Search

This paper introduces the Watermark Imaging System (WImSy) which can be used to photograph, document, and study sheets of paper. The WImSy provides surface images, raking light images, and transmitted light images of the paper, all in perfect alignment. We develop algorithms that exploit this alignment by combining several images together in a process that mimics both the "surface image removal" technique and the method of "high dynamic range" photographs. An improved optimization criterion and an automatic parameter selection procedure streamline the process and make it practical for art historians and conservators to extract the relevant information to study watermarks. The effectiveness of the method is demonstrated in several experiments on images taken with the WImSy at the Metropolitan Museum of Art in New York and at the Getty Museum in Los Angeles, and the results are compared with manually optimized images. [ABSTRACT FROM AUTHOR]

Modern challenges such as the liberalization of the railway sector and growing demands for sustainability, high-quality services, and user satisfaction set new standards in railway operations. In this context, railway infrastructure managers (RIMs) play a crucial role in ensuring innovative approaches that will strengthen the position of railways in the market by enhancing efficiency and competitiveness. Evaluating their performance is essential for assessing the achieved objectives, and it is conducted through a wide range of key performance indicators (KPIs), which encompass various dimensions of operations. Monitoring and analyzing KPIs are crucial for improving service quality, achieving sustainability, and establishing a foundation for research and development of new strategies in the railway sector. This paper provides a detailed overview and evaluation of KPIs for RIMs. This paper creates a framework for RIM evaluation using various scientific methods, from identifying KPIs to applying complex analysis methods. A novel hybrid model, which integrates the fuzzy Delphi method for aggregating expert opinions on the KPIs' importance, the extended fuzzy analytic hierarchy process (AHP) method for determining the relative weights of these KPIs, and the ADAM method for ranking RIMs, has been developed in this paper. This approach enables a detailed analysis and comparison of RIMs and their performances, providing the basis for informed decision-making and the development of new strategies within the railway sector. The analysis results provide insight into the current state of railway infrastructure and encourage further efforts to improve the railway sector by identifying key areas for enhancement. The main contributions of the research include a detailed overview of KPIs for RIMs and the development of a hybrid multi-criteria decision making (MCDM) model. The hybrid model represents a significant step in RIM performance analysis, providing a basis for future research in this area. The model is universal and, as such, represents a valuable contribution to MCDM theory. [ABSTRACT FROM AUTHOR]

The advancement toward sustainable infrastructure presents complex multi-objective optimization (MOO) challenges. This paper expands the current understanding of design frameworks that balance cost, environmental impacts, social factors, and structural integrity. Integrating MOO with multi-criteria decision-making (MCDM), the study targets enhancements in life cycle sustainability for complex engineering projects using precast modular road frames. Three advanced evolutionary algorithms—NSGA-II, NSGA-III, and RVEA—are optimized and deployed to address sustainability objectives under performance constraints. The efficacy of these algorithms is gauged through a comparative analysis, and a robust MCDM approach is applied to nine non-dominated solutions, employing SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR decision-making techniques. An entropy theory-based method ensures systematic, unbiased criteria weighting, augmenting the framework's capacity to pinpoint designs balancing life cycle sustainability. The results reveal that NSGA-III is the algorithm converging towards the most cost-effective solutions, surpassing NSGA-II and RVEA by 21.11% and 10.07%, respectively, while maintaining balanced environmental and social impacts. The RVEA achieves up to 15.94% greater environmental efficiency than its counterparts. The analysis of non-dominated solutions identifies the A 4 design, utilizing 35 MPa concrete and B500S steel, as the most sustainable alternative across 80% of decision-making algorithms. The ranking correlation coefficients above 0.94 demonstrate consistency among decision-making techniques, underscoring the robustness of the integrated MOO and MCDM framework. The results in this paper expand the understanding of the applicability of novel techniques for enhancing engineering practices and advocate for a comprehensive strategy that employs advanced MOO algorithms and MCDM to enhance sustainable infrastructure development. [ABSTRACT FROM AUTHOR]

Digital financial inclusion and common prosperity are pivotal elements in promoting the sustainable socioeconomic development of China. This study introduces a novel Multi-Criteria Decision Analysis (MCDA) method to evaluate the Common Prosperity Index (CPI). Using this index, alongside the Digital Financial Inclusion Index (DFII) released by Peking University, it examines the evolution of the coupling coordination relationship between digital financial inclusion and common prosperity within the Yangtze River Delta (YRD) urban agglomeration from 2011 to 2021. By integrating gravity models and social network analysis, in this paper, we thoroughly investigate the spatiotemporal evolution characteristics of the spatial network of this coupling coordination relationship. The results indicate that both the DFII and CPI generally exhibit an upward trend, but the decline in the coupling degree reflects a weakened interaction strength between them. Specifically, Anhui significantly lags behind Jiangsu, Zhejiang, and Shanghai in the development of digital financial inclusion and common prosperity, indicating regional development imbalances. Furthermore, the strength of spatial connections in city coupling coordination has significantly increased, with Nanjing's siphon effect on cities in Anhui becoming markedly stronger, and the number of core cities in the network increasing, which demonstrates a geographical proximity feature in network development. Additionally, the overall network characteristics are transitioning towards higher density and "small-world" properties, suggesting a trend toward network stabilization. The disparity in centrality among cities has decreased, with an overall enhancement in centrality, where the spatial spillover effects from core areas such as Hangzhou-Ningbo, Nanjing-Changzhou, and Shanghai-Suzhou-Wuxi significantly promote the development of peripheral cities. Based on these findings, this paper proposes policy recommendations for the sustainable development of digital financial inclusion and common prosperity in the YRD region. [ABSTRACT FROM AUTHOR]

Abstract: Chemical pulp is one of the most important raw materials used in the paper industry. This material is known to make a significant contribution to the water footprint and cost of final paper products; therefore, chemical pulp is crucial in determining the competitiveness of final products'. Several studies have focused on these aspects, but there have been no previous reports on the integrated application of raw material water footprint accounting and costs in the definition of the optimal supply mix of chemical pulps from different countries. The current models that have been applied specifically to the paper industry are based mainly on general sectorial data; therefore, they cannot reflect the importance of the efficiency of the different processes in the supply chain of paper production. The objective of this study was to develop a multi-objective optimization model to identify the supply mix that minimizes the water footprint accounting results and costs of chemical pulp, thereby facilitating the assessment of the water footprint by accounting for different chemical pulps purchased from various suppliers, with a focus on the efficiency of the production process. Water footprint accounting was adapted to better represent the efficiency of pulp and paper production. A multi-objective model for supply mix optimization was also developed using multi-criteria decision analysis (MCDA). Water footprint accounting confirmed the importance of the production efficiency of chemical pulp, which affected the final results, with an average factor of 4.7 m3 wood/t paper. The MCDA that we developed was used to determine the optimal mix of chemical pulps from different countries, which demonstrated how the optimal mix changed when considering only one of the two variables. Herein, we also discuss the latest developments in impact assessments related to water based on a life cycle assessment, which should be used as a framework for the future development of the model that is presented. [Copyright &y& Elsevier]

Drone delivery in city logistics is gaining attention due to road congestion, environmental threats, etc. However, there are risks associated with using drones which can result in hazardous events, such as conflicts in the air, loss of control, and system failures. It is crucial to assess the risks involved in using different types of drones and choose the option with the lowest risk. The existence of different criteria important for this decision imposes the need to apply the multi-criteria decision-making (MCDM) method(s). This paper proposes a new hybrid model that combines the fuzzy Factor Relationship (FARE) method for obtaining the criteria weights and the Axial Distance-based Aggregated Measurement (ADAM) method for obtaining the final ranking of the alternatives. A single-rotor microdrone weighing up to 4.4 lb was chosen as the optimal solution, and after that, the most favorable are also the drones of this size (multi-rotor and fixed-wing microdrones). The establishment of a novel hybrid MCDM model, the identified risks, the set of criteria for evaluating the least risky drones, and the framework for prioritizing the drones are the main novelties and contributions of the paper. [ABSTRACT FROM AUTHOR]

Selecting emergency suppliers from a wide range of candidates based on their performance under each criterion can be regarded as a multi-criterion decision making (MCDM) problem. Existing MCDM models to solve the emergency supplier selection problem ignored situations where large-scale suppliers exist, the influence of criteria weights on the robustness of ranking results, and the influence of psychology of regret aversion and disappointment aversion on decision results. To make up for these deficiencies, this paper proposes an MCDM model to solve emergency supplier selection problem with large-scale alternatives. Firstly, to avoid the influence of criteria weights on ranking of alternatives, the Robustness, Correlation, and Standard Deviation (ROCOSD) method is introduced to determine objective weights of criteria based on three objectives. Secondly, the τ-balanced clustering method is applied to cluster large-scale alternatives into balanced clusters. Next, considering the psychology of regret aversion and disappointment aversion of decision makers, a two-stage method is proposed to rank alternatives, which identifies the optimal alternative within each cluster and forms a new cluster consisting of these optimal alternatives in the first stage, and selects the optimal alternative from the new-formed cluster in the second stage. A numerical case is given to validate the proposed model. [ABSTRACT FROM AUTHOR]

The superiority of BWM over other weighting methods for obtaining the weight values of the attributes is that it achieves high-confidence results with a reasonable number of pairwise comparisons. Although the best-worst method (BWM) is a well-known multi-criteria decision-making (MCDM) method that has been successfully utilized in almost all scientific areas to solve challenging real-life problems, no research has comprehensively examined the state-of-the-art in this regard. The present study depicts a detailed overview of publications concerned with BWM during the period 2015–2022. Based on the information obtained from the Scopus database, this work presents a big picture of current research on BWM. In other words, this paper analyzes the existing literature about BWM and identifies thematic contexts, application areas, emerging trends, and remaining research gaps to shed light on future research agendas aligning with those gaps. Further, the most recent BWM research is analyzed in the top ten scientific areas, from engineering to materials science. “Engineering”, “computer science”, and “business, management, and accounting” are the hottest fields of BWM research. China is the most active country regarding “engineering” and “computer science”, whereas India is the leader in “business, management, and accounting”. The study also reveals that there are still many research gaps in BWM research. The big picture taken in this study will not only showcase the current situation of BWM research but will also positively impact the direction and quality of new research. [ABSTRACT FROM AUTHOR]

Various factors influence wildfire probability, including land use/land cover (LULC), fuel types, and their moisture content, meteorological conditions, and terrain characteristics. The Adriatic Sea coastal area in Croatia has a long record of devastating wildfires that have caused severe ecological and economic damages as well as the loss of human lives. Assessing the conditions favorable for wildfires and the possible damages are crucial in fire risk management. Adriatic settlements and ecosystems are highly vulnerable, especially during summer, when the pressure from tourist migration is the highest. However, available fire risk models designed to fit the macro-scale level of assessment cannot provide information detailed enough to meet the decision-making conditions at the local level. This paper describes a model designed to assess wildfire risks at the meso-scale, focusing on environmental and anthropogenic descriptors derived from moderate- to high-resolution remote sensing data (Sentinel-2), Copernicus Land Monitoring Service datasets, and other open sources. Risk indices were integrated using the multi-criteria decision analysis method, the analytic hierarchy process (AHP), in a GIS environment. The model was tested in three coastal catchments, each having recently experienced severe fire events. The approach successfully identified zones at risk and the level of risk, depending on the various environmental and anthropogenic conditions. [ABSTRACT FROM AUTHOR]

This research investigates the optimal integration of Blockchain Technology (BT) in Supply Chain Management (SCM) within Chile's maritime ports. Utilizing fuzzy Logarithmic Methodology of Additive Weights (LMAW) and Double Normalization-based Multiple Aggregation Methods (DNMA), the study systematically identifies, prioritizes, and ranks key factors influencing BT adoption in SCM. The study's findings highlight crucial factors like enhanced transaction security, good supply chain practices, and risk management. Furthermore, it ranks the application of ports as prime candidates for BT integration. The research contributes theoretically by developing a hybrid model combining MCDA methods, and practically by guiding the strategic application of BT in the maritime logistics sector, aligning with the principles of Industry 5.0. This paper presents a novel approach that explores the utilization of BT in maritime supply chain management, incorporating MCDA in a vague environment. The research gap of this study lies in defining new contexts in both theoretical and practical literature reviews for extending the use of BT in SCM in the ports of Chile, according to Industry 5.0, to increase the efficiency and effectiveness of all aspects of operations in these places. The contribution of this research is applying hybrid MCDA methods in an uncertain environment to assist decision-makers (DMs) in better implementing BT in SCM in Chilean ports, according to Industry 5.0. [ABSTRACT FROM AUTHOR]

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs. [ABSTRACT FROM AUTHOR]

Determining criteria weights plays a crucial role in multi-criteria decision analyses. Entropy is a significant measure in information science, and several multi-criteria decision-making methods utilize the entropy weight method (EWM). In the literature, two approaches for determining the entropy weight method can be found. One involves normalization before calculating the entropy values, while the second does not. This paper investigates the normalization effect for entropy-based weights and Hellwig's method. To compare the influence of various normalization methods in both the EWM and Hellwig's method, a study evaluating the sustainable development of EU countries in the education area in the year 2021 was analyzed. The study used data from Eurostat related to European countries' realization of the SDG 4 goal. It is observed that vector normalization and sum normalization did not change the entropy-based weights. In the case study, the max–min normalization influenced EWM weights. At the same time, these weights had only a very weak impact on the final rankings of countries with respect to achieving the SDG 4 goal, as determined by Hellwig's method. The results are compared with the outcome obtained by Hellwig's method with equal weights. The simulation study was conducted by modifying Eurostat data to investigate how the different normalization relationships discovered among the criteria affect entropy-based weights and Hellwig's method results. [ABSTRACT FROM AUTHOR]

Optimization is a process of minimizing or maximizing a given objective function under specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are optimized within defined criteria. Numerous MOO techniques have been developed utilizing various meta-heuristic methods such as Evolutionary Algorithms (EAs), Genetic Algorithms (GAs), and other biologically inspired processes. In a cooperative environment, a Pareto front is generated, and an MOO technique is applied to solve for the solution set. On other hand, Multi-Criteria Decision Making (MCDM) is often used to select a single best solution from a set of provided solution candidates. The Multi-Criteria Decision Making–Pareto Front (M-PF) optimizer combines both of these techniques to find a quality set of heuristic solutions. This paper provides an improved version of the M-PF optimizer, which is called the elite Multi-Criteria Decision Making–Pareto Front (eMPF) optimizer. The eMPF method uses an evolutionary algorithm for the meta-heuristic process and then generates a Pareto front and applies MCDM to the Pareto front to rank the solutions in the set. The main objective of the new optimizer is to exploit the Pareto front while also exploring the solution area. The performance of the developed method is tested against M-PF, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), and Non-Dominated Sorting Genetic Algorithm-III (NSGA-III). The test results demonstrate the performance of the new eMPF optimizer over M-PF, NSGA-II, and NSGA-III. eMPF was not only able to exploit the search domain but also was able to find better heuristic solutions for most of the test functions used. [ABSTRACT FROM AUTHOR]

In today's competitive environment, industries are required to increase reliability and safety level of their equipment with reasonable cost. Consequently, it is essential to select the appropriate maintenance strategy. In the paper industry, pumping of cooked pulp requires abrasion- and sometimes corrosion-resistant pumps that are able to handle up to 70 % solids content. But the maintenance engineer and supervisor often face failures of the pump. Hence, this article describes the application of multi criteria decision-making (MCDM) technique for the selection of optimum maintenance strategy for pumps used in the paper industry. The proposed hybrid MCDM model comprises fuzzy analytical hierarchy process (FAHP), grey relational analysis (GRA), and technique for order preference by similarity to ideal solution (TOPSIS) technique. The FAHP is used to compute the criteria weights whereas GRA-TOPSIS is used for determining the ranking of alternatives. This study discusses four maintenance strategies: corrective maintenance, predictive maintenance, time-based preventive maintenance, and condition-based maintenance. Four main criteria-safety, cost, added value, and feasibility-have been used to evaluate the optimum maintenance strategy. [ABSTRACT FROM AUTHOR]

In most project portfolio selection (PPS) situations, the presence of multiple attributes and decision-maker preference is inevitable. As Multi-criteria Decision Analysis (MCDA) methods provide a framework well-suited to deal with these challenges in PPS problems, the use of MCDA methods in real-life PPS problems has increased in recent years. This paper provides a comprehensive literature review of the use of different MCDA methods and their individual or combined utilization with other modeling techniques to support PPS problems. First, we summarize how MCDA methods are used in different modeling approaches. Second, we examine the mathematical models that are generally used to combine MCDA with mathematical programming techniques to solve PPS problems with resource constraints. Third, we present the drawbacks of combined utilization and discuss recent advances. Finally, we visualize the summary of the reviewed papers as a decision tree to assist researchers and practitioners in the use of MCDA methods in a specific PPS context and propose some future research directions. [ABSTRACT FROM AUTHOR]

Pooling the opinions of the decision makers about premier alternative from a list of given choices is the most crucial phenomenon theory of decision making. The MSM operator is an effective approach and can identify the collective connection among various input viewpoints. So, tacking the fully benefit of MSM operator, this paper presents a new novel in depth investigation of MSM in context of Neutrosophic fuzzy variables. Initially we developed the structure of One-dimension uncertain linguistic interval valued neutrosophic fuzzy variables, deliberate their fundamental properties along with meaningful laws. Moreover, we encompass the MSM operator to the extent of one dimensional uncertain-linguistic Neutrosophic fuzzy variables and introduced some novel aggregations operators including one-dimensional uncertain linguistic interval-valued Neutrosophic fuzzy, weighted fuzzy and ordered weighted fuzzy McLaurin symmetric mean operator. The practical utilization of the proposed aggregation operators in business analytic are also included in the paper. In last comparison is made between current vs old studies which proved solidity and reliability of the new operator's vs existing operators in the literature. [ABSTRACT FROM AUTHOR]

The increasing frequency of wildfires has posed significant challenges to communities worldwide. The effectiveness of all aspects of disaster management depends on a credible estimation of the prevailing risk. Risk, the product of a hazard's likelihood and its potential consequences, encompasses the probability of hazard occurrence, the exposure of assets to these hazards, existing vulnerabilities that amplify the consequences, and the capacity to manage, mitigate, and recover from their consequences. This paper employs the multiple criteria decision-making (MCDM) framework, which produces reliable results and allows for the customization of the relative importance of factors based on expert opinions. Utilizing the AROMAN algorithm, the study ranks counties in the state of Arizona according to their wildfire risk, drawing upon 25 factors categorized into expected annual loss, community resilience, and social vulnerability. A sensitivity analysis demonstrates the stability of the results when model parameters are altered, reinforcing the robustness of this approach in disaster risk assessment. While the paper primarily focuses on enhancing the safety of human communities in the context of wildfires, it highlights the versatility of the methodology, which can be applied to other natural hazards and accommodate more subjective risk and safety assessments. [ABSTRACT FROM AUTHOR]

Over the past fifty years, the form and function of space habitat modules have remained largely unchanged. While cylindrical modules are relatively simple to transport and deploy, their limited size poses a challenge to their efficiency and usability. In contrast, large space habitat construction projects are costly and present logistical difficulties. The development of polyhedral modules could bridge the gap between these two approaches, allowing for modular, polyhedral units to be assembled and linked together to create larger habitats more efficiently and with less risk. However, identifying the most suitable polyhedron for use in space remains a critical question. Previous research has explored the construction of polyhedral modules, but little rigor has been applied to identifying the most optimal form. This is a crucial issue because the first module to be used will likely set the standard for subsequent modules and any disadvantages present at that time will be perpetuated. Therefore, it is essential to identify the optimal form prior to constructing prototypes. This research paper employs multi-criteria decision analysis and sensitivity analysis to compare various candidate polyhedra across several evaluation metrics, including number of faces, volume to surface area ratio, and joint stress, among several other quantitative and qualitative metrics. The results demonstrate that the Rhombic Dodecahedron is a particularly suitable candidate compared to other forms analyzed. Thus, the Rhombic Dodecahedron should be considered the standard polyhedral form for future research involving the development of polyhedral modules. • Rhombic Dodecahedron identified as optimal polyhedron for space habitat design. • Polyhedral modules surpass cylindrical counterparts in multiple criteria. • Novel multi-criteria analysis method allows cross-metric comparison. [ABSTRACT FROM AUTHOR]

Abstract: Retrofit process design can be seen as a multi-criteria decision-making (MCDM) problem aiming at selecting the best alternative for improving the performance of a chemical process. There is a growing consensus that environmental considerations, including the environmental impact associated with all life cycle stages of materials, should be integrated in chemical process design. Life cycle assessment (LCA) is a technique that allows quantification of those impacts. However, implementing this technique would increase the decision-making complexity and thus would necessitate systematic tools and methods. The analogy between LCA and MCDM has been recognized, but the systematic evaluation of LCA methodological choices and interpretation in this context is rarely discussed. The importance of those is demonstrated in this paper using a case study involving the implementation of de-inked pulp capacity and cogeneration and an integrated newsprint mill. [Copyright &y& Elsevier]

This study focuses on the objective assessment of sport development in socio-economic environments, considering the challenges faced by the industry. These challenges include disparities in regional investments, limited market participation, slow progress towards sports professionalization, and insufficient technological innovations. To tackle these challenges, we suggest implementing an integrated evaluation model that follows the DPSIR (Drivers, Pressures, States, Impacts, Responses) framework and incorporates comprehensive socioeconomic indicators. Subsequently, we utilized the Entropy power method and TOPSIS (Order Preference Technique for Similarity to an Ideal Solution, TOPSIS) analysis to comprehensively assess the progress of competitive sports development in 31 provinces and cities in China. Additionally, we recommended further developments in competitive sports and proposed precise strategies for promoting its growth. The framework and methodology developed in this paper provide an objective and scientifically based set of decision-making guidelines that can be adopted by government agencies and related industries in order to create successful plans that promote the sustainable growth of competitive sport. This is expected to bolster the nation's global influence, enhance social unity, and fuel economic expansion. The findings of this study offer policymakers valuable insights regarding competitive sports and can advance the development of the sports sector in China, thus making it a crucial driver of regional socio-economic progress. [ABSTRACT FROM AUTHOR]

In recent years, influencer marketing has taken over traditional brand advertisements on social media platforms, combining word-of-mouth marketing with celebrity endorsements. However, there has been limited academic research on the key success factors in influencer marketing. This paper used a hybrid MCDM model that integrates the Delphi method and the decision-making trial and evaluation laboratory (DEMATEL) approach. Through a two-stage empirical study, the research aims to explore the crucial success factors in influencer marketing. This study focuses on the Taiwanese market. The target respondents for the survey are consumers in Taiwan who have purchased products or services based on recommendations from key opinion leaders (KOLs) or internet celebrities. This systematic research framework not only pinpoints key factors that capture consumer attention towards influencers, but it also illustrates the inter-relationship of structure and improvement directions among these factors. According to results, the key factors include influencer reputation, credibility, degree of key opinion influence, attractiveness, popularity, consistency between influencers and brands, fan engagement level, and informativeness. Finally, businesses can consider five aspects to be the purpose of collaboration, product type, target audience for the product, characteristics of the influencer's creative content, and media attributes. By comprehensively considering these aspects, businesses can determine the appropriate format for collaborative content. This decision can then guide how influencers communicate with consumers, effectively conveying brand information to the relevant target audience. The research findings provide fresh and significant insights in the field of influencer marketing studies. [ABSTRACT FROM AUTHOR]

The capacity of transport infrastructure is one of the very important tasks in transport engineering, which depends mostly on the geometric characteristics of road and headway analysis. In this paper, we have considered 14 road sections and determined their efficiency based on headway analysis. We have developed a novel interval fuzzy-rough-number decision-making model consisting of DEA (data envelopment analysis), IFRN SWARA (interval-valued fuzzy-rough-number stepwise weight-assessment-ratio analysis), and IFRN WASPAS (interval-valued fuzzy-rough-number weighted-aggregate sum–product assessment) methods. The main contribution of this study is a new extension of WASPAS method with interval fuzzy rough numbers. Firstly, the DEA model was applied to determine the efficiency of 14 road sections according to seven input–output parameters. Seven out of the fourteen alternatives showed full efficiency and were implemented further in the model. After that, the IFRN SWARA method was used for the calculation of the final weights, while IFRN WASPAS was applied for ranking seven of the road sections. The results show that two sections are very similar and have almost equal efficiency, while the other results are very stable. According to the results obtained, the best-ranked is a measuring segment of the Ivanjska–Šargovac section, with a road gradient = −5.5%, which has low deviating values of headways according to the measurement classes from PC-PC to AT-PC, which shows balanced and continuous traffic flow. Finally, verification tests such as changing the criteria weights, comparative analysis, changing the λ parameter, and reverse rank analysis have been performed. [ABSTRACT FROM AUTHOR]

This research addresses the problem of multi-objective resource allocation or resource deficits, offering robust answers to planning decisions that involve the elementary question: "How is it done?". The solution to the problem is realized using the general scheme of multi-criteria decision-making in uncertain conditions. The bases of the proposed scheme are associated with the possibilistic approach, which involves the generalization of fuzzy sets from the classical approach to process the uncertainty of information to produce robust (non-dominated) solutions in multi-criteria analysis. Applying this general scheme makes it possible to reduce regions of decision uncertainty through the maximum use of available quantitative information. In the case where quantitative information analysis is insufficient to obtain a unique solution, the proposed approach presupposes the appropriation of qualitative data extracted from experts, who express their opinions considering their knowledge, experience, and intuition. The information on the qualitative character can be represented in diverse preference formats processed by transformation functions to provide homogeneous information for decision procedures used at the final decision stage. The presented results have been implemented within the system of multi-criteria decision-making under uncertain conditions described in the paper. Its functioning is illustrated by solving the typical problem in investment planning activities. [ABSTRACT FROM AUTHOR]

One of the crucial steps in the multi-criteria decision analysis involves establishing the importance of criteria and determining the relationship between them. This paper proposes an extended Hellwig's method (H_EM) that utilizes entropy-based weights and Mahalanobis distance to address this issue. By incorporating the concept of entropy, weights are determined based on their information content represented by the matrix data. The Mahalanobis distance is employed to address interdependencies among criteria, contributing to the improved performance of the proposed framework. To illustrate the relevance and effectiveness of the extended H_EM method, this study utilizes it to assess the progress toward achieving Sustainable Development Goal 4 of the 2030 Agenda within the European Union countries for education in the year 2021. Performance comparison is conducted between results obtained by the extended Hellwig's method and its other variants. The results reveal a significant impact on the ranking of the EU countries in the education area, depending on the choice of distance measure (Euclidean or Mahalanobis) and the system of weights (equal or entropy-based). Overall, this study highlights the potential of the proposed method in addressing complex decision-making scenarios with interdependent criteria. [ABSTRACT FROM AUTHOR]

Fused deposition modelling (FDM) is one of the mostly used additive technologies, due to its ability to produce complex parts with good mechanical properties. The selection of FDM process parameters is crucial to achieve good mechanical properties of the manufactured parts. Therefore, in this paper, a hybrid multi-criteria decision-making (MCDM) approach based on Preference Selection Index (PSI) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed for the selection of optimal process parameters in FDM printing of polylactic acid (PLA) parts. Printing temperature, layer thickness and raster angle were considered as input process parameters. In order to prove the effectiveness of the proposed hybrid PSI - TOPSIS method, the obtained results were compared with the results obtained with different MCDM methods. The obtained best option of process parameters was confirmed by other MCDM methods. The optimal combination of process parameters to achieve the maximal flexural strength, maximal flexural modulus and maximal compressive strength is selected using the hybrid PSI-TOPSIS method. The results show that the hybrid PSI-TOPSIS approach could be used for optimisation process parameters for any machining process. [ABSTRACT FROM AUTHOR]

The purpose of this manuscript is to develop a novel MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) method to solve the MCDM (Multiple Criteria Decision-Making) problems with completely unknown weights in the q-rung orthopair fuzzy (q-ROF) setting. Firstly, the new concepts of q-ROF Lance distance are defined and some related properties are discussed in this paper, from which we establish the maximizing deviation method (MDM) model for q-ROF numbers to determine the optimal criteria weight. Then, the Lance distance-based MAIRCA (MAIRCA-L) method is designed. In it, the preference, theoretical and real evaluation matrices are calculated considering the interaction relationship in q-ROF numbers, and the q-ROF Lance distance is applied to obtain the gap matrix. Finally, we manifest the effectiveness and advantage of the q-ROF MAIRCA-L method by two numerical examples. [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]

While biogas production offers promising solutions for waste management, energy diversification, and sustainable development, effective project implementation requires comprehensive evaluation criteria that encompass diverse aspects, such as the problem to be addressed, biodigester technology selection, business model development, investment considerations, and final product utilization. A preliminary study involving an integrative review of 58 articles yielded 499 unique criteria. These criteria were categorized into four groups: economic, environmental, social, and technical, encompassing a total of 39 subcriteria. Six stages of the biogas production cycle were considered in the analysis: project, initiation, biodigester type selection, location determination, operational cycle definition, and final product utilization. The analysis revealed that existing decision-making models often prioritize technical and economic considerations while neglecting broader social and environmental perspectives. This paper addresses this gap by proposing, for the first time, stage-specific, multicriteria decision-making (MDCA) models tailored to each phase of a biogas production cycle. These models empower project managers and policymakers to optimize resource allocation, minimize the environmental impact, maximize social benefits, and ensure project viability and profitability. The models' adaptability allows for tailored prioritization based on specific project requirements and contexts. This groundbreaking research fills a critical void in biogas decision making by bridging the gap between existing technical and economic model limitations and the growing need for truly sustainable project development. [ABSTRACT FROM AUTHOR]

Objective: To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology. Methods: This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury's imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value. Results: RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI's lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring. Conclusion: The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology. Critical relevance statement: The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation. Keypoints: • Radiology artificial intelligence lacks a comprehensive approach to value assessment. • The RADAR framework provides a dynamic, hierarchical method for thorough valuation of radiology AI. • RADAR advances clinical radiology by bridging the artificial intelligence implementation gap. [ABSTRACT FROM AUTHOR]

This paper introduces a pioneering model that merges coupled Markov chains (CMC) with the fuzzy analytic hierarchy process (FAHP) to enhance multi-criteria decision making (MCDM) amidst the dynamic interplay of criteria. Traditional MCDM frameworks often lack the granularity to manage the intricate and changing relationships among criteria. Our model addresses this gap by integrating fuzzy numbers into AHP, providing a nuanced means to handle the inherent uncertainty of decision criteria. The application of the Kronecker product in CMC enriches our approach, offering a data-driven analysis while mitigating excessive dependence on subjective expert opinion. A comprehensive numerical example underlines the model's improved decision-making accuracy and efficiency, marking a substantial advancement in MCDM methodologies. This research contributes to the field by presenting a sophisticated yet practical framework for dynamic decision analysis in complex uncertain environments. [ABSTRACT FROM AUTHOR]

Simple Summary: Beekeeping is an activity that supports agriculture and wildlife on earth, so it is very important to provide accurate information that is able to help in increasing the profitability of beekeeping and valorize pollination services. In this paper, a new methodology is proposed to assess beekeeping potential using Geographic Information Systems (GIS) and the Analytical Hierarchy Process. The proposed tool will support decision making by selecting the best apiary locations to maximize honey production and, consequently, the risks of losing bee colonies will be reduced. The Suitability map (apiary aptitude map) allows for validating the best locations for apiaries (considering the spatial and floristic resources use), and the aptitude for installing new apiaries or moving existing ones. In this sense, the application of Multi-criteria GIS Analysis, in the context of beekeeping planning, is a useful tool for the Beekeepers Association to manage honeybee resources within the territory in order to maximize their profit, and for government agencies to implement promotional measures and policies to maximize food support needs and mitigate spared diseases. Beekeeping management is greatly influenced by spatial factors (e.g., land use/land cover, roads, or electrical energy networks), so GIS are a powerful tool to overlap and relate a variety of spatial data levels and, consequently, a very useful tool for beekeeping activity planning. This study was developed within the intervention area of three controlled zones managed by Portuguese Beekeepers Associations. The methodology, based on multi-criteria decision analysis, integrates several criteria, such as hydrographic networks, road networks, soil occupation, solar radiation, and electromagnetic radiation sources. These criteria were proposed and evaluated through online questionnaires carried out with beekeepers. Concerning the selected criteria and the respective geographical data, the most relevant were land use/land cover and water availability, with a significance of 44% and 24%, respectively. The beekeeping suitability map enabled us to evaluate the degree of compliance for the actual location of apiaries, with 60% of the apiaries being installed in high potential areas. In the context of beekeeping planning, the potential of the techniques applied seems to be an important tool for optimizing the location of apiaries and the profitability of beekeeping. [ABSTRACT FROM AUTHOR]

In the ever-evolving landscape of blockchain-based smart healthcare, ensuring the security and integrity of patient data stands as an utmost priority. This paper is dedicated to unveiling the pivotal role of Neutrosophic sets theory within our methodology as we tackle the multifaceted challenges of safeguarding patient data. Central to our approach is the innovative integration of Neutrosophic sets theory, a mathematical framework adept at handling the inherent uncertainties and imprecisions often encountered in healthcare decision-making. Leveraging Neutrosophic sets, we construct a comprehensive evaluation model that accommodates the complexities of the smart healthcare environment. Our methodology harnesses Multi-Criteria Decision Making (MCDM) techniques, notably the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to systematically assess and rank service providers based on their proximity to ideal solutions. The Neutrosophic aspect comes to the forefront as we apply Neutrosophic sets in representing and managing decision-makers' judgments, which often exhibit varying degrees of truth, indeterminacy, and falsity. Furthermore, the Ordered Weighted Averaging (OWA) operator is strategically employed to aggregate these Neutrosophic judgments, accentuating the role of Neutrosophic sets in our decision fusion process. Our empirical study, firmly rooted in Neutrosophic sets theory, showcases the efficacy of this innovative model. It offers a structured and robust framework for healthcare organizations to fortify patient data security in the realm of blockchain-based smart healthcare systems. This paper advances the understanding of the indispensable role played by Neutrosophic sets in enhancing data security, thereby facilitating the adoption of blockchain technology within healthcare, while contributing to the burgeoning field of Neutrosophic Intelligence. [ABSTRACT FROM AUTHOR]

Researchers have addressed uncertainty in multicriteria decision making from the perspective of the performance values of the alternatives, weighting of the evaluation criteria, and the evaluation methods. Still, they are yet to address the uncertainty caused by the normalization approach. In this paper, we show that different normalization methods, namely sum normalization, min–max normalization, vector normalization, and maximization normalization, can result in different rankings of the alternatives while the performance values and weights are unchanged. We applied the grey system theory to address the problem of uncertainty in this study from three aspects: alternative performance values measurement, criteria weighting, and decision matrix/table normalization within a period. The grey hybrid normalization method is proposed as the main contribution in this paper. Then, we present the rankings of 48 cities under uncertainty to decide the location of a branch office of a Chinese electric vehicle manufacturer as a practical example based on the grey entropy weighting method and grey relational analysis with positive and negative references (GRA-PNR) within the period from the year 2019 to 2021. The research results using this approach ranked New York City the best, with a stock market capitalization of economy validity as the top contributor in terms of weighting. Finally, we used simple additive weighting with grey value and the technique for order of preference by similarity to ideal solution with grey value methods to validate the study results. [ABSTRACT FROM AUTHOR]

Quality function deployment (QFD) is now used in various fields, such as product development, design, manufacturing, planning, and quality management services, as a planning tool to achieve customer requirements and needs while improving performance and sustainability concerns. This paper presents a systematic literature review of multi-criteria decision-making (MCDM) methodologies integrated into QFD over the last year. In 2022, 56 research papers on planning strategies, the supply chain, and product development using QFD were published. Other fields such as energy, academia, and environment have also shown considerable interest in the integration of MCDM methodologies in QFD to improve decision-making processes. This research shows that the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solutions (TOPSIS) methodologies are mainly used to rank customer requirements and weigh their importance in the house of quality (HoQ) structure. The use of fuzzy logic has been incorporated into the correlation matrix to evaluate the relationships between customer requirements and technical requirements. Methodologies such as decision-making trial and evaluation laboratory (DEMATEL) and fuzzy cognitive maps are implemented to deal with contradictions, and they have also been used to rank engineering characteristics. In the field of energy and renewable technologies, only few studies related to the integration of MCDM methodologies in QFD were found, but it is forecasted that their use will be used more often as they offer improvements and benefits in the ocean energy sector. [ABSTRACT FROM AUTHOR]

The selection of unmanned aerial vehicles for different purposes is a frequent topic of research. This paper presents a hybrid model of an unmanned aerial vehicle (UAV) selection using the Defining Interrelationships Between Ranked criteria (DIBR), Full Consistency Method (FUCOM), Logarithm Methodology of Additive Weights (LMAW) and grey - Evaluation based on Distance from Average Solution (G-EDAS) methods. The above-mentioned model is tested and confirmed in a case study. First of all, in the paper are defined the criteria conditioning the selection, and then with the help of experts and by applying the DIBR, FUCOM and LMAW methods, the weight coefficients of the criteria are determined. The final values of the weight coefficients are obtained by aggregating the values of the criteria weights from all the three methods using the Bonferroni aggregator. Ranking and selection of the optimal UAV from twentythree defined alternatives is carried out using the G-EDAS method. Sensitivity analysis confirmed a high degree of consistency of the solutions obtained using other MCDM methods, as well as changing the criteria weight coefficients. The proposed model has proved to be stable; its application is also possible in other areas and it is a reliable tool for decision-makers during the selection process. [ABSTRACT FROM AUTHOR]

Energy, healthcare, electronics, transportation, ecology, and infrastructure are just a few of the areas that might greatly benefit from the use of new materials and systems. This study delves into the factors that should be taken into account when determining the possible effect of a certain substance or system. This analysis takes into account ecological, monetary, technical, health and safety, regulatory, commercial, and cooperative factors. Stakeholders may make more well-informed decisions and ensure that materials and systems are used to their maximum capacity if they take these into account. This paper used the concept of multi-criteria decision-making (MCDM) to deal with various criteria and factors. The VIKOR method is used as an MCDM method to rank the materials according to various criteria. The VIKOR method is integrated with the single-valued neutrosophic set to overcome uncertain information. This paper used eight criteria and ten materials to select the best one. The results show the cost criteria is the height weight in all criteria. [ABSTRACT FROM AUTHOR]

Purchasing food in a way that minimizes negative effects on the environment, society, and the economy is a growing trend in the food industry. Sustainable procurement is discussed in this study, along with its significance, important criteria, and advantages in the food business. Businesses may aid sustainable development, lessen their impact on the environment, provide aid to local communities, and keep up with shifting consumer expectations for sustainably and ethically produced food when they prioritize responsible sourcing practices. To effectively implement sustainable procurement in the food sector, this article stresses the need for teamwork, openness, and a long-term commitment to sustainability. So, this paper ranks the best supplier in sustainable procurement in the food business to achieve sustainability. The concept of multi-criteria decision-making (MCDM) is used in this paper to deal with the various criteria. This paper used the TOPSIS method as an MCDM tool to compute the weights of criteria and rank the suppliers. The TOPSIS method is integrated with the single-valued neutrosophic set to deal with uncertain and vague information. There are seven criteria and 10 suppliers in the food business are evaluated and ranked in this study. We obtained the environmental impacts as the best criteria in seven criteria. The goal of environmental impact prioritizing suppliers and products that minimize negative environmental impact. [ABSTRACT FROM AUTHOR]

Researchers have addressed uncertainty in multicriteria decision making from the perspective of the performance values of the alternatives, weighting of the evaluation criteria, and the evaluation methods. Still, they are yet to address the uncertainty caused by the normalization approach. In this paper, we show that different normalization methods, namely sum normalization, min–max normalization, vector normalization, and maximization normalization, can result in different rankings of the alternatives while the performance values and weights are unchanged. We applied the grey system theory to address the problem of uncertainty in this study from three aspects: alternative performance values measurement, criteria weighting, and decision matrix/table normalization within a period. The grey hybrid normalization method is proposed as the main contribution in this paper. Then, we present the rankings of 48 cities under uncertainty to decide the location of a branch office of a Chinese electric vehicle manufacturer as a practical example based on the grey entropy weighting method and grey relational analysis with positive and negative references (GRA-PNR) within the period from the year 2019 to 2021. The research results using this approach ranked New York City the best, with a stock market capitalization of economy validity as the top contributor in terms of weighting. Finally, we used simple additive weighting with grey value and the technique for order of preference by similarity to ideal solution with grey value methods to validate the study results. [ABSTRACT FROM AUTHOR]

This paper proposes the method pELECTRE Tri, a novel probabilistic Multi-Criteria Decision Making (MCDM) method using the traditional ELECTRE Tri, probability distribution, and Monte Carlo simulation to support informed decision-making in complex and uncertain decision environments. The proposed method is illustrated through a case study involving the renovation of three social housing buildings. The paper provides new insights into the application of probabilistic sorting MCDM in the context of energy efficiency in buildings and highlights the benefits of using probabilities rather than crisp values to categorize alternatives enabling stakeholders to make better use of available resources, especially when dealing with a large dataset of energy measures with different features. The methodology implemented in Python (DOI: 10.5281/zenodo.7967655) is available as an open source. [ABSTRACT FROM AUTHOR]

Plithogenic sets coined by Smarandache in the year 2018 has unveiled new research opportunities in the field of Multi criteria decision making (MCDM). The contributions and developments of new decision making approaches based on plithogeny is gaining high momentum presently. The theoretical conceptualization of different phenomenon with plithogenic sets are also applied in designing optimal solutions to the decision making problems. This review paper presents the applications of plithogenic MCDM from the year 2018 to till date in almost all the spheres of decision making scenario. The literature works of the researchers presented in this paper will certainly portray the compatibility and flexibility of plithogenic sets, operators and other decision making tools. Though the time span considered for counting on the plithogeny based works is short, the applications of plithogenic sets are growing many in number and also plithogeny based theories are amplifying in a speedy manner. This has motivated the authors to investigate on the proliferation of plithogeny applications in decision making. This review paper has focused on the dimensions of different fields to which plithogeny is applied, new plithogeny based theories, extension of plithogeny, plithogenic based operators and measures. In addition to it the data on the publications of plithogeny based articles and interests of researchers are also presented as a part of this review work. The overall impact of plithogeny in the arena of decision making science and on the researchers of the same field is well sketched in this paper with the intention and hope of inspiring plithogenic researchers. [ABSTRACT FROM AUTHOR]

Microemulsion fuels, also known as surfactant-free fuels, are fuels made from a combination of two immiscible liquids, hydrocarbon fuel, and water, with a trace quantity of a co-solvent. Surfactants are often used in conventional microemulsion fuels, however surfactant-free microemulsion fuels instead depend on the thermodynamic features of the combination to generate a stable emulsion. In this paper, the multi-criteria decision-making (MCDM) model for choosing microemulsion fuel with surfactant-free. Various helpful and harmful criteria were evaluated for ten fuels at varying motor loads, according to performance and emission characteristics. This paper integrated the neutrosophic set with the TOPSIS method. The neutrosophic set is used to deal with uncertain data. The TOPSIS method is used to rank the different fuels. [ABSTRACT FROM AUTHOR]

One of the most important challenges when building road infrastructure is the selection of appropriate mechanization, on which the efficiency of construction and the life of exploitation depends largely. As construction machinery, pavers occupy a significant place in civil engineering projects, so their selection, depending on a road category, is a very important activity. The objective of this paper is to develop an intelligent Fuzzy MCDM (Multi-Criteria Decision-Making) model, which consists of the integration of D and Z numbers for the selection of construction machinery. The IMF D-SWARA (Improved Fuzzy D Step-Wise Weight Assessment Ratio Analysis) method was used to determine weighting coefficients. A novel Fuzzy ARAS-Z (Additive Ratio Assessment) method has been developed to determine an adequate paver for a lower category of roads (asphalt width up to 5 m), which represents an important contribution and novelty of the paper. A total of 10 alternatives were evaluated based on 16 criteria which were classified into 4 main groups. The results have shown that the alternative A8—SUPER 1300-3 represents a paver with the best characteristics for the considered set of parameters. After that, verification tests were calculated, and they include a comparative analysis with four other MCDM methods based on Z numbers, a change in the normalization procedure, and the impact of changing the size of an initial fuzzy matrix. The tests showed the stability of the developed model with negligible deviations. [ABSTRACT FROM AUTHOR]

Multi-criteria decision-making (MCDM) methods have gained increased attention in sustainable engineering, where complex decision-making problems require consideration of multiple criteria and stakeholder perspectives. This review paper provides a comprehensive overview of the different MCDM methods, their applications in sustainable engineering, and their strengths and weaknesses. The paper discusses the concept of sustainable engineering, its principles, and the different areas where MCDM methods have been applied, including energy, manufacturing, transportation, and environmental engineering. Case studies of real-world applications are presented and analyzed, highlighting the main findings and implications for engineering practice. Finally, the challenges and limitations of MCDM methods in sustainable engineering are discussed, and future research directions are proposed. This review contributes to the understanding of the role of MCDM methods in sustainable engineering and provides guidance for researchers and practitioners. [ABSTRACT FROM AUTHOR]

Mobility of military vehicles is a key element of military transport and it guarantees safety of the logistic system of the Armed Forces. The aim of this article is to compare medium-load and high-mobility means of transport belonging to the Polish Armed Forces and to identify the vehicle with the best parameters. The Analytic Hierarchy Process method was used for this, and this paper approximates its assumptions and process. Seven vehicles currently in use by the Polish Armed Forces were compared. As a result of the analysis, a hierarchy of the studied vehicles was established, ranking them from the highest to the lowest rated. The paper allows to assess whether the currently purchased Jelcz 442.32 is a good choice in the procuring policy of the Polish army. [ABSTRACT FROM AUTHOR]

A large number of materials and various criteria fashion material selection problems as complex multi-criteria decision-making (MCDM) problems. This paper proposes a new decision-making method called the simple ranking process (SRP) to solve complex material selection problems. The accuracy of the criteria weights has a direct impact on the outcomes of the new method. In contrast to current MCDM methods, the normalization step has been eliminated from the SRP method as a potential source of producing incorrect results. The application of the method is appropriate for situations with high levels of complexity in material selection because it only considers the ranks of alternatives in each criterion. The first scenario of vital-immaterial mediocre method (VIMM) is used as a tool to derive criteria weights based on expert assessment. The result of SRP is compared with a number of MCDM methods. In order to evaluate the findings of analytical comparison, a novel statistical measure known as compromise decision index (CDI) is proposed in this paper. CDI revealed that the MCDM methods' outputs for solving the material selection could not be theoretically proven and requires to be evaluated through practice. As a result, the dependency analysis-an additional innovative statistical measure is introduced to demonstrate the reliability of MCDM methods by assessing its dependency on criteria weights. The findings demonstrated that SRP is extremely reliant on criteria weights and its reliability rises with the number of criteria, making it a perfect tool for solving challenging MCDM problems. [ABSTRACT FROM AUTHOR]

Properly assigning weights to decision makers in a group decision making environment is one of the essential processes of utmost importance as it can have a direct effect on the final outcome. This paper, basing on the entropy method, proposes a correct and efficient way to objectively assign weights to decision makers in a multi-criteria group decision making environment. Two approaches namely, constant and variable weighting, have been put forward whilst providing necessary proofs and numerical examples in order to show one of the existing ways in literature is erroneous as it is not resulting in weights that reflects entropy method’s basic intuition. [ABSTRACT FROM AUTHOR]

Hellwig's method is a multi-criteria decision-making technique designed to facilitate the ranking of alternatives based on their proximity to the ideal solution. Typically, this approach calculates distances using the Euclidean norm, assuming implicitly that the considered criteria are independent. However, in real-world situations, the assumption of criteria independence is rarely met. The paper aims to propose an extension of Hellwig's method by incorporating the Mahalanobis distance. Substituting the Euclidean distance with the Mahalanobis distance has proven to be effective in handling correlations among criteria, especially in the context of asymmetrical relationships between criteria. Subsequently, we investigate the impact of the Euclidean and Mahalanobis distance measures on the several variants of Hellwig procedures, analyzing examples based on various illustrative data with 10 alternatives and 4 criteria. Additionally, we examine the influence of three normalization formulas in Hellwig's aggregation procedures. The investigation results indicate that both the distance measure and normalization formulas have some impact on the final rankings. The evaluation and ranking of alternatives using the Euclidean distance measure are influenced by the normalization formula, albeit to a limited extent. In contrast, the Mahalanobis distance-based Hellwig's method remains unaffected by the choice of normalization formulas. The study concludes that the ranking of alternatives is strongly dependent on the distance measure employed, whether it is Euclidean or Mahalanobis. The Mahalanobis distance-based Hellwig method is deemed a valuable tool for decision-makers in real-life situations. It enables the evaluation of alternatives by considering interactions between criteria, providing a more comprehensive perspective for decision-making. [ABSTRACT FROM AUTHOR]

Population growth has become a serious problem in many countries, especially Egypt. Which leads to an increase in the population area and an increase in buildings, which then leads to several problems, including large energy consumption, increased pollution, traffic congest ion, and others. Therefore, many governments have resorted to using technology and applying it to build smart buildings to help save energy by using renewable energy to improve its impact on the environment, improve the quality of life of citizens, provide security and safety, and so on. The selection of smart buildings depends on many criteria. Since this problem is described as a multi-criteria decision-making (MCDM) problem, MCDM methods will be used in this paper. A hybrid method is presented to evaluate smart buildings. The first method, MEREC, was used to calculate the weights of criteria, and the VIKOR model was used for ranking alternatives. Then applying those weights to the CoCoSo, COPRAS, and TOPSIS methods for making comparisons using Spearman's correlation coefficients for ranking these four methods. All methods used are applied in the T2NN environment. [ABSTRACT FROM AUTHOR]

Single-cell protein (SCP) is a promising alternative for replacing plant and animalderived dietary proteins. SCP contains essential nutrients and high levels of essential amino acids (AA). Given the versatility of microbial strains and waste substrates that can be used as feedstocks, many variations of production processes can be explored. Improving these microorganism strains by enhancing their properties and productivity is vital to increasing SCP competitiveness. One of the options to enhance microorganism strains would be by creating mutants with better AA profiles. By using mutagenesis and AA inhibitors it should be possible the create novel strains with improved AA-producing properties. The use of AA inhibitors to promote selective pressure on SCP-producing strains is a novel concept and is not a widely explored approach, therefore, the further development of this method should be explored. This paper used a multi-criteria decision analysis method to evaluate different technological factors vital for creating protein-rich mutants. These factors are microorganism strains, agro-industrial waste substrates used as process feedstocks, AA inhibitors, and mutagenesis methods. Microorganisms Candida utilis and Bacillus subtilis showed the highest potential for being used. Molasses was the 'closest to the ideal' substrate to be used as feedstock for SCP production. As the most promising mutagenesis method ethyl methane sulphonate was selected. Glufosinate ammonium and methionine sulfoximine for both bacteria and fungi were identified as the best inhibitors for SCP-rich mutant selection. Identified combinations of optimal solutions for microorganisms, substrates, inhibitors, and mutagenesis techniques should be further investigated and evaluated in laboratory settings. This could help to increase SCP's competitiveness as a sustainable protein source. [ABSTRACT FROM AUTHOR]

The renewable energy transition of oil- and gas-producing countries has specific peculiarities due to the ambivalent position of these countries in the global energy market, both as producers and consumers of energy resources. This task becomes even more challenging when the share of oil and gas in the country's GDP is very high. These circumstances pose serious challenges for long-term energy policy development and require compromising decisions to better align the existing and newly created energy policies of the country. The scale, scope, and pace of changes in the transition process must be well balanced, considering the increasing pressure of economic and environmental factors. The objective of this paper is to develop models that allow the selection of the most appropriate scenario for renewable energy transition in an oil- and gas-producing country. The distinguishing feature of the proposed model is that alternatives in the decision matrix are presented as scenarios, composed of a set of energy resources and the level of their use. Linguistic descriptions of the alternative scenarios are formalized in the form of fuzzy statements. For the problem solution, four different Multiple-Criteria Decision-Making (MCDM) methods were used: the fuzzy simple additive weighting (F-SAW) method, the distance-based fuzzy TOPSIS method (Technique of Order Preference Similarity to the Ideal Solution), the ratio-analysis-based fuzzy MOORA method (Multi-Objective Optimization Model Based on the Ratio Analysis), and the fuzzy multi-criteria optimization and compromise solution method VIKOR (Serbian: VIekriterijumsko Kompromisno Rangiranje). This approach is illustrated using the example of the energy sector of Azerbaijan. The recommended solution for the country involves increasing natural gas (NG) moderately, maintaining hydro, and increasing solar notably and wind moderately. [ABSTRACT FROM AUTHOR]