Multi-criteria System Research Articles

A Universal Framework for Skill-Based Cyber-Physical Production Systems

In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and a shortage of skilled labor. This paper proposes a universal framework for skill-based cyber–physical production systems (CPPS) that formalizes production knowledge into machine-processable formats. Key contributions include a novel conceptual model for skill-based production processes and an automated method to derive production plans from high-level CPPS skills for production planning and execution. This framework aims to enhance smart manufacturing by enabling more efficient, transparent, and automated production planning, thereby addressing the critical gap in current manufacturing practices. The framework’s benefits include making production processes explainable, optimizing multi-criteria systems, and eliminating human biases in process selection. A case study illustrates the framework’s application, demonstrating its current capabilities and potential for modern manufacturing.

66. Optimizing the use of antibiotics: Towards multi-criteria systems for monitoring the health and well-being of pigs and poultry

66. Optimizing the use of antibiotics: Towards multi-criteria systems for monitoring the health and well-being of pigs and poultry

An Engineered Assessment System to Manage Demand Reduction Initiatives

The Joint Warfighting Concept is a framework for how the United States will fight in the future security landscape. To successfully carry out these new concepts, the military services must find ways to lessen the logistics burden at the strategic, operational, and tactical levels. Only by reducing the demands on its current and planned sustainment systems can the United States continue to be a dominant force on the world stage. However, defense agencies such as the Logistics Functional Capability Board lack a quantitative, credible, repeatable process to effectively assess and manage demand reduction initiatives. This paper is based on work from a student/faculty team that created an assessment system for demand reduction initiatives. Discussion highlights the systems engineering techniques that were used in the effort, which includes but is not limited to stakeholder analysis, functional analysis, morphological analysis, and value modeling. The blended qualitative and quantitative methods focused on user and stakeholder needs to create a process system to evaluate technologically disparate, multicriteria systems. The team expects that the resultant assessments system’s applicability to a range of demand reduction initiatives would be of interest to organizations that must prioritize technology proposals, as well as perform trade-off analysis. Additionally, the approach to create the assessment system has the flexibility to adapt to changes in the stakeholders’ requirements from the reduction initiatives, as well as the ongoing evolution of the warfighting concepts.

Comprehensive economic analysis of innovation activity of the enterprise

Subject. The article deals with scientific and methodological approach to the complex economic analysis of innovation activity of enterprises. Objectives. The aim is to determine the vector of management decisions regarding the increase in the level of enterprise’s innovation activity. Methods. The study employs methods of scientific abstraction, analysis, synthesis, systematization and generalization, complex integrated assessment, statistical analysis, and matrix methods. Results. To perform the comprehensive economic analysis of innovation activity, we developed a system of indicators and a structural and logical scheme. The results of the analysis of innovation activity of enterprises operating in the food industry of the Donetsk People's Republic show positive trend in the introduction of different kinds of innovations in different aspects of activity. At the same time, the level of innovation activity of most enterprises (60%) is assessed as average, and of 40% - as satisfactory. This requires to develop directions for innovative renewal of existing business models of food industry enterprises, and actively introduce innovations. Conclusions. The practical value of our structural and logical scheme of integrated economic analysis of enterprises’ innovation activity consists in the possibility of integrating into a single cyclical process of a sequence of actions to determine the actual level of innovation activity. The scientific and methodological approach, presented in the study, rests on a multi-criteria system of evaluation indicators, takes into account innovation performance criteria, and enables to reasonably determine management responses to innovation renewal of the current business model of the enterprise.

A multi-criteria system for performance assessment and support decision-making based on the example of Premier League top football strikers

The effective management of sports teams plays a pivotal role in achieving substantial success. In this pursuit, an increasing number of teams are resorting to information systems to enhance decision-making processes. These systems ensure the implementation of well-informed decisions, thereby contributing additional insights into training management. Through their assistance, it becomes feasible to optimize both player potential and overall team performance. In this paper, we propose a decision system rooted in the Multi-Criteria Decision Analysis (MCDA) approach, complemented by sensitivity analysis, to assess the performance of soccer players. The evaluation focused on football players in the forward position in the Premier League during the seasons 2015-2021. Subsequently, a sensitivity analysis of the results was conducted to delineate key elements in the game that significantly influence performance quality. Based on these findings, adjustments to a player's training plan can be made, targeting specific aspects of the game to enhance the player's potential. The study's results underscore that, with appropriate enhancements to players' performance, they can achieve markedly superior ratings compared to their counterparts.

МЕТОД ОЦЕНКИ СТРУКТУРНОЙ ЭФФЕКТИВНОСТИ СИСТЕМЫ ГРУЗОВЫХ КОНТЕЙНЕРНЫХ АВТОМОБИЛЬНЫХ ПЕРЕВОЗОК

The article presents a method for assessing the structural efficiency of the container freight transportation system, designed to manage the quality indicators of container road transportation. The developed method makes it possible to implement digital control technology in the form of objective estimates of the effectiveness of individual structures in the system of container road transportation. Effective states of individual structures (motor transport enterprises) are defined as a comprehensive assessment of interaction in a complex multi-level multi-criteria system. The de-veloped method can be scaled for use in cross-industry control systems.

Multiple criteria decision support system for customer segmentation using a sorting outranking method

For companies, customer segmentation plays a key role in improving supply chain management by implementing appropriate marketing strategies. The objectives of this research are to design and validate a multicriteria model to support decision making for customer segmentation in a business to business context. First, the model based on the transactional customer behaviour is extended by a hierarchy with three main criteria: Recency, Frequency and Monetary (RFM), customer collaboration and growth rates. Customer collaboration includes quota compliance, variety of products and customer commitment to sustainability (reverse logistics and shared information). Second, the Global Local Net Flow Sorting (GLNF sorting) algorithm is implemented and validated using real company data to classify 8,157 customers of a multinational healthcare company. Third, the SILS quality indicator has been implemented and validated to assess the quality of preference-ordered customer groups and its parameters have been adapted for contexts with thousands of alternatives. The results are also compared with an alternative model based on data mining (K-means). The multicriteria system proposed allows to segment thousands of customers in ordered categories by preferences according to company strategies. The segments generated are more homogeneous, robust and understandable by managers than those from alternative methods. These advantages represent a relevant contribution to automating supply chain management while providing detailed analysis tools for decision making.

MULTI-CRITERIA EVALUATION OF THE MULTIFACTOR STOCHASTIC SYSTEMS EFFECTIVENESS

Subject of the research in the article is the evaluation of complex multifactor stochastic systems functioning effectiveness according to a variety of correlated criteria. The problem actuality is determined by the fact that an independent evaluation of system effectiveness for each of the mutually correlated criteria for the system under consideration is not informative. In well-known works in the direction of multiple correlation, a relatively simple problem of estimating the correlation between one resulting parameter and a set of influencing factors is considered, which is not enough for the analysis and management of multicriteria systems. In addition, the known results do not take into account possible significant differences in influencing factors mutual correlation values. Purpose of Work is to develop a methodology for a comprehensive assessment of system effectiveness according to a variety of interrelated criteria. Tasks to be Solved: splitting the set of system parameters into two subsets (parameters determining the effectiveness of system and parameters affecting it), forming additive convolutions of parameters included in subsets, developing a methodology for calculating the multiple correlation coefficient between the components of the selected subsets, developing a method for differentiating a scalar function from a vector argument by this argument. Applied Methods: nonlinear programming, multidimensional correlation analysis, method of differentiation of scalar functions by vector argument. These methods are used for forming and calculating a multiple correlation coefficient between the set of system effectiveness complex criterion components values and its control parameters set values. Results Obtained: proposed methodology provides the possibility of solving the problems of system management, taking into account the revealed relationship between the multi-criteria evaluation of system effectiveness and values of its controlled parameters. At the same time, an important advantage of the obtained result lies in the possibility that arises when using it to take into account the joint (group) influence of control variables on the complex criterion of system efficiency. The developed technology of scalar functions differentiation by vector argument has great practical importance which expands the arsenal of computational mathematics.

Customer Attitude toward Digital Wallet Services

The goal of this study is to examine and identify the factors influencing customer attitude toward and intention to use digital wallets (electronic wallets, e-wallets) during and after the COVID-19 pandemic. A total of 257 correctly fulfilled questionnaires from an online survey were summarized. The main features of e-wallet payment systems were classified with a focus on consumer satisfaction via the integration of classic and modern data analysis methods. Structural Equation Modeling (SEM) was preferred to reveal the dependencies between the variables from e-wallets users’ perspective. The designed model can discover and explain the underlying relationships that determine the e-wallets’ adoption mechanism. The obtained results lead to specific recommendations to stakeholders in the value chain of payment processing. Financial regulatory authorities could employ the presented results in planning the development of payment systems. E-commerce marketers could utilize the proposed methodology to assess, compare and select an alternative way for order payment. E-wallet service providers could establish a reliable multi-criteria system for the evaluation of digital wallet adoption. Being aware of the most important components of e-wallets value, managers can more effectively run and control payment platforms, enhance customer experience, and thus improve the company’s competitiveness. As the perceived value of customer satisfaction is subjective and dynamic, measurements and data analysis should be conducted periodically.

Modeling the assessment of a region’s innovative potential

The paper is devoted to the design and modeling of the system for assessing a region’s innovative potential to justify management decisions in public authorities. The purpose of the study is to substantiate the feasibility of using digital tools and artificial intelligence to formalize and visualize the assessment results. The use of a multi-criteria system for assessing the level of a region’s development, which includes four areas, characterized by 46 indicators, is difficult to interpret to justify management decisions. Based on the theory of fuzzy logic, logical programming methods focused on the application of a system of facts and rules, formal knowledge representation languages for describing subject areas and methodology for developing expert systems for complex problem solving, taking into account the empirical knowledge of experts, a model for assessing the innovative potential of a region is proposed. A case study of one of the Russian regions is illustrated by graphs of 3D visualization of the optimal combination of parameters of the level of innovative development. The formed triad of sustainable and safe development makes it possible to display the behavior of the regional innovation development index as a whole on the basis of social, economic and environmental indicators of sustainability.

Characterisation of likelihood of gas hydrates occurrence in the South China Sea based on Bonferroni mean-based TOPSIS and fuzzy set theory

The efficiency of gas hydrate production depends on the success of gas exploration and occurrence evaluation. The existing evaluation models are generally univariate and only applicable to certain geological settings. This study presents a holistic approach to evaluate the likelihood of gas hydrate occurrence by supplying an index for mapping gas hydrate levels with depth. The approach integrates a generalised TOPSIS method with the fuzzy set theory. An expedition of gas hydrate conducted in the Shenhu area of the South China Sea was adopted as a case study to assess the reliability of the proposed index. As a multivariate model, the proposed approach enables the capture of non-linearity associated with gas hydrates in its entirety. The magnitude of the strength of the influential factor varies substantially from one site to another across the Shenhu area. The results also show that no site achieves the highest likelihood ‘Level V’. These results are consistent with the gas saturation values obtained using Archie’s relationship. For example, at SH4 and SH7, the values of the likelihood index are the highest between 170–185 m and 150–165 m, respectively, and the observed saturation at these locations varies from 20% (SH4) to 43% (SH7). The proposed likelihood index yields a prominent ability to quantify the level of occurrence of gas hydrates with depth at different sites. It appears to be an efficient multicriteria system bound to improve the management of the gas production trial stage.

Artificial intelligence enabled efficient power generation and emissions reduction underpinning net-zero goal from the coal-based power plants

A large power generation facility is a complex multi-criteria system associated with multivariate couplings, high dependency, and non-linearity among the operating variables which present a major challenge to ensure efficient power production. In this research, an integrated artificial intelligence (AI) and response surface methodology (AI-RSM) framework to achieve the efficient power production operation of a 660 MW coal power plant is presented. Two AI algorithms, i.e., extreme learning machine (ELM) and support vector machine (SVM) are trained comprehensively on the power plant’s operational data and are validated as well. Full factorial design of experiments on the three levels of the operating parameters are constructed and simulated from the better performing AI model which is an effective non-linear representation of the complex power plant operation. RSM analysis is carried out under three power generation scenarios to simulate the effective values of the operating variables which are tested on the power plant’s operation and a reasonable agreement is found with the experimental observations. The notable improvement in fuel consumption rate, thermal efficiency, and heat rate of the power plant under Half Load, Mid Load, and Full Load capacity of the power plant is achieved by the AI-RSM framework enabled analyses. It is estimated that annual reduction in CO2, CH4 and Hg emissions measuring 210 kg tons per year (kt/y), 23.8 t/y and 2.7 kg/y, respectively can be obtained corresponding to Mid Load operating state of the power plant. The research presents the reliable and robust utilization of AI-RSM framework for simulating the effective operating conditions for the fossil-based power plants’ operation with an eventual goal to improve the techno-environmental performance which is expected to contribute to net-zero emissions goal from the energy sector.

Developing a Quasi-Optimal, in Terms of Transition Time and Energy Consumption, Closed-Loop Control System for an Electrical Installation

Introduction. The efficiency and normal work of electrotechnological processes and installations in their operation dynamic modes is characterized by the time of transition from initial to final state, low energy consumption, accuracy in transients and stability of the desired final state. It is proposed, from a single position on the basis of special optimal control, to combine transition and stabilization systems both in terms of determining the control algorithm with minimal energy consumption in the function of the object states and determining the parameters and conditions of movement with minimal deviation from a given trajectory providing the optimal transmission time and energy saving in a stable closed system of an object control. Materials and Methods. The principle of maximum is used as the main method for finding optimal program control, which for the study of special modes was supplemented with the apparatus of the position generality conditions for nonlinear objects with the coordinate space expansion, taking into account the occurrence of time and optimality criterion. The position generality apparatus is also used to solve energy-saving problems through using linearization in a large source object. Quasi-optimality in terms of transition time and energy consumption is achieved through minimizing energy according to the program motion parameter, which has a contradictory effect on the transition time and control amplitude. Results. To assess computational difficulties, transition time, energy saving, accuracy and stability, an example of inertial object control according to various criteria is given. The structure of a closed quasi-optimal system with stationary feedback, which is simple in technical implementation, is obtained. Discussion and Conclusion. The formalization of the approach to the construction of quasi-optimal systems based on the position generality allows it to be used in multi-criteria optimization tasks and computer-aided design systems for energy-intensive industrial, transport, and agricultural electrical installations.

Using single impact metrics to assess research in business and economics: why institutions should use multi-criteria systems for assessing research

PurposeDespite the general recommendation of using a combination of multiple criteria for research assessment and faculty promotion decisions, the raise of quantitative indicators is generating an emerging trend in Business Schools to use single journal impact factors (IFs) as key (unique) drivers for those relevant school decisions. This paper aims to investigate the effects of using single Web of Science (WoS)-based journal impact metrics when assessing research from two related disciplines: Business and Economics, and its potential impact for the strategic sustainability of a Business School.Design/methodology/approachThis study collected impact indicators data for Business and Economics journals from the Clarivate Web of Science database. We concentrated on the IF indicators, the Eigenfactor and the article influence score (AIS). This study examined the correlations between these indicators and then ranked disciplines and journals using these different impact metrics.FindingsConsistent with previous findings, this study finds positive correlations among these metrics. Then this study ranks the disciplines and journals using each impact metric, finding relevant and substantial differences, depending on the metric used. It is found that using AIS instead of the IF raises the relative ranking of Economics, while Business remains basically with the same rank.Research limitations/implicationsThis study contributes to the research assessment literature by adding substantial evidence that given the sensitivity of journal rankings to particular indicators, the selection of a single impact metric for assessing research and hiring/promotion and tenure decisions is risky and too simplistic. This research shows that biases may be larger when assessment involves researchers from related disciplines – like Business and Economics – but with different research foundations and traditions.Practical implicationsConsistent with the literature, given the sensibility of journal rankings to particular indicators, the selection of a single impact metric for assessing research, assigning research funds and hiring/promotion and tenure decisions is risky and simplistic. However, this research shows that risks and biases may be larger when assessment involves researchers from related disciplines – like Business and Economics – but with different research foundations and trajectories. The use of multiple criteria is advised for such purposes.Originality/valueThis is an applied work using real data from WoS that addresses a practical case of comparing the use of different journal IFs to rank-related disciplines like Business and Economics, with important implications for faculty tenure and promotion committees and for research funds granting institutions and decision-makers.

Importance of Weather Conditions in a Flight Corridor

Current research initiatives, such as the Single European Sky Air Traffic Management Research Program, call for an air traffic system with improved safety and efficiency records and environmental compatibility. The resulting multi-criteria system optimization and individual flight trajectories require, in particular, reliable three-dimensional meteorological information. The Global (Weather) Forecast System only provides data at a resolution of around 100 km. We postulate a reliable interpolation at high resolution to compute these trajectories accurately and in due time to comply with operational requirements. We investigate different interpolation methods for aerodynamic crucial weather variables such as temperature, wind speed, and wind direction. These methods, including Ordinary Kriging, the radial basis function method, neural networks, and decision trees, are compared concerning cross-validation interpolation errors. We show that using the interpolated data in a flight performance model emphasizes the effect of weather data accuracy on trajectory optimization. Considering a trajectory from Prague to Tunis, a Monte Carlo simulation is applied to examine the effect of errors on input (GFS data) and output (i.e., Ordinary Kriging) on the optimized trajectory.

Optimal location of hydraulic energy storage using geographic information systems and multi-criteria analysis

In recent years, society's concern for sustainable development and the environment has grown to the point where renewable energies have become one of the main pillars of this interest. One problem that arises when producing large quantities of renewable energies is the storage and the integration of this production into the grid, which, amongst other possible options, can be solved by means of reversible power plants or pumped storage. For all these reasons, in recent years there has been a growing interest in searching for locations in which to install these types of plants, which have high energy and territorial requirements. This research develops a methodological proposal that allows in the context of Geographic Information Systems to detect and evaluate the most appropriate places to implement this type of facilities in a quasi-automatic way. The methodology proposes a multi-criteria analysis that makes it possible to analyse large extensions of land with ten criteria that cover the energy and territorial requirements that all installations must have, duly weighted. The method assigns each reservoir a location coefficient that reflects the weighting of the ten criteria chosen, so that the value ordered from the highest to the lowest reflects the best to worst location. The proposed methodology is much more consistent than traditional methods because it uses a multi-criteria system and a statistically consistent weighting mechanism, as well as being objective and based on logical criteria. The analysis is general and once the location has been chosen, the corresponding detailed project will be carried out. The proposal is innovative because it involves carrying out the environmental and urban analysis during the decision-making process for the site.

Suitable and Available Land for Settlement Development in Cianjur Regency

A merging of Jakarta and Bandung metropolitans influence the development of the areas around them. Consequently, people are migrating to the cities caused an increasing population. It leads to experiencing uncontrolled advancement and arising problems. One of them is the raising land demand for settlement development, but the land is limited. Therefore, there is a need to address these particularities. This research aimed to model suitable and available land for settlement development in Cianjur Regency. Multi-criteria evaluation (MCE) and geographical information system (GIS) were collaborated to define the suitability criteria. The analytical hierarchy process (AHP) was employed to set the criteria's weight. Spatial Plan and LP2B were carried out as limiting factors. The result showed that the most crucial consideration came from the disaster. The suitability classes in Cianjur regency are: highly suitable 36%, Suitable 7%, Marginally suitable 44%, and Not suitable 14%. 85% of land in 2018 was available for settlement. Yet it is decreasing becomes 2% in 2030. Therefore, the potential land for settlement development is The different perspectives among developers, academics, and government in determining the most critical criteria for land suitability plays a crucial role in the next Cianjur’s settlement development planning. The implementation of the Spatial Plan can minimize environmental problems such as land conversion. In addition, regarding the new regulation on LP2B, the Spatial Plan has to be updated to synchronize the plan between Spatial Plan and LP2B.

Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment

Artificial intelligence, particularly machine learning, is the fastest-growing research trend in educational fields. Machine learning shows an impressive performance in many prediction models, including psychosocial education. The capability of machine learning to discover hidden patterns in large datasets encourages researchers to invent data with high-dimensional features. In contrast, not all features are needed by machine learning, and in many cases, high-dimensional features decrease the performance of machine learning. The feature selection method is one of the appropriate approaches to reducing the features to ensure machine learning works efficiently. Various selection methods have been proposed, but research to determine the essential subset feature in psychosocial education has not been established thus far. This research investigated and proposed methods to determine the best feature selection method in the domain of psychosocial education. We used a multi-criteria decision system (MCDM) approach with Additive Ratio Assessment (ARAS) to rank seven feature selection methods. The proposed model evaluated the best feature selection method using nine criteria from the performance metrics provided by machine learning. The experimental results showed that the ARAS is promising for evaluating and recommending the best feature selection method for psychosocial education data using the teacher’s psychosocial risk levels dataset.

Solving the problem of optimizing the resource efficiency of an enterprise using lean manufacturing technologies

The article provides a solution to the problem of studying the characteristics of resource efficiency for a given time interval in the form of solving an optimization problem (using dynamic programming methods and the method of recurrent Bellman equations. A multistep search for a solution was carried out for a given period of planning production activities. Boundary requirements were met in different ways due to the multivariate management decisions. The features of the optimization model were considered. The solution of the multistep problem is reduced to the sequential solution of several one-step optimization problems. The function of changes in resource efficiency in lean manufacturing technologies was set for the planning period. Changes in technologies for the capability involved demonstrate how resource efficiency changes with the observation time. It is noted thaqt the experience accumulation of experts and participants in the production process in the implementation of innovative approaches is of great importance for reducing all types of losses. It was revealed that one of the crucial parameters in a multi-criteria system is the preservation of efficient operation in the subsystem associated with intelligent decision support. The optimal amounts of costs have been determined. Their assessment was carried out to allow for the fact that when solving optimization problems, it is necessary to take into account such criteria for which the values take extrema. Assuming a successful solution of the optimization problem posed and analyzing the results obtained when dividing the multistep decision into three one-step ones, we received S-shaped functions with a function value equal to 6.8%

РЕШЕНИЕ МНОГОКРИТЕРИАЛЬНОЙ ЗАДАЧИ ОПРЕДЕЛЕНИЯ КРИТЕРИЕВ ЭФФЕКТИВНОСТИ ТРАНСПОРТНО-ЛОГИСТИЧЕСКОЙ СИСТЕМЫ

Work objective. The article considers the process of optimizing transport and logistics system in various areas of work and represents the task of solving a complex multi-criteria system. Determining the most optimal and accurate way to solve this problem will lead to a significant stimulation of the management processes of transport and logistics complexes, in particular trucking.
 Research methods. The research was carried out by analyzing the solution of multi-criteria mathematical problems. 
 Novelty of the work. The novelty of the work is due to the need to solve the main problems of optimizing the operation of the transport and logistics system. The solution of this task will stimulate the country's economy by increasing the volume of annual shipping goods, as this indicator is one of the key road transport complex of our country. 
 Research results and novelty. According to the results of the study, the main ways of solving the problem of optimizing the transport and logistics system are determined. 
 Conclusions: The main criteria for solving a multi-criteria task in optimizing the planning, organization and management of terminal and warehouse complexes of work are determined.