information to reduce uncertainty in the considered choice situation. Results. The main result of the conducted research was the scheme of substantiation of the optimal choice of a tool includes the construction of a variety of alternatives – tools of domestic and foreign development, the construction of a variety of criteria and the attraction of additional information to form a hierarchy of criteria, as well as a consistent quantitative assessment of each selected alternative according to all the criteria under consideration. The constructed set of alternatives includes the tools of Jira Software, Asana, Bitrix24, Megaplan, GanttPro, YouGile and Planfix. The set of criteria is limited to the six most significant criteria: “Convenience of communication within the framework of the tool”; “Price of the tool”; “Level of complexity of the tool”; “Integration with external services”; “Functionality of the tool”; “Convenience and simplicity of the tool interface”. The presented system of criteria takes into account the main functional and instrumental characteristics necessary for systems to support the implementation of projects in the financial sector at the level of small and medium-sized businesses.Conclusion. The content of the article can be useful for improving decision-making processes during technological renewal and digitalization of projects in the financial sphere of small and medium-sized businesses in the context of the implementation of import substitution programs, the development of optimal choice mechanisms under several criteria. The described structure of the applied problem of multi-criteria selection can be used to update the content of such professionally significant academic disciplines for higher economic school as “Decision Theory” and “Instrumental methods in Economics”.

The efficient selection of a perfect site for the development of an industrial site in spatial planning should consider the land condition and environmental factors to minimize the impacts and risks. This paper aimed to create a model to assess if an area that can be used for the development of an industrial site meets multiple criteria. The new method in this research integrates the GIS and AHP approaches into a web-based geographic information system application that is used to easily support widespread assessment and mapping. The assessment of the development potential of industrial areas in West Java Province showed that 77% of the land was appropriate for industrial sites, whereas the other 33% was less suitable for use as an industrial site location. The assessment of 23 industrial site development location points in the Industrial Site Spatial Plan of West Java Province showed that 14 locations were in the highly suitable category (P1) and 9 other locations were in the suitable category (P2). [ABSTRACT FROM AUTHOR]

Project portfolio selection is a common problem in modern organizations. The allocation of resources to projects taking into account (a) the multi-criteria evaluation of projects and (b) the policy requirements for the final portfolio, is often addressed with a combination of multi-criteria analysis for the evaluation part and integer programming for the optimization part. However, the final portfolio is sensitive to changes in the importance of criteria, due to the multi-criteria evaluation of the projects which is the driver of the optimization. In the proposed approach, we take into account the inherent subjectivity expressed in the weights of criteria using a variation of the Iterative Trichotomic Approach method (Mavrotas and Pechak in Int J Mult Criteria Decis Mak 3:79–97, 2013). Specifically, we use an iterative process that starts considering portfolios that emerge from optimizing separately each criterion and gradually converging to the original set of criteria weights. The additional information provided to the decision maker by the proposed method, is that she/he can realize if the selection or exclusion of a specific project in the final portfolio is objective or it depends on the subjective weights and to what extent, while the conventional MCDA-IP approach does not differentiate the selected projects according to the imposed degree of subjectivity. The method is illustrated with a real data application from a project portfolio selection problem in Greece with 540 R&D projects that have to follow sectoral and geographical constraints. [ABSTRACT FROM AUTHOR]

Creating routes across open areas is challenging due to the absence of a defined routing network and the complexity of the environment, in which multiple criteria may affect route choice. In the context of urban environments, research has found Visibility and Spider-Grid subgraphs to be effective approaches that generate realistic routes. However, the case studies presented typically focus on plazas or parks with defined entry and exit points; little work has been carried out to date on creating routes across open areas in rural settings, which are complex environments with varying terrain and obstacles and undefined entry or exit points. To address this gap, this study proposes a method for routing across open areas based on a Spider-Grid subgraph using queen contiguity. The method leverages a Weighted Sum–Dijkstra’s algorithm to allow multiple criteria such as surface condition, total time, and gradient to be considered when creating routes. The method is tested on the problem of routing across two areas of Dartmoor National Park, United Kingdom. The generated routes are compared with benchmark algorithms and real paths created by users of the Ordnance Survey’s Maps App. The generated routes are found to be more realistic than those of the benchmark methods and closer to the real paths. Furthermore, the routes are able to bypass hazards and obstacles while still providing realistic and flexible routes to the user.

One of the major sources of renewable energy, particularly electricity generation and water desalination, is solar energy. The National Initiative to produce Water and Electricity started when the electricity consumption in Saudi Arabia begun to increase by about 5% per year. The current investigation aims to use a multicriteria GIS technique to identify the best spatial location for solar energy collection in the Mecca Administrative District. The best locations for solar power plant construction were determined with the use of a set of factors and criteria, including planning and environmental criteria, and terrain calibrator. These criteria were defined through a thorough literature review. This information was then used to create a digital geographic database, which was incorporated into an integrated GIS to produce a spatial fit model. According to the suitability data, most of Mecca region is ideal for solar energy projects, with an applicability percentage ranging between 30% and 80%. These findings are encouraging and promising for Mecca's renewable energy industry and they should be considered. It was discovered by examining these spatial locations and the level of suitability to the specifications that the lands with a sufficient share of more than 80% form an area of around 4000km2 and makeup 3% of all suitable lands. The governorates of the Mecca Administrative Area are home to most of these exceptionally suited locations. The Taif governorate takes first place with 35% of the total area and the two governorates of Turbah are placed second and third with 24% and 14%. In the Mecca Administrative Area, the appropriate lands for solar energy projects are distributed spatially according to a digital map. The study proposes incorporating the findings into the Saudi national plan for renewable energy sources. [ABSTRACT FROM AUTHOR]

This paper aims to measure the level of artificial intelligence (AI) support to project management (PM) in selected service sector activities. The exploratory factor analysis was employed based on the extensive survey on AI in Slovenian companies and the multi-criteria measurement with an emphasis on value functions and pairwise comparisons in the analytic hierarchy process. The synthesis and performance sensitivity analysis results show that in the service sector, concerning all criteria, PM is with the level 0.276 best supported with AI in services of professional, scientific, and technical activities, which also stand out concerning the first-level goals in using AI solutions in a project with the value 0.284, and in successful project implementation using AI with the value 0.301. Although the lowest level of AI support to PM, which is 0.220, is in services of wholesale and retail trade and repair of motor vehicles and motorcycles, these services excel in adopting AI technologies in a project with a value of 0.277. Services of financial and insurance activities, with the level 0.257 second-ranked concerning all criteria, have the highest value of 0.269 concerning the first-level goal of improving the work of project leaders using AI. The paper, therefore, contributes to the comparison of AI support to PM in service sector activities. The results can help AI development policymakers determine which activities need to be supported and which should be set as an example. The presented methodological frame can serve to perform measurements and benchmarking in various research fields.

Seriously abnormal data exist in the synchronous monitoring data of transformer DC bias, which causes serious data feature contamination and even affects the identification of transformer DC bias. For this reason, this paper aims to ensure the reliability and validity of synchronous monitoring data. This paper proposes an identification of abnormal data for the synchronous monitoring of transformer DC bias based on multiple criteria. By analyzing the abnormal data of different types, the characteristics of abnormal data are obtained. Based on this, the abnormal data identification indexes are introduced, including gradient, sliding kurtosis and Pearson correlation coefficient. Firstly, the Pauta criterion is used to determine the threshold of the gradient index. Then, gradient is used to identify the suspected abnormal data. Finally, the sliding kurtosis and Pearson correlation coefficient are used to identify the abnormal data. Data for synchronous monitoring of transformer DC bias in a certain power grid are used to verify the proposed method. The results show that the accuracy of the proposed method in identifying mutated abnormal data and zero-value abnormal data is claimed to be 100%. Compared with traditional abnormal data identification methods, the accuracy of the proposed method is significantly improved. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative procedure for designing efficient biomass-biofuel logistics networks (BBLNs). This procedure is based on the two-stage network data envelopment analysis (TSN-DEA) models that have been developed to provide specific process guidance for the managers to improve the efficiency of the decision-making unit (DMU) with the TSN process. The crucial issue of the TSN-DEA is that the overall efficiency score depends on the DMUs under evaluation. Thus, the rankings for the DMUs generated by the TSN-DEA model are inconsistent. As a result, the TSN-DEA-based ranking methods are limited. The TSN-DEA's inconsistency frequently makes it difficult for decision-makers to select the top-rated DMUs. We develop the transformed TSN (T-TSN) DEA method by applying the multi-criteria DEA model to overcome this issue. The proposed method transforms the DMUs with any number of inputs, intermediate measures, and outputs in the TSN process, through the multi-objective programming model with a minimax objective approach, into the DMUs with two inputs and one output in the single-stage network (SSN) process. Then, the well-known DEA methods for the SSN, such as the cross-efficiency and super-efficiency DEA methods, can be applied to evaluate and rank the transformed DMUs more consistently. We exhibit the applicability of the proposed approach for the BBLN design problem. A case study of South Carolina in the USA demonstrates that the proposed method performs well in identifying efficient BBLN schemes more consistently than the traditional TSN-DEA. [ABSTRACT FROM AUTHOR]

The generalized estimating equations (GEE) procedure is a common approach for regression analyses of correlated responses, which is common for data obtained in longitudinal studies. Selecting a working correlation structure has been a momentous topic in longitudinal data analysis using GEE. Although consistent and asymptotically normal estimates can be obtained with misspecified working correlation structure, an appropriate selection of the correlation structure improves the efficiency of the regression estimators. Numerous criteria have been proposed to select the best working correlation structure in GEE. Besides single criterion, some authors suggested different approaches of using multiple criteria. In this paper, a comparison of available criteria is done along with different combinations of selected multiple criteria. We investigated their performance using both simulated and real data. Simulation results indicate that data analysts should be cautious in selecting multiple criteria to be combined. Practical recommendations are also given for choosing a working correlation structure in GEE. [ABSTRACT FROM AUTHOR]

Land suitability analysis and land use mapping are one of the most practical applications of Geographic Information Systems in land resource management. In the present study, the qualitative assessment of land suitability was investigated using fuzzy and parametric hierarchical analysis process models for the irrigated wheat crop. Soil characteristics, climatic conditions, topography, and accessibility were selected based on the Food and Agriculture Organization framework and expert opinions. The interpolation function was used to plot values to points in terms of quality/terrain characteristics for the type of operation and the evaluation was performed based on parametric and fuzzy analytical hierarchy process models. According to the parametric model, the values of land indices vary from 50% in some places to 78% in the study area, which classifies the plain from suitable classes (S2) to upper classes (S1). Preferred values fuzzy analytical hierarchy process for wheat cultivation in the study area are from 0.25 to 0.73, which is classified as medium to very high for wheat production. Kappa Cohen coefficient between parametric land indices and preferential values fuzzy analytical hierarchy process with observed wheat yield varies between 0.03 and 0.237, respectively, which confirms the validity of both models in estimating land suitability for irrigated wheat production in the study area. The results of the present study show that fuzzy analytical hierarchy process is an effective strategy to increase the weight accuracy of the criteria affecting the analysis of land suitability. [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.)

Revenue management (RM) deals with selling the right product to the right customer at the right time at the right price through the right channel to maximise revenue. The innovation of RM lies in the way decisions are made. The performance of revenue management approaches can be evaluated against several criteria. Both discrete and continuous multicriteria models can be used to analyse RM. The performance pyramid is a comprehensive, fully integrated perperformance system that captures multiple perspectives such as internal, financial, customer and innovation. The assessment is based on a combination of Analytic Hierarchy Process (AHP), Analytic Network Process (ANP) and Data Envelopment Analysis (DEA) approaches. Customer behavior modeling is gaining increasing attention in revenue management. Customer choice models can be extended with more inputs and more outputs. Evaluation of alternatives can be performed using DEAbased evaluation methods. The search for an efficient frontier in a DEA model can be formulated as a multiobjective linear programming problem. We propose to use an Aspiration Level Oriented Procedure (ALOP) to solve the problem. [ABSTRACT FROM AUTHOR]

The identification of conservation priority areas (CPAs) based on multiple ecosystem services (ESs) is a prerequisite and important basis for decision-makers in implementing regional ecological restoration. However, the trade-offs between different services pose great challenges to determining the locations of priority areas. Based on the identification and assessment of key ESs in the Hengduan Mountain region from 2000 to 2020, a multicriteria ordered weighted average (OWA) method was used to balance the conflicts among different services and determine the location of CPAs. The results indicated that the optimal CPAs in the Hengduan Mountain region totaled 89,699 km2, accounting for 20.30% of the study area, and were located mainly in the southwestern and eastern regions. Moreover, the OWA method balanced the trade-offs among multiple services. In the four CPA scenarios that protected each service exclusively, the protection efficiency (PE) of each respective service was greater, but the PE of the remaining three services was always smaller than the corresponding PEs in the optimal scenario. The comprehensive PE of the optimal CPA scenario was 1.27, and the PE values of net primary productivity, water yield, soil conservation, and cultural service were 1.45, 1.20, 1.08, and 1.39, respectively. The CPA determination method in this study can provide a scientific blueprint for regional ecological restoration and the determination of the CPAs in other regions.

Underrepresentation of students from low-income families and/or certain ethnic groups has been a persistent worldwide problem in the field of gifted education. Because teachers frequently serve as gatekeepers for students' participation in gifted programming, their roles in the identification process are critical. In this study, 55 elementary school teachers in South Korea completed a teacher-rating scale (HOPE Scale; Peters & Gentry, 2012a) for all students (n = 1,157) in their classrooms, including their evaluation of each students' reading and mathematics talents. Their ratings and talent evaluations were then compared to students' reading and mathematics achievement scores to determine whether teachers' ratings promoted more equitable identification of students from underrepresented groups. In addition, we explored the effects of combining various cut-off scores for each assessment for the identification of students from low-income and/or culturally diverse families. [ABSTRACT FROM AUTHOR]

COVID-19 has brought several harmful consequences to the world from many perspectives, including social, economic, and well-being in addition to health issues. However, these harmful consequences vary in intensity in different regions. Identifying which cities are most vulnerable to COVID-19 and understanding which variables could be associated with the advance of registered cases is a challenge. Therefore, this study explores and builds a spatial decision model to identify the characteristics of the cities that are most vulnerable to COVID-19, taking into account social, economic, demographic, and territorial aspects. Hence, 18 features were separated into the four groups mentioned. We employed a model joining the dominance-based rough set approach to aggregate the features (multiple criteria) and spatial analysis (Moran index, and Getis and Ord) to obtain final results. The results show that the most vulnerable places have characteristics with high population density and poor economic conditions. In addition, we conducted subsequent analysis to validate the results. The case was developed in the northeast region of Brazil. [ABSTRACT FROM AUTHOR]

The environmental-energy-economic-social (3E-S) sustainable development problem aims to balance the environment, energy, and economy upon which human society critically relies. However, it is further complicated by various ambiguous input parameters about greenhouse gas (GHG) emissions, electricity consumption, gross domestic product (GDP), and unemployment rate. To address this complexity, this paper develops an ambiguous chance constrained optimization model with multiple chance soft constraints for optimizing the 3E-S sustainable development problem by strategically allocating labor across key sectors. Tractable formulations of the proposed model are derived by constructing three types of ambiguity sets for input parameters with ambiguous probability distributions. The first is to construct support-mean ambiguous distribution sets with little known information, the second is to extend support-mean ambiguity sets to mean-covariance ambiguity sets by incorporating interactive relationships among sectors, and the third is to extend support-mean ambiguity sets to inner-outer ambiguity sets with Wasserstein metric from globalized distributionally robust perspective. The availability and computability of the proposed model have been illustrated by planning India's 3E-S sustainability for the year 2030. The results suggest that (i) the proposed method exhibits the capability to offer flexible policy measures; (ii) India should prioritize the development of the Mining and quarrying sector , the Transport, storage, communication, and services sector , and the Constructions sector ; (iii) India needs to make greater efforts for promoting GDP while controlling GHG emissions and energy consumption; (iv) The policy measures and objectives are influenced by tolerance levels, support intervals, interactive relationships among sectors, globalized sensitivity, and Wasserstein ball size. • Ambiguous chance constrained optimization method for 3E-S sustainability is developed. • Various distribution uncertainties are incorporated into the proposed model. • Tractable formulations are derived under multiple ambiguity sets. • Tractable extension to inner-outer ambiguity sets is derived from globalized distributionally robust perspective. • In-depth analyses and managerial implications are obtained for India's sustainability for 2030. [ABSTRACT FROM AUTHOR]

Sales territory design is an important research field because salesforce allocation within territories impacts sales organization effectiveness and customer service. This work presents a novel multi-objective model for re-designing sales territories with three main objectives: sales balancing, workload balancing, and geographic balancing. To measure sales and workload balancing, the variance among territories was calculated. The metric considered for geographic balancing was the sum of the distances from every salesperson to their assigned customers. A metaheuristic algorithm based on Tabu search was developed to solve a weighted aggregate function that integrates the three objectives. The algorithm is embedded in a procedure to systematically change the weights in the aggregate objective function to produce an approximate Pareto front of solutions. The algorithm was tested with instances based on data from a company in Mexico, providing salesperson-customer assignments that can be projected in territories in geographic information systems. The algorithm converges very fast for the instances studied and produces a Pareto front efficiently. Comparing the current situation of the company to a dominating solution obtained with the algorithm in the Pareto front, a significant improvement in the balance is achieved, in the order of 42.0 - 47.1% on average in the three objective functions. Another managerial benefit achieved by the company was a better understanding for the top managers of the salesforce, the customer preferences, and the challenge of serving a large and dispersed market.

Seriously abnormal data exist in the synchronous monitoring data of transformer DC bias, which causes serious data feature contamination and even affects the identification of transformer DC bias. For this reason, this paper aims to ensure the reliability and validity of synchronous monitoring data. This paper proposes an identification of abnormal data for the synchronous monitoring of transformer DC bias based on multiple criteria. By analyzing the abnormal data of different types, the characteristics of abnormal data are obtained. Based on this, the abnormal data identification indexes are introduced, including gradient, sliding kurtosis and Pearson correlation coefficient. Firstly, the Pauta criterion is used to determine the threshold of the gradient index. Then, gradient is used to identify the suspected abnormal data. Finally, the sliding kurtosis and Pearson correlation coefficient are used to identify the abnormal data. Data for synchronous monitoring of transformer DC bias in a certain power grid are used to verify the proposed method. The results show that the accuracy of the proposed method in identifying mutated abnormal data and zero-value abnormal data is claimed to be 100%. Compared with traditional abnormal data identification methods, the accuracy of the proposed method is significantly improved.

In Brazil, the location of sites for disposal in urban cities needs a lot of attention. Still, in most of the small cities, there is no waste management - which means they do not have landfill sites for waste disposal. The purpose of this paper is to evaluate the suitability of the landfills for Municipal solid waste (MSW) management in the Brazilian semi-arid region. Most of Brazil's semi-arid regions are in the northeast part of the country. One of their characteristics is the lack of practices to manage wastes disposal in small cities. Therefore, this paper employs a spatial multi-criteria analysis (Dominance-based Rough Set Approach) and spatial analysis to evaluate several site candidates for waste disposal in multiple counties located in the Angicos region (Brazilian northeast), on Rio Grande do Norte state. Our findings consist of the identification of local sites for landfills facilities, considering factors such as elevation, land use/land cover, and the distance from river bodies, urban areas. The site was selected through spatial analysis. Two sites available to receive and manage waste from eight counties were identified as a result. As suggested, the proposed analysis method may be applied in similar regions. [ABSTRACT FROM AUTHOR]

In response to Oklahoma State University's goal to provide safe face-to-face course options for as many students as possible in fall 2020, the Spears School of Business leadership developed a strategy based on data analytics and multiobjective optimization that allowed the implementation of a smooth transition plan for classes in fall 2020. Open- Solver was used (within Excel) to create an effective and efficient tool that successfully implemented classroom assignments that maximized face-to-face offerings while minimizing disruptions to the schedule, faculty, and registrar's office. [ABSTRACT FROM AUTHOR]

The current educational system radically changed during the last years and continues to grow. The Smart education is now a typical feature of education issue from a new information and communication technologies and of the introduction of these new technologies in the institutional learning. The introduction of these technologies in education has been associated with a rise evolution of people's quality of life by improving teaching and learning. MOOCs, are some of the recent technologies, which have been introduced recently in the higher education sector. This article focuses on the level of intelligence introduced into the online teaching and learning process. Different MOOCs will be presented, compared and analyzed according to a comparative study of multicriteria analysis by applying decision analysis methods; Analytical Hierarchy Process (AHP) and Complex Proportional Assessment Method (COPRAS) to show the importance of MOOC platforms and their impact in smart education. [ABSTRACT FROM AUTHOR]

This paper provides a novel framework for solving multiobjective discrete optimization problems with an arbitrary number of objectives. Our framework represents these problems as network models, in that enumerating the Pareto frontier amounts to solving a multicriteria shortest-path problem in an auxiliary network. We design techniques for exploiting network models in order to accelerate the identification of the Pareto frontier, most notably a number of operations to simplify the network by removing nodes and arcs while preserving the set of nondominated solutions. We show that the proposed framework yields orders-of-magnitude performance improvements over existing state-of-the-art algorithms on five problem classes containing both linear and nonlinear objective functions. Summary of Contribution: Multiobjective optimization has a long history of research with applications in several domains. Our paper provides an alternative modeling and solution approach for multiobjective discrete optimization problems by leveraging graphical structures. Specifically, we encode the decision space of a problem as a layered network and propose graph reduction operators to preserve only solutions whose image are part of the Pareto frontier. The nondominated solutions can then be extracted through shortest-path algorithms on such a network. Numerical results comparing our method with state-of-the-art approaches on several problem classes, including the knapsack, set covering, and the traveling salesperson problem (TSP), suggest orders-of-magnitude runtime speed-ups for exactly enumerating the Pareto frontier, especially when the number of objective functions grows. [ABSTRACT FROM AUTHOR]

The parametric concept of equilibrium in a finite cooperative game of several players in a normal form is introduced. This concept is defined by the partitioning of a set of players into coalitions. Two extreme cases of such partitioning correspond to Pareto optimal and Nash equilibrium outcomes, respectively. The game is characterized by its matrix, in which each element is a subject for independent perturbations., i.e. a set of perturbing matrices is formed by a set of additive matrices, with two arbitrary Hölder norms specified independently in the outcome and criterion spaces. We undertake post-optimal analysis for the so-called stability kernel. The analytical expression for supreme levels of such perturbations is found. Numerical examples illustrate some of the pertinent cases. [ABSTRACT FROM AUTHOR]

The use of project portfolio management is increasingly becoming a tool for promoting the strategy of the organization. Using sophisticated quantitative tools becomes a significant competitive advantage for project portfolio management. Project portfolio management is a dynamic multi-criteria decision-making problem under risk. The paper presents new approaches for analyzing the problem. A dynamic version of the Analytic Network Process (ANP) captures the network, multicriteria and dynamic structure of the problem. Multicriteria decision trees analyze risk of project portfolios. Possible projects are characterized by sets of inputs and outputs, where inputs are resources for project realization and outputs measure multiple criteria of goals of the organization. The Data Envelopment Analysis (DEA) is an appropriate approach to select efficient project portfolios.

Copyright of Radio Electronics, Computer Science, Control is the property of Zaporizhzhia National Technical University 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.)

As an important method for uncertainty modeling, Dempster-Shafer (DS) evidence theory has been widely applied in practical applications. However, the counter-intuitive results are often generated when fusing different sources of highly conflicting evidence with Dempster's combination rule. Several different methods for measuring the evidence conflict have been proposed. Nevertheless, these methods showed focus only on a single criterion to measure the conflicting evidence. Mono-criteria factor for the measurement of the conflict between evidence is, however, often unreliable and inaccuracy. Because various factors affect the degree of conflict between the evidence, such as imperfection, dissimilarity, disparity, and uncertainty. To address this issue, multiple criteria factors are utilized to measure the degree of conflict between the evidence in this paper. An improved analytic hierarchy process is proposed to determine the weights of each body of evidence by considering multiple criteria. Firstly, calculating the quantitative value of the evaluation index of each evidence under every criterion. The covariance matrix of the criterion layer is determined based on the covariance between the quantitative values of each criterion. Then, the pairwise comparison matrix of the criterion layer can be obtained by transforming the covariance matrix. Next, the variance among the quantitative values of each criterion is applied to construct the fuzzy preference relation matrix. The fuzzy preference relation matrix is used to replace the pairwise comparison matrix of the scheme layer. After that, the weight of the criterion layer and the scheme layer are combined to acquire the final weights of each evidence. Finally, the original evidence is modified with the final weights of the evidence before using Dempster's combination rule. Two numerical experiments are given to verify the efficiency of the proposed approach. The result shows that the proposed method is more efficient and feasible in managing the conflicting evidence than other approaches available in the literature described.

The paper presents an innovative application to identify areas vulnerable to coronavirus disease 2019 (COVID-19) considering a combination of spatial analysis and a multi-criteria learning approach. We applied this methodology in the state of Pernambuco, Brazil identifying vulnerable areas by considering a set of determinants and risk factors for COVID-19, including demographic, economic and spatial characteristics and the number of human COVID-19 infections. Examining possible patterns over a set number of days taking the number of cases recorded, we arrived at a set of compatible decision rules to explain the relation between risk factors and COVID-19 cases. The results reveal why certain municipalities are critically vulnerable to COVID-19 highlighting locations for which knowledge can be gained about environmental factors.

Consuming too much fat, sugar, and salt is associated with adverse health outcomes. Food reformulation is one possible strategy to enhance the food environment by improving the nutritional quality of commercial products. However, food reformulation faces many hindrances. One way to alleviate some of these hindrances is to embrace a multicriteria approach that is based on a market inventory. In this objective, additional sensory screening and water content analyses allow going beyond nutrition and composition information on the packaging. However, due to feasibility reasons for later in-depth analyses, it is necessary to work with several reduced and manageable products. To the best of the authors' knowledge, in the literature, there is no sample selection approach taking into account multiple criteria as a base for future food reformulation. The overall aim of this paper is to propose a method to select the best representative products from the market base, for future reformulation by going beyond nutrition and composition information on the packaging. This approach considered therefore nutrition, composition, economic, water content, and sensory information with the example of the cookies market. The first step is the creation of an extensive cookie database including sensory and water content information. In total 178 cookies among the French market were identified, then focus was placed on 62 chocolate chip cookies only. Sensory screening and water content analyses of all 62 products were conducted. The second step is to make an informed subset selection, thanks to a cluster analysis based on 11 nutrition, composition, and water content variables. A representative subset of 18 cookies could be derived from the obtained clusters. The representativity was evaluated with statistical uni- and multivariate analyses. Results showed a broad variety of chocolate chips cookies with a large nutritional, compositional, water content, and sensory differences. These results highlighted the first paths for future reformulation in this product category and showed the importance to include physical product information beyond the information on the packaging. This complete database on the selected cookies constituted a solid base for identifying future reformulation levers, in order to improve the nutritional quality and health.

We develop interactive algorithms to find a strict total order for a set of discrete alternatives for two different value functions: linear and quasiconcave. The algorithms first construct a preference matrix and then find a strict total order. Based on the ordering, they select a meaningful pair of alternatives to present the decision maker (DM) for comparison. We employ methods to find all implied preferences of the DM, after he or she makes a preference. Considering all the preferences of the DM, the preference matrix is updated and a new strict total order is obtained until the termination conditions are met. We test the algorithms on several instances. The algorithms show fast convergence to the exact total order for both value functions, and eliciting preference information progressively proves to be efficient. [ABSTRACT FROM AUTHOR]

Principal covariates regression (PCovR) allows one to deal with the interpretational and technical problems associated with running ordinary regression using many predictor variables. In PCovR, the predictor variables are reduced to a limited number of components, and simultaneously, criterion variables are regressed on these components. By means of a weighting parameter, users can flexibly choose how much they want to emphasize reconstruction and prediction. However, when datasets contain many criterion variables, PCovR users face new interpretational problems, because many regression weights will be obtained and because some criteria might be unrelated to the predictors. We therefore propose PCovR2, which extends PCovR by also reducing the criteria to a few components. These criterion components are predicted based on the predictor components. The PCovR2 weighting parameter can again be flexibly used to focus on the reconstruction of the predictors and criteria, or on filtering out relevant predictor components and predictable criterion components. We compare PCovR2 to two other approaches, based on partial least squares (PLS) and principal components regression (PCR), that also reduce the criteria and are therefore called PLS2 and PCR2. By means of a simulated example, we show that PCovR2 outperforms PLS2 and PCR2 when one aims to recover all relevant predictor components and predictable criterion components. Moreover, we conduct a simulation study to evaluate how well PCovR2, PLS2 and PCR2 succeed in finding (1) all underlying components and (2) the subset of relevant predictor and predictable criterion components. Finally, we illustrate the use of PCovR2 by means of empirical data. [ABSTRACT FROM AUTHOR]