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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]

Taking as an example the very interesting and motivating paper by Sousa Ribeiro et al. (2021) an attempt is made of providing a couple of insights into the decision making process from the point of view of the potentially helpful aspects of data analysis and OR-related modelling. These are just hints and suggestions, meant primarily to emphasise the multifaceted character of the decision making situations and processes, especially when concerning more complex issues. While the course of the procedure proposed and exemplified in Sousa Ribeiro et al. (2021) is treated as fully correctly and successfully carried out to the end, we wish to show the potential use of information, constituting in a sense a "by-product" of such a procedure, or, actually, of any similar procedure, aimed at supporting decision making. [ABSTRACT FROM AUTHOR]

Wang, and J.-T. Teng in their paper develop and compare the seller's profit per unit time under each of three payment methods - advance, cash, and credit. In their paper, S. Sarkar, S. Tiwari, and B. C. Giri present a continuous-review vendor-buyer supply chain (SC) model and uncovers the best policy that minimizes the system's total expected cost. The authors investigate the contracts that the manufacturer offers to the retailers and finds the optimal pricing and ordering decisions made by retailers and the best contract that includes maximum profit for the supply chain. The analysis of large-scale data generated by humans, online activity in different web and social platforms is a challenging problem faced by researchers, practitioners, and academicians. [Extracted from the article]

The European Higher Education Area has ushered in a significant shift in university teaching, aiming to engage students more actively in classes. Professors have leveraged virtual platforms and external tools to introduce interactive tasks. With the proliferation of technology, educators face a challenge in choosing the most suitable approach. This paper presents SMART (Selection Model for Assessment Resources and Techniques), a methodology that determines the optimal assessment activities for university-level education. The methodology employs multicriteria decision-making techniques, specifically AHP and TOPSIS methods, to optimize activities based on various subject-, lecturer-, activity-, and student-related criteria. According to SMART, the top five assessment tasks are group and individual report submissions, workshops, complex H5P activities, and questionnaires. Therefore, it is advisable to prioritize these activities based on the methodology's results, emphasizing their importance over other assessment methods. [ABSTRACT FROM AUTHOR]

The article announces the winner of the 2023 Clemen-Kleinmuntz Decision Analysis Best Paper Award, which comes with a $2,000 prize. The winning paper, titled "Information Density in Decision Analysis," introduces the concept of "information density" as a new approach to sensitivity analysis in decision analysis. The paper provides a technical grounding of information density and offers examples for real-world implementation. Another paper, titled "An Empirical Comparison of Rank-Based Surrogate Weights in Additive Multiattribute Decision Analysis," was identified as a finalist for the award and provides valuable insights into converting ordinal rankings into attribute weights. The article encourages authors to continue submitting high-quality manuscripts for future consideration. [Extracted from the article]

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]

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]

The goal of this paper is to provide a comprehensive overview of application of multi-criteria decision making (MCDM) methods in papers from the field of ecotourism and sustainable tourism. A search has been done of the relevant terms in titles, abstracts and keywords found in papers from 26 prominent journals from the field of tourism belonging in Web of Science (WoS) Clarivate Analytics. It has been established which MCDM methods were used and who are the most common authors of such papers. A keyword frequency analysis was also performed. It is established there are 39 papers in the field of ecotourism and sustainable tourism where MCDM methods were applied whereby all were published after the year 2000, as well as that their number is constantly increasing. [ABSTRACT FROM AUTHOR]

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

The article focuses on the fourth issue of the Publisher in 2023, featuring 10 papers from various countries. Topics include enhanced ultrasound classification of microemboli using convolutional neural networks, a hybrid metaheuristic optimization algorithm for global optimization and data classification, and a novel tolerance-based moderator-guided heterogeneous group decision-making involving experts and end-users.

The 2017 International Conference on Multiple Objective Programming and Goal Programming (MOPGP 2017) has been organized to disseminate recent theoretical and methodological developments, significant technical applications, case studies and survey results in the related area. Multiple Objective Programming (MOP), also known in literature as multi-objective optimization, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization, is a subarea of Multiple Criteria Decision Making (MCDM) or Multiple Criteria Decision Analysis (MCDA) and it is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. If we suppose that HT ht is ordered by the classical Pareto cone HT ht , then HT ht if HT ht . [Extracted from the article]

The aim of this paper is to present a decision support system (DSS) to capture the complexity of the transition of a national energy system to net zero in the context of multiple sustainability themes. The paper proposes an integrated assessment framework that combines dynamic systems modeling, sustainability indicators, and multi-criteria decision analysis (MCDA) with direct stakeholder involvement. To illustrate the use of the DSS, the paper compares bundles of climate change policies that aim to decarbonize the road transport sector in Iceland. Eighteen scenarios and alternative development trajectories are defined for the Icelandic energy system based on a combination of three main driving forces. These are, firstly, economic development (three cases); secondly, changes in energy efficiency (two cases); and finally, three climate policy bundles aimed at increasing the share of electric vehicles. Based on the results from the integrated assessment framework, the performance scores of the climate policy bundles are compared across the following five sustainability themes: social impact; economic development; environmental impact; energy security; and technical aspects. The findings confirm that a different conclusion may be reached when multiple sustainability themes are applied in the selection of preferred policy bundles as compared to conventional techno-economic criteria. Banning the registration of fossil-fueled vehicles, combined with economic instruments, offers the best decarbonizing strategy to reach climate and energy policy goals simultaneously. [ABSTRACT FROM AUTHOR]

In the current landscape of higher education, the quality of teaching plays a crucial role in supporting the comprehensive development of students. To ensure the effectiveness of the learning process, evaluating lecturers based on student opinions is an essential means of providing feedback and optimizing the learning experience. This paper focuses on constructing a lecturer ranking system, particularly in the context of a specific course through the evaluation process from students. Four different methods were employed to assess lecturers, including the PSI method, SRP method, RAM method, and PIV method. The evaluation results using these four methods were compared with each other and also with the traditional evaluation approach currently utilized in the educational institution. The achieved results demonstrate that the approach outlined in this paper is highly suitable for determining the rankings of lecturers when teaching individual courses. [ABSTRACT FROM AUTHOR]

The sudden collapse of a school roof in the UK brought widespread attention to the structural integrity of buildings constructed with reinforced autoclaved aerated concrete (RAAC), a material widely used from the 1950s to the mid-1990s. RAAC, known for its lightweight and insulating properties, has been found to suffer from weak compressive strength, poor reinforcement anchorage, and high susceptibility to environmental degradation. The structural profiles of RAAC panels in the UK are unique, particularly in their reinforcement configurations and failure modes, which limits the applicability of the existing literature from other regions. This paper conducts a state-of-the-art review, identifying a significant gap in current research due to the unique challenges posed by RAAC in the UK, and highlights the need for novel methodologies. In response to this gap, the paper introduces a multi-criteria decision analysis (MCDA) framework utilising the decision-making trial and evaluation laboratory (DEMATEL) method to assess the interdependencies of RAAC defects. This methodology quantifies the influence of observed defects and guides the selection of appropriate remediation strategies, offering a more structured and objective approach to RAAC panel assessment and retrofitting. Practically, this study aligns with ongoing research efforts towards the digitalisation of RAAC management by integrating the MCDA model within digital asset management systems. This integration supports a holistic approach to addressing the RAAC crisis, enhancing current efforts to digitalise the surveying and management processes and ensuring safer long-term solutions. [ABSTRACT FROM AUTHOR]

Over the last few decades, researches related to the impact of production and industrial manufacturing on environmental sustainability, coupled with multi-dimensional multi-criteria decision analysis and performance evaluation has consistently gained importance. However, no significant work in which an effort to optimize the total production process with the consideration of the positive and negative impacts of each input and output on Environmental sustainability has been noticed. The main aim of our paper is to address this gap. In this paper, an effort has been made to develop a realistic optimization model wherein a manufacturing firm has been considered to function with several decision-making units taking into account the effects of factors like profit, employment generation, social development, environmental pollution and sustainability. The various inputs employed and outputs produced by the firm are responsible for having negative impacts on the environment, besides creative value generation. With due consideration to both these values instead of mere profit, the model determines and explores the firm's decision considering different objectives. The optimization problem has been solved by a GA developed by us and demonstrated with numerical examples. The solutions of the numerical examples point towards some interesting revelations and indicate some prospective future research directions which contributes to this field of research. [ABSTRACT FROM AUTHOR]

This paper contributes to the expansion of green energy production, which is being pursued in order to mitigate climate change and carbon emissions from energy production. It addresses the delineation of areas that are suitable for the application of photovoltaics in the context of agricultural areas, water bodies, and parking spaces. Three specific photovoltaic types are examined in order to find out which criteria influence their geographic potential and whether spatial multi-criteria decision analysis methods are suitable for identifying suitable areas. The proposed approach consists of four steps: (1) collecting factors through expert interviews and questionnaires; (2) mapping the criteria to the spatial datasets; (3) deriving weighted scores for individual criteria through expert interviews; (4) applying the multi-criteria decision analysis method to compute and aggregate the final scores. We test our methodology at selected sites in the state of Styria, Austria. The test sites represent the topographical characteristics of the state and are about 5% of the size of Styria, approximately 820 km2. The key contributions are a weighted set of criteria that are relevant for the geographic potential of alternative photovoltaic types and the developed methodology to determine this potential. [ABSTRACT FROM AUTHOR]

As the number of alternative machines has increased and their technology has been continuously developed, the machine selection problem has attracted many researchers. This article reviews recent developments in applying multi-criteria decision-making (MCDM) methods for selecting machines in the manufacturing and construction industries. Selected articles are classified according to the application area and the applied MCDM method. By focusing on the last five years, this paper identifies recent trends in developing and using these methods. Results suggest that there has been a noticeable growth in the utilization of MCDM techniques for machine selection problems in both sectors. It is also noted that several decision-support tools and methods have been developed and successfully applied during this period. Accordingly, needs and directions for future research are discussed. [ABSTRACT FROM AUTHOR]

In papers published in Journal of Civil Engineering and Management, many applications of fuzzy logic can be found. To commemorate the contributions of the fuzzy set theory proposed by Professor Lotfi Zadeh to the fields of civil engineering and construction management, seven papers were selected for this special issue. These papers combined fuzzy set theory with appropriate methods to solve specialized problems in the fields of civil engineering and construction management, and provided good insights for the applications of fuzzy technology in solving problems related to civil engineering and construction management. [ABSTRACT FROM AUTHOR]

The article presents the discussion on considering uncertain and imprecise data in the decision-making process. Topics include MADM methods being the most frequently applied method on strategy management and supply chain; and enhancing inventory management using BlockChain Technology under cloud sector enabled by Hybrid Multi-Verse with Whale Optimization Algorithm.

For the successful remediation of polluted sites, priority setting among the possible remediation technologies is of major importance. The related decisions are typically conditioned by a limited set of alternative remediation techniques and multiple, often contradicting criteria. These characteristics make the decision problem suitable for applying a formalised discrete multi-criteria decision analysis (MCDA). This paper reports on the outcome of a systematic review of articles published between 1995 and 2020 encompassing 43 MCDA applications to support the selection of the remediation technique for polluted soils. It focuses on the comparison between implementations of the MCDA methodology. The review identifies four gaps where progress can be made to mobilise the full strength of the MCDA methodology to support the remediation of polluted soils: (i) early stakeholder engagement, (ii) inclusion of social criteria, (iii) an informed choice of the weighting and aggregation method, (iv) and sensitivity analysis. [ABSTRACT FROM AUTHOR]

Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013-22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi's L9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decisionmaking tools. [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]

This paper presents a model for assessing the impact of various factors on maritime accidents. This paper discusses the issue of maritime transport and its risks. The taxonomy of causes and consequences used in maritime transport is explained. Two mathematical principles were used, i.e., multi-criteria decision analysis (MCDA) and principal component analysis (PCA). The analyses were carried out using the Promethee/Gaia method. The connection between causes and accidents constituted the decision problem. The evaluation criteria, a set of actions and preferences, were identified based on the method principles. The results of this research include rankings of causal chains. They were analysed in multi-criteria and single-criteria dimensions. The outcomes are presented numerically and graphically. Such research contributes to improving safety at sea. It allows us to understand how a particular transport system works. Conclusions can be drawn and measures can be initiated to change the situation in the future. [ABSTRACT FROM AUTHOR]

In this paper, we consider multi-criteria decision-making problems of selecting the best decision alternative in which the preferences of the decision maker are given in the form of quasi-orders. If the decision maker's preferences are modeled by a family of complete quasi-orders (in particular, by a parametric family of additive value functions) then a common approach to solving such problems is based on the notion of potential optimality. In this paper, we consider the case in which the decision maker's preferences are modeled by a family of incomplete quasi-orders. We suggest a new approach to selecting decision alternatives by introducing the notion of potential nondominated alternatives. Such alternatives can be regarded as candidates for the best alternative. We investigate properties of the sets of potential nondominated alternatives and, for the case of a parametric family of partial quasi-orders, we develop a method of their identification. Our development is illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

Over the last few decades, Multi-criteria Decision Analysis (MCDA) techniques have been successfully applied to complex decision-making problems in a wide range of fields, such as economics, finance, logistics, environmental restoration, health or industrial organization, to name but a few, and imprecision and uncertainty have been incorporated into the decision-making process and applied to group decision-making contexts. The paper by Segura et at. [[7]] proposes models to solve real-world supplier evaluation problems and validate them with real data on fresh fruits in a supermarket chain. It is hoped that the international scientific community will find these selected research papers impactful and that these papers will motivate further research on MCDA for solving complex problems in various disciplines and application fields. [Extracted from the article]

Purchasing occupies a strategic role in supply chain management for a firm and is the driver of competitive advantage. Owing to the high purchase cost to revenue ratio, decisions such as evaluation, selection, and performance management of suppliers are of the matter of immense interest to firms. Multi-criteria decision making tools allow the purchasing managers to evaluate the suppliers holistically. One such tool, data envelopment analysis (DEA) has been used extensively for supplier evaluation and selection. This paper presents a comprehensive review of 161 articles published since 2000, on the application of DEA in supplier selection. These articles are located from the Scopus database. With little existing literature on a full-fledged review, this work envisages to be first of its kind, by aiding DEA practitioners in purchasing function. The analysis of the study indicates the emergence of the theme of green supply chain and sustainability in recent years as well as the adoption of hybrid approaches to solving the problem of supplier selection using DEA. The paper presents various classifications of DEA methods based on input criteria, sectors of application, and industry-wide case studies, which can be used as a quick reckoner by an academician or a purchasing manager. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce systems, the centralized service model gradually fails to meet the needs of SMEs. In the existing centralized e-commerce system, users' transaction data and reputation scores are stored in a centralized cloud server, which has high storage cost, low processing efficiency, and the data is vulnerable to attacks and leaks. However, the existing decentralized e-commerce systems more than its reputation system to store the average credit score evaluation are receiving unfair evaluations against risk. The system of malicious nodes and no disciplinary measures, is not conducive to system development. To solve this problem, this paper proposes a blockchain-based decentralized e-commerce transaction system. The system commodity information is stored in the Interplanetary File System (IPFS) and the returned commodity addresses are stored in the blockchain to enhance the service performance. This paper proposes a reputation evaluation model based on multi-criteria decision making (MCDM), which can effectively resist unfair evaluation and collusion attacks, and proposes an incentive mechanism based on reputation value to reward and punish nodes, thus promoting the good circulation of the system. We implement the proposed system based on Ethereum. The experimental results show that the system has a small communication cost, accurately reflects the user's reputation value, and has good availability and reliability. [ABSTRACT FROM AUTHOR]

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

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]

The paper presents an analysis of the impact of normalization techniques on the ranking of alternatives obtained using the combined compromise solution (CoCoSo) method. Similarity measures known from the literature and a new measure called the TOPSIS similarity measure (TOPSIS-SM) are used to assess the resulting rankings. This new measure is based on the TOPSIS algorithm, where the arithmetic mean of the considered rankings is taken as the ideal solution. In contrast, the antiideal solution is divided into a minimum and a maximum solution, which exhibit maximum separation from the ideal solution. The results obtained by this new method are different from those obtained using other similarity measures known from the literature. [ABSTRACT FROM AUTHOR]

This paper introduces the comprehensive value function, a novel framework designed to address multi-dimensional challenges in sustainability assessment within decision-making processes. Multi-criteria decision-making methods based on multi-attribute utility theory excel at integrating environmental, social, and economic factors but typically focus on positive and neutral impacts. This limitation often results in the oversight of critical negative consequences, thus restricting their effectiveness in all-encompassing sustainability evaluations. The proposed framework addresses this gap by quantifying the degree of satisfaction across a full spectrum of sustainability impacts and integrating negative outcomes essential for holistic assessments. The necessity of this model is highlighted by the shortcomings of current practices, where adverse impacts are frequently overlooked and existing frameworks fail to foresee the negative repercussions of various alternatives. By facilitating the creation of positive, negative, or piecewise functions, the CVF provides a comprehensive reflection of impacts, essential for well-informed sustainability decisions. Integrating this function into established decision-making models leads to a more balanced approach adept at navigating the intricate trade-offs inherent in sustainable development. Organised systematically, the paper presents the comprehensive value function, its application across various domains, and a concluding section synthesising the findings. [ABSTRACT FROM AUTHOR]

Customer feedback on online platforms is an unstructured database of growing importance for organizations, which, together with the rise of Natural Language Processing algorithms, is increasingly present when making decisions. In this paper, a bibliometric analysis is carried out with the intention of understanding the prevailing state of research about the adoption of sentiment analysis methods in organizations when making decisions. It is also a goal to comprehend which business sectors, and areas within the company, they are most applied, and to identify what future challenges that in this area may arise, as well as the main topics, authors, articles, countries, and universities most influential in the scientific literature. To this end, a total of 101 articles have been gathered from the Scopus and Clarivate Analytics Web of Science (WoS) databases, of which 85 were selected for analysis using the Bibliometrix tool. This study highlights the growing popularity of sentiment analysis methods combined with Multicriteria Decision Making and predictive algorithms. Twitter and Amazon are commonly used data sources, with applications across multiple sectors (supply chain, financial, etc.). Sentiment analysis enhances decision-making and promotes customer-centric approaches. [ABSTRACT FROM AUTHOR]

This study aims to present a multiattribute decision-making (MADM) and clustering method to explore the ranking, classifications and evolution mechanisms of the research fronts in the Web of Science Essential Science Indicators (ESI) database. First, bibliometrics are used to reveal the characteristics of the 57 ESI research fronts with more than 40 ESI highly cited papers (ESI-HCPs) for each research front. Second, the eight representative indicators are discovered to get answers to the following two questions: (i) Who publishes the ESI-HCPs that form a research front? and (ii) Where citations to these ESI-HCPs come from on a research front? Next, we investigate the ranking and clusters among the 57 ESI research fronts using the MADM and k -means clustering method and uncover the evolution process of the research fronts in different clusters based on the representative indicators. We also compare the performances of different countries in these research fronts and find that the USA and China are the leading countries in most research fronts. However, the two countries behave differently with regard to the rankings, the classifications and the evolution. [ABSTRACT FROM AUTHOR]

The dynamic multiple-criteria decision-making (MCDM) approach has drawn immense attention in the decision analysis domain over recent years. An essential problem of this dynamic process is how to retrieve the utmost from historical data. Although most previous dynamic MCDM studies process historical data only with external representatives, namely scores given to alternatives on each criterion, we argue that internal patterns of historical data are also essential to achieve a more comprehensive evaluation of alternatives. Nevertheless, a data-driven methodology aiming to capture internal patterns of historical data and consequently provide decision-makers with meaningful insights is still lacking. In this paper, we propose a framework that serves as integration within which both external representations and internal patterns are used to make a more comprehensive evaluation. Then we apply our framework to student evaluations and country-level risk assessments in the contexts of additive and fuzzy measures, respectively. Both cases show that either using external representatives or internal patterns alone leads to entirely different and even biased results. On the contrary, integrating external representations and internal patterns can offer more flexibility and higher interpretation power. [ABSTRACT FROM AUTHOR]

This paper introduces the conceptual framework of the multi-criteria decision-making (MCDM) rank model, which embodies the integration and harmonization of the aggregation method, the weighing method, the decision matrix normalization technique, and the selection of distance metrics. This definition serves to broaden the spectrum of acceptable MCDM methodologies for problem-solving and specifiing the associated tools. A Multi-Method Model (3M) approach is employed for multi-criteria selection to enhance the reliability of the results. The methodology is outlined for adjusting the rankings of alternatives to account for the distinguishability of ratings in a particular MCDM model using the Relative Performance Indicator (RPI) of alternatives. Through RPI, four methods are established for aggregating individual characteristics of alternatives that yield identical results: Weighted Sum Model (WSM), Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS (L1)), and Ratio System approach (RS), eliminating the need to duplicate these methods in the 3M approach. A comprehensive comparison of numerous multi-criteria methods is conducted based on two lists: ranking and rating. Additionally, a method for step-by-step linear transformation of alternative ratings obtained from various MCDM models is defined, facilitating comparison and aggregation of ratings. [ABSTRACT FROM AUTHOR]

The issue of sustainable development in financial institutions has become a primary concern for both the industry and investors. This study proposes a comprehensive decision-making method by integrating Cumulative Prospect Theory (CPT) with the traditional TOPSIS model for the sustainability assessment of commercial banks. Building on previous research, this study establishes a multi-criteria framework under the Global Reporting Initiative (GRI) that includes five major dimensions: economic, social, environmental, governance, and financial, along with 15 indicators. The study employs this model to conduct a comprehensive evaluation of five Chinese commercial banks. The results indicate that the Bank of China (BOC) ranked first in sustainability performance, particularly excelling in the environmental dimension compared to the second-ranked Industrial and Commercial Bank of China (ICBC), highlighting the importance of environmental indicators in the sustainability assessment of commercial banks. Empirical analysis shows that this model considers cognitive biases at the psychological level while accounting for uncertainties and risk preferences, offering significant advantages over the entropy-weighted TOPSIS model. The contribution of this paper lies in the pioneering introduction of Cumulative Prospect Theory into the study of sustainable development in Chinese commercial banks, combined with 15 related indicators under the GRI framework, providing new theoretical and practical insights for banking performance and sustainability research. This model effectively narrows the gap between the sustainability assessment practices of large Chinese commercial banks and international standards and holds promise as a useful tool for analyzing and improving sustainable development strategies in the banking sector. [ABSTRACT FROM AUTHOR]

To enhance the effectiveness of supply chain talent education, higher education institutions and other organisations have started to develop and use Massive Open Online Courses (MOOCs) in their training programs. However, the problem is that the design and delivery of supply chain management MOOCs can be inappropriately presented and, thus, ineffective, especially for educational teams with fewer teaching experiences of MOOCs. This eventually makes it hard for the students' learning outcomes to meet the industrial requirements of supply chain experts. Motivated by such a problem, this paper aims to improve the design and delivery of supply chain management MOOCs to enhance student learning outcomes. To achieve this goal, the research method adopted in this paper is to analyse online reviews in a widely-used Chinese MOOC platform with a novel TOPSIS-CoCoSo approach, aiming to identify the barriers to supply chain management MOOCs and their potential solutions. The results of this study show that 16 barriers to MOOCs are identified from the online reviews and then ranked based on their severity of reducing learning outcomes. The perceptions of the severity of the barriers to students and lecturers are compared, and the solutions to the barriers are then discussed. In addition, our comparison indicates that although students and lecturers have similar perceptions of severity for the majority of the barriers, they have significant disagreements on certain barriers. The significance of this study is that it can inform lecturers in supply chain management or relevant disciplines to better design and deliver their MOOC content, as well as contribute to the existing literature by providing new methodological tools for educational analysis. Also, this study highlights the necessity of comparative study in the MOOC online review analysis. [ABSTRACT FROM AUTHOR]

The importance of determining the appropriate location for the construction of the main centers of a company is quite tangible, so much so that the senior managers of an organization are trying to find the appropriate decision-making methods in this regard. In discrete decision-making methods, we see the existence of a decision-making matrix containing information on different options compared to the problem evaluation criteria. In this paper, a new decision-making method is presented in a way that tries to evaluate options more accurately by considering the possibility of changing the criteria's desirability in different ranges. It is not always appropriate to increase\decrease a positive\negative criterion. Even in some situations, crossing a certain limit can reduce the desirability of the desired criterion. Therefore, we will introduce the concept of value alteration boundaries for evaluation criteria in the formation of a decision matrix, which tries to examine the decision problems more realistically. Also, in the examination of alternatives, with simultaneous attention to ideal, anti-ideal and average solutions, evaluation and ranking based on accumulation and skewness of alternatives are presented. The present paper has examined the validation of methods by examining several numerical examples and also evaluating the candidate locations for the construction of a central warehouse in Gohar Zamin Mining and Industrial Company. Also, sensitivity analysis includes checking with other existing multi-criteria decision-making methods, changes in the number of problem options and checking the stability of the method, changes in the weight of the criteria and changes in the value alteration boundaries. Analysis of results shows that the proposed methods are efficient and suitable for dealing with multi-criteria decision-making problems. [ABSTRACT FROM AUTHOR]

This paper describes a generalized Diophantine fuzzy sets, which can be seen as a generalization of both Diophantine fuzzy sets and Pythagorean fuzzy sets. We define the basic properties of generalized Diophantine fuzzy set, as well as their relationships and distances. We compare Diophantine fuzzy sets with other Diophantine Pythagorean fuzzy sets to demonstrate their importance in the literature. We introduce new operators including necessity, possibility, accuracy function and score function. Furthermore, we discuss the new distance between normalized Euclidean distance and normalized Hamming distance. For a generalized Diophantine fuzzy relation, image and inverse image functions are defined. Numerous real-world applications can be found for the prevalent ideas of intuitionistic fuzzy sets, Pythagorean fuzzy sets, Diophantine fuzzy sets and q-rung orthopair fuzzy sets. Regretfully, these theories about the membership and non-membership grades have their own limits. We provide a new idea the generalized Diophantine fuzzy set that eliminates these limitations by including reference parameters. Compared to other kinds of fuzzy sets, there are more applications for generalized Diophantine fuzzy sets. We offer practical examples that show how different enhanced distances might be used in everyday situations. Additionally, to demonstrate the effectiveness of the suggested approach, flowchart based multi-criteria decision-making is provided and used to a numerical example. The outcomes are assessed for various parameter values. Furthermore, a comparative analysis developed to demonstrate the superiority of the suggested technique over current methodologies. [ABSTRACT FROM AUTHOR]

In the context of sustainable development and global challenges such as climate change and energy security, this paper conducts a bibliometric analysis of scientific journals on multi-criteria decision-making (MCDM) methods with an emphasis on their applications in environmental and energy engineering. The study used the CiteSpace software program 6.2.R6 Advanced to analyze citation networks and identify key publications, authors, and research topics. The simulations carried out made it possible to identify the main research centers and patterns of international cooperation, pointing to the key countries and institutions involved in MCDM research. The results of the analysis reveal the research areas of greatest interest and the main directions for future research. These results can support scientists, researchers, and policymakers in making more informed and sustainable decisions, contributing to the achievement of the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

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

This study introduces a novel mathematical tool for representing imprecise and ambiguous data: the multi-q cubic bipolar fuzzy soft set. Building upon established bipolar fuzzy sets and soft sets, this paper fist defines the concept of multi-q cubic bipolar fuzzy sets and their fundamental properties. Mathematical operations such as complement, union, and intersection are then developed for these sets. The core contribution lies in the introduction of multi-q cubic bipolar fuzzy soft sets. This new tool allows for a more nuanced representation of imprecise data compared to existing approaches. Key operations for manipulating these sets, including complement, restriction, and expansion, are defined. The applicability of multi-q cubic bipolar fuzzy soft sets extends to various domains, including multi-criteria decision making and problem solving. Illustrative examples demonstrate the practical utility of this innovative concept. [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]

This paper focuses on the testing and evaluation of special anti-chemical protective suits. The testing and evaluation focused on the subjective aspect of the protective suits, which is best perceived by their wearers; however, the subjective aspect only accounts for a certain proportion of the overall evaluation. For this reason, the proposed objective evaluation criteria are also presented in the thesis. Four types of anti-chemical protective suits were selected for testing. In this way, it was possible to compare their design specificities. An older type of civil protection suit and three modern types were selected for evaluation. Multi-criteria decision making and the expert method were used to develop the criteria. Mathematical relationships were used to evaluate the test results. A methodological procedure was developed to integrate the established subjective criteria in order to test. The results of the testing confirmed the construction and design advances of the protective suits over the older type of civil protection suit. At the same time, they pointed out possible shortcomings in the work and execution of the movements of the individual selected protective suits. By evaluating the data obtained, the most suitable protective suit was selected that best met the subjective criteria. However, these results need to be supplemented by an assessment of the objective criteria in the final evaluation. This paper also presents the characteristics of the selected protective suits in terms of technical parameters and objective criteria. The results of the study can further serve as a complementary way of evaluating the quality of protective suits. It is the subjective evaluation that is essential in a comprehensive assessment of the quality of protective suits, as the user himself often perceives the quality of the product differently from the manufacturer. [ABSTRACT FROM AUTHOR]

The paper aims to improve a simple additive weighting method (SAW) and the max-min rule designed for M-DMC (multicriteria decision making under certainty) based on already developed extensions for the Laplace and Wald rules (applied to one-criterion decision making under uncertainty, i.e., 1-DMU). Some evident analogies between scenario-based 1-DMU and M-DMC have been recently revealed in the literature, which gives the possibility to implement necessary amendments in M-DMC procedures, particularly in the multiple solutions case. The suggested modifications consist of applying additional decision tools (for SAW) and using the lexicographic approach (for the max-min rule). Thanks to them, options, treated as equivalent according to original M-DMC procedures, may obtain different ranks in the ranking. Such an improvement facilitates the decision making process. Both modified methods are illustrated by employing an example concerning the ranking creation for UE countries. [ABSTRACT FROM AUTHOR]