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Industrial firms in the EU have had to adapt over the past decades to face the increasing pressure of becoming more environmentally responsible. The European Union Emissions Trading Scheme (EU ETS) has played a crucial role in this process, and its impact on participating firms is a matter of debate for researchers, corporate managers, and policy makers alike. Unfortunately, there are only a minimum of models that assess the impact of the EU ETS on individual firms, especially in the context of overall firm performance and dependencies on other (economic and social) factors. For the paper industry, such a study is completely lacking. The authors use the well-established framework of the CSR model and the DC-PROMETHEE method to explore the ranking of paper producing firms in the EU. DC-PROMETHEE considers the effect of the dependencies between criteria that can potentially distort the results. The study revealed that both the EU ETS itself and the links between the evaluating criteria have an impact on the final CSR ranking of the paper producing companies. [Display omitted] • First CSR ranking model for EU paper firms, including environmental factors. • Multi-criteria decision model including the effect of EU ETS at company level. • CSR decision model considering criteria dependencies for unbiased evaluation. [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]

Abstract: Recommendation algorithms have been researched extensively to help people deal with abundance of information. In recent years, the incorporation of multiple relevance criteria has attracted increased interest. Such multi-criteria recommendation approaches are researched as a paradigm for building intelligent systems that can be tailored to multiple interest indicators of end-users – such as combinations of implicit and explicit interest indicators in the form of ratings or ratings on multiple relevance dimensions. Nevertheless, evaluation of these recommendation techniques in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined that the performance of such algorithms is often dependent on the dataset – and indicate the importance of carrying out careful testing and parameterization. Especially when looking at large scale datasets, it becomes very difficult to deploy evaluation methods that may help in assessing the effect that different system components have to the overall design. In this paper, we study how layered evaluation can be applied for the case of a multi-criteria recommendation service that we plan to deploy for paper recommendation using the Mendeley dataset. The paper introduces layered evaluation and suggests two experiments that may help assess the components of the envisaged system separately. [Copyright &y& Elsevier]

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]

Abstract: This paper proposes a multi-attribute comprehensive evaluation method of individual research output (IRO). It highlights the fact that a single index can never give more than a rough approximation to IRO, and the evaluation of IRO is a multi-attribute complex problem. Firstly, an evaluation index system is established by determining evaluation attributes and choosing the appropriate bibliometric indicators. To address the multiple authorship problem, this paper develops an improved number-of-papers-published indicator. Following this, TOPSIS method is used to conduct a comprehensive IRO evaluation. Then this paper uses a case study to test the feasibility of the methodology. Finally, this paper discusses the effectiveness of the proposed method. Compared with traditional single-indicator evaluation approaches, the proposed multi-attribute evaluation takes more aspects into consideration, therefore it is able to effectively overcome the one-sidedness of a single indicator. The proposed method also has significant advantages compared with other comprehensive IRO evaluation methods. [Copyright &y& Elsevier]

Abstract: The use of Multi-criteria Decision Analysis (MCDA) was investigated in an exercise using a panel of local residents and stakeholders to assess the options for managing waste paper on the Isle of Wight. Seven recycling, recovery and disposal options were considered by the panel who evaluated each option against seven environmental, financial and social criteria. The panel preferred options where the waste was managed on the island with gasification and recycling achieving the highest scores. Exporting the waste to the English mainland for incineration or landfill proved to be the least preferred options. This research has demonstrated that MCDA is an effective way of involving community groups in waste management decision making. [Copyright &y& Elsevier]

• Aluminium is one of the most recycled metal with high recycling potential rate. • Waste cables are one of the most massive growing waste streams in the world. • The main driver of cable waste recycling is the high value of the aluminium. • Gravity and electrostatic processes are widely used to recycle waste cables. • The MCDM methods is used to select the most acceptable recycling separation process. The separation process, particularly gravity and electrostatic separation, is very important in the recycling processing industry. Some characteristics of waste cable, as well as separation have a significant impact on the results and effects of aluminium recycling. At the same time, this process requires a significant amount of energy which significantly affects the overall recycling costs. This paper focuses on the recycling of waste aluminum cable by gravity and electrostatic separation. Experimental tests were performed on three different separators. Therefore, this paper presents the application of multiple criteria decision making (MCDM) methods, combining TOPSIS and AHP methods, as well as WASPAS and AHP methods, which should enable an efficient selection of the separation process. Also, the impact of the criteria weights to the ranking order of alternatives was performed in order to verify the obtained results. Results of all the studies presented in this paper show that a corona-electrostatic separation process is the most acceptable process for waste aluminium cable recycling. [ABSTRACT FROM AUTHOR]

Decarbonizing road transportation is vital for addressing climate change, given that the sector currently contributes to 16% of global GHG emissions. This paper presents a comparative analysis of electric and hydrogen mobility infrastructures in a remote context, i.e., an off-grid island. The assessment includes resource assessment and sizing of renewable energy power plants to facilitate on-site self-production. We introduce a comprehensive methodology for sizing the overall infrastructure and carry out a set of techno-economic simulations to optimize both energy performance and cost-effectiveness. The levelized cost of driving at the hydrogen refueling station is 0.40 €/km, i.e., 20% lower than the electric charging station. However, when considering the total annualized cost, the battery-electric scenario (110 k€/year) is more favorable compared to the hydrogen scenario (170 k€/year). To facilitate informed decision-making, we employ a multi-criteria decision-making analysis to navigate through the techno-economic findings. When considering a combination of economic and environmental criteria, the hydrogen mobility infrastructure emerges as the preferred solution. However, when energy efficiency is taken into account, electric mobility proves to be more advantageous. [Display omitted] • Decarbonization of public mobility in a remote island context. • Comparative analysis of hydrogen and electric mobility infrastructure. • Techno-economic optimization modeled in PyPSA framework. • Multicriteria Decision Making Analysis to steer policymakers' decisions. [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]

• Proposed a Multi Criteria Decision Making based Reverse Vaccinology model for predicting Bacterial Protective Antigens. • The proposed model tested on extracted physicochemical features from bacterial protein sequence. • Applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour to handle the data imbalance problem. • Consider MCDM-based TOPSIS and CRITIC methods for order preference with soft and hard ranking model. Reverse vaccinology (RV) is a significant step in sensible vaccine design. In recent years, many machine learning (ML) methods have been used to improve RV prediction accuracy. However, there are still issues with prediction accuracy and programme accessibility in ML-based RV. This paper presents a supervised ML-based method to classify bacterial protective antigens (BPAgs) and identify the model(s) that consistently perform well for the training dataset. Six ML classifiers are used for testing with physiochemical features extracted from a comprehensive training dataset. Selecting the best performing model from different performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) has not been easy, because all the metrics has the same importance to predict BPAgs. To fix this issue, we propose a soft and hard ranking model based on multi-criteria decision-making (MCDM) approach for selecting the best performing ML method that classifies BPAgs. First, our proposed model uses homologous proteins (positive and negative samples) from Protegen and Uniprot databases. Second, we applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour (SMOTE-ENN) to handle the data imbalance problem and train the model using ML methods. Third, we consider MCDM-based technique for order preference by similarity to the ideal solution (TOPSIS) method integrated with soft and hard ranking model. The entropy is used to obtain weighted evaluation criteria for ranking the models. Our experimental evaluations show that the proposed method with best performing models (Random Forest and Extreme Gradient Boosting) outperforms compared to existing open-source RV methods using benchmark datasets. [ABSTRACT FROM AUTHOR]

In this paper, we show the practical application of two recently proposed Multi-Criteria Decision Analysis (MCDA) methods, namely the Stable Preference Ordering Towards the Ideal Solution (SPOTIS) and the Reference Ideal Method (RIM) methods. Both of these methods can utilize the Expected Solution Point (ESP) concept, which allows it to easily reflect the decision-maker's or the customer's preferences and expectations. We show the comparison of those methods in the practical study case of laptop choice for the customer with specific needs and expectations for the hardware expressed using ESP. The comparison also includes rankings built toward an optimal solution to underline the necessity of such an approach as the Expected Solution Point. We also use the recently developed Ranking Comparison (RANCOM) method to identify criteria weights to include customer preferences in the weights. The paper also contains a broad discussion of the obtained results and limitations of the presented methods. [ABSTRACT FROM AUTHOR]

Enterprise resource planning (ERP) adoption literature has a consensus that selecting the right ERP system is one of the most critical success factors in the ERP adoption lifecycle. While choosing a non-fitting ERP system may lead to adoption failures, however very few papers focus solely on this selection phase. Hence, given the criticality of the ERP selection phase, this paper aims to identify and review the different ERP selection methods in extant literature. This research also presents the factors and variables included in each identified selection method in ERP literature. As a result, each method identified was reviewed, analyzed, and summarized. Our main findings suggest that ERP selection is a multi-criteria decision-making (MCDM) problem, with various methods and techniques that can be utilized for such problems. Several MCDM methods have been used in literature, but often complementing more than one method combined at a time. This is since some methods excel in considering factors in uncertain environments, and other methods are best in evaluating qualitative and quantitative factors. Finally, while there are some methods that were used for cloud-ERP selections, there is no clear consensus in extant literature if some methods could best fit specifically cloud-ERP contexts in contrast to on-premises counterparts. [ABSTRACT FROM AUTHOR]

Machine Learning as a Service, MLaaS refers to cloud-based platforms that provide machine learning tools and infrastructure to users, allowing them to access and utilize machine learning capabilities without managing the underlying hardware and software. Cloud service providers offer scalable and flexible resources, enabling businesses to train and deploy machine learning models efficiently and cost-effectively. Multi-Criteria Decision making, MCDM helps individuals or organizations make choices when multiple criteria or objectives are involved. It aims to find the best alternative among a set of available options by considering various criteria or factors that are relevant to the decision. Machine learning as a service (MLaaS) and MCDM are two distinct concepts that can be combined to create powerful decision-making solutions. The present paper proposed an integrated approach based on the Analytic Hierarchy Process and TOPSIS method for choosing the best MLaaS cloud. Four alternatives for MLaaS cloud were analyzed based on seventeen shortlisted criteria. Based on the calculations, conclusions have been reached and potential study areas have been identified. [ABSTRACT FROM AUTHOR]

• This paper examines a reinforcement learning (RL) method that uses 2steps-ahead to schedule the batteries and PV. • Develop an essential architecture to make multi-criteria decisions via an individual user. • Increase the battery's usage at peak times and increase the wind turbine's and PVs usage for local consumption. • Reinforcement learning algorithms modeling in digital twin to select the optimum battery planning measures based on forecasts of wind power and photovoltaic availability. This research study aims to investigate the microgrid operation for distributing energy including of a local user, a wind turbine, 5 photovoltaics (PV), and a battery, which is linked by a transformer to the external network. This paper examines a reinforcement learning (RL) method that uses 2steps-ahead to schedule the batteries, which is essential for achieving the objective of the users. There is an essential architecture to make multi-criteria decisions via an individual user to increase the battery's usage at peak times and increase the wind turbine's usage for local consumption. RL algorithms select the optimum battery planning measures based on forecasts of wind power and photovoltaic availability. Through the suggested learning, the user can better understand the optimum battery planning measures for various time-varying environment factors. By using the proposed architecture, smart users are capable of learning the uncertain environment and selecting optimum energy management measures based on their experiences. [ABSTRACT FROM AUTHOR]

This paper aims to perform a literature review on the use of multi-criteria methods for assessing agricultural sustainability, focusing on the distribution of papers according to year, journal, and countries, the most productive authors, the most frequently used multi-criteria methods and their characteristics, the type of system and the type of agriculture in which these methods are being performed, the methodological approaches and assessment types, and the sustainability dimensions considered. The data collection has been carried out through the Web of Science™ platform on September 3rd, 2021. After a refinement process, 41 papers were selected. The descriptive analysis was carried out through Bibliometrix tool, while content analysis was performed using Nvivo. The descriptive analysis shows that from 2016 to 2021 the scientific production addressing multi-criteria methods to assess agricultural sustainability started to grow markedly in a very rapid matter, reaching an average of 6 papers per year. France and China are the most scientifically productive countries. The content analysis points out that the most used multi-criteria method is the AHP that was used 11 times. The outranking methods, instead, were used only 3 times. In 68% of the papers the Triple Bottom Line was used as dimensions, and in 41% of the papers the spatial applicability was the farming system. The results highlight that doesn't exist many MCDM/A methods for assessing agricultural sustainability, and most of them are compensatory. These results point out that the use of multi-criteria methods in assessing agricultural sustainability is still underexplored and can be improved. [Display omitted] • Multicriteria methods are powerful tools for assessing agricultural sustainability. • A literature review on agricultural sustainability evaluation was performed. • The number of studies has been increasing in the last five years. • Most of the approaches are based on compensatory multicriteria methods. [ABSTRACT FROM AUTHOR]

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. [ABSTRACT FROM AUTHOR]

• This paper conducted a predictive evaluation of sustainability for 34 cities in northeastern China. • An innovative framework for predictive evaluation was proposed. • The evaluation results revealed that the overall level of sustainability in northeastern China was not high. • Economy had the highest relationship with city sustainability. • The predictive results indicated that the sustainability of catching-up cities will make varying degrees of progress. Sustainability is a recurring theme in city development, aiming to foster an environmentally friendly, livable, and prosperous future. Sustainable development, as a long-term vision for cities, requires an interdisciplinary method and a systems-orientated perspective on evaluating and solving city problems. The multi-criteria decision making (MCDM) method is highly applicable given the excellent multidimensional analytical and model building capabilities. As a widely used decision-making tool, the classical paradigm of MCDM is to make summary measurements of the system based on historical data to facilitate the best conclusions. Nevertheless, from the perspective of long-term sustainability evaluation, the currently available MCDM models are gradually failing to meet the predicting needs highlighted by policy makers in urban planning. Therefore, this paper proposes an innovative predictive evaluation method of city sustainability by integrating benchmarking and stochastic simulation under the framework of MCDM, which enables cities to develop visionary scenarios to accelerate the achievement of sustainability goals. This method was used to predict the future sustainability of 34 cities in northeastern China, which is a good demonstration of its utility and validity in sustainable cities and related fields. [ABSTRACT FROM AUTHOR]

Correctly adhering to the processes within the software development life cycle (SDLC), from analysis and design to coding and testing, is vital for ensuring the successful and efficient creation of high-quality software applications. These structured phases provide a systematic approach to software development, facilitating clear communication, reducing errors, and improving collaboration among development teams. For the proper and correct use of SDLC processes, it is essential for both software engineers and software decision makers to perform the correct and needed actions while performing each software process, and one method of facilitating that is multicriteria decision making (MCDM). This study aims to provide a systematic review of the use of MCDM within the field of software engineering (SE), encompassing methodologies such as fuzzy MCDM, AHP, TOPSIS, DEMATEL, and other methods, with a deliberate focus on software engineering development processes. To ensure the high quality of this review, a methodical and structured literature search process was performed with strict selection criteria, resulting in the identification of 32 contributions on the applications of MCDM in SE from various databases, including Scopus, ScienceDirect, IEEE Xplore digital library (IEEE), and Web of Science (WOS). The selected papers were taxonomized into seven main categories, with some divided into subcategories. This paper presents a systematic and comprehensive analysis of the aforementioned studies, investigating the challenges, motivations, and recommendations found within each, thereby paving the way for potential future research. Bibliometric analysis is also provided to show concise quantitative analysis of related bibliographic information, which draws several key insights into publication trends. Finally, a critical analysis of the current literature and existing research is presented, while also addressing relevant research gaps. • Investigates the taxonomy of MCDM applicability in software engineering. • Provides four bibliometric analyses and insights into the research landscape. • Explores data quality issues, high computation time, and sampling bias. • Illustrates perspectives on motivations, challenges, and recommendations. • Identifies a critical analysis and research gaps in MCDM-software engineering. [ABSTRACT FROM AUTHOR]

• Inner and marginalized areas: an increasingly relevant issue at European level. • Inner areas as socio-ecological systems (SES): an integrated sustainability approach. • An innovative methodology to design a system of indicators of sustainability. • Theoretical background based on Ecological Economics and strong sustainability. • Multi-criteria framework for a standardized social-biophysical evaluation grid. The phenomenon of inner peripherality is on the rise in Europe. Inner peripheries can be defined as peripheral rural areas experiencing territorial disconnection and progressive marginalization given their distance to urban centres, where the supply of services is concentrated. Assessing and comparing the sustainable development of these areas according to nuanced and integrated social-biophysical perspectives is crucial to highlight their genuine long-term sustainability potential and critical aspects. Moreover, it is essential to monitor that the socio-economic development of the latter goes hand in hand with territorial stewardship and an appropriate management of their extensive natural endowments. To achieve these objectives, this paper illustrates an innovative methodology to design a multi-criteria evaluation framework named PAISDA (Peripheral Areas Integrated Sustainable Development Assessment). The framework, based on the theoretical background of ecological economics, makes it possible to monitor the sustainable development of inner and marginalized areas according to a "post-growth" approach. It also allows a shift towards a strong sustainability perspective by assessing the phenomenon in an integrated way, which combines social and biophysical dimensions. This can help to design more robust and better targeted policies for these areas, thereby improving local strategic planning. The assessment process is as follows: first, the values of lower order components are computed through a reflective measurement model, then aggregated to form the values of higher order components, which are used as criteria in the last phase of multi-criteria decision analysis (MCDA). The choice of normalization and aggregation methods responds to methodological coherence. The paper describes a step-by-step procedure for applying the PAISDA framework in any European country, providing an illustrative example of its implementation in relation to Italian inner areas. [ABSTRACT FROM AUTHOR]

Decision-making problem is a fundamental activity in human life every day. Regarding group decision-making, individuals have insufficient comprehension of the decision objects owing to the limited experience and knowledge and drawback the evaluation process. Therefore, the evaluation information is missing and the group cannot get the precise information for decision objects resulting in the generation of incomplete information systems. It is meaningful for individuals finding an approach to Leverage and tap into existing information. On the basis, the work presented in this paper focuses on group decision-making problems (GDM) with incomplete information. Under the framework of group decision-making, a GDM approach with an incomplete information system is presented combining the formal concept analysis and rough set theory. Firstly, we propose a concept called formal decision context and discuss the properties of the formal decision context, which could divide the incomplete information system into many cognitive units, and the definitions of cognitive units will be given. Then, the property and the relations of various cognitive units will be discussed. Additionally, the upper and lower approximation space under the formal decision context is founded considering the risk attitudes integrating fuzzy rough set and formal concept context. Considering the order relation of various cognitive units and decision objects, we introduce the dominance relation and the importance degree of each cognitive unit under the framework of formal decision context. Furthermore, a theoretical framework of the multi-criteria decision-making problem is presented and two models for the multi-criteria group decision-making problem are discussed combined with fuzzy rough set and formal decision context from two perspectives, including qualitative analysis and quantitative analysis. Finally, an illustrated example is applied to verify the validity and effectiveness of the proposed models. The contributions of this paper include two aspects. On the one hand, this paper provides a new perspective and paradigm considering MCGDM problems with the incomplete information system from the perspective of group consensus. On the other hand, this paper gives two approaches for group decision-making problems from sorting and set theory by the perspective of multi-granular rough set and group consensus. • A model of formal decision context with incomplete information to describe the cognitive situation. • An approach to MCDM problem combined rough set with formal decision context with incomplete information. • Two approaches to MCGDM problem from qualitative analysis and quantitative analysis. [ABSTRACT FROM AUTHOR]

Over the last twenty years, patient opinions of the received healthcare services have emerged as a crucial quality indicator and a valuable instrument for evaluating the service quality of health organizations. However, many surveyors still utilized uncertain information for conducting this evaluation process. Whereas, in the past few years, the issues of inconsistency, indeterminacy, and uncertainty in data have been growing and have become a matter of concern in dealing with decision-making issues. This paper proposes an improved RAFSI method based on a single-valued trapezoidal neutrosophic number (SVTraNN) as a multi-attribute framework for dealing with a healthcare service quality evaluation. The proposed framework consists of two features: a feature for determining numerical criteria weights involving the procedures of the Ordinal Priority Approach (OPA) and a feature for selecting the best polyclinic through contributing our new SVTraNN Harmonic and arithmetic mean to yield criterion functions in the RAFSI method. This paper then compares the results obtained by the proposed method with other SVTraNN decision-making methods. A comparative analysis and rank reversal testing of the improved method demonstrated that the multi-attribute framework proffers the option of capability and stability in overcoming inconsistency, indeterminacy, and uncertainty information efficiently during the healthcare service quality evaluation. [ABSTRACT FROM AUTHOR]

Motivated by the practical needs of improving the rescue efficiency of emergency events and reducing the damage, the problem of emergency material supplier selection arises. This paper aims to propose a novel group decision making framework, this framework can aggregate experts' opinions from different backgrounds to evaluate emergency material suppliers. Firstly, an evaluation indicator system considering the supplier's emergency supply capacity is established, to make up for the insufficient applicability of the traditional supplier evaluation indicator system in the field of emergency material supplier evaluation. Then, the game theory integrated weight (GTIW) method is used to gather the initial weights obtained by Maximum Consensus Minimum Hesitation Model (MCMHM) and expert background weights. Moreover, Exponential TODIM (ExpTODIM) method based on Hesitant fuzzy linguistic set (HFLs) named HF-ExpTODIM is introduced to solve the problem of emergency material supplier selection, TODIM is an acronym in Portuguese for Interactive and Multi-criteria Decision Making. Again, a case study of China on emergency medical mask supplier selection is presented, and the corresponding results are discussed. Finally, the practicality of the proposed framework in a real-world context is discussed, including the necessary preparation, operational priorities and caveats. This paper can provide a reference for group decision making of material supplier selection in emergencies. • A framework for selecting emergency material suppliers has been proposed. • Expert consensus and expert background differences are comprehensively considered. • Supplier's emergency supply capacity is added to the evaluation indicator system. • HF-ExpTODIM is proposed by combining HFLs and ExpTODIM. • The effectiveness of the proposed method is verified by a Chinese case study. [ABSTRACT FROM AUTHOR]

Decision analysis is a key aspect in improving slums intervention strategies utilizing a variety of tools to evaluate all relevant information that support decision-making process for dealing with the complexity of slums interventions policies. This paper presents a framework integrating Multi Criterion Decision Analysis (MCDA) with (GIS) based decision support tool to quantitatively assess land suitability for site relocation of an urban slum site utilizing Analytical Hierarchy Process (AHP), which is commonly used for fastest growing decision-analytic techniques in several disciplines for the determination of various aspects weights. Herein Batn Al Baqara region (which is located in south of Cairo) was selected as study area to be relocated as it was considered among the most difficult to deal with, three proposed relocation sites were selected and analyzed using MCDA-GIS Model. Twelve different criteria were utilized to evaluate the degree of suitability of the relocation sites. A five degree of suitability were employed according to the different criteria scores for each site. The results supported that Al Asmarat is the most suitable site for the relocation of slum since it achieved the highest evaluation score (84.92 %) when the model was applied to the relocation sites. [ABSTRACT FROM AUTHOR]

As an important carrier of multi-energy coupling of regional integrated energy system, the integrated energy station integrates a variety of energy coupling devices, which can fulfill interconnection and complementarity of energy between regions and enhance the economic and environmental benefits of energy use. In the planning of integrated energy stations, it is necessary to consider the characteristics of regional resource endowments and load energy consumption. In this paper geographic information system (GIS) is introduced into the site selection planning of integrated energy stations, and a location decision model is built based on GIS & improved TOPSIS. In order to verify this for the feasibility of the model, Tianjin, China is selected as a representative case for empirical research. First, according to Chinese national standards and local planning requirements, GIS is adopted to initially screen the distribution and area of exclusion areas and viable areas for construction in the study area. Secondly, a comprehensive evaluation index system is established for nature, economy and society, in which the AHP method is used for calculate the weight of each index to obtain the final optimal alternative with the integration of the GIS geographic layer overlay function. Finally, the improved TOPSIS model is used to rank the alternatives and obtain the ranking results. Sensitivity analysis and comparative analysis is conducted on the ranking results, indicating a higher priority of selecting sites A1, A4, A5 and A11. Analysis result effectively verifies that the improved TOPSIS method can improve the accuracy of the site selection decision for integrated energy stations. This paper can provide support for the site selection and layout of integrated energy stations, effectively improve the decision-making level and work efficiency of decision-makers, and enrich the application space of GIS methods in the energy field. •Introduction of GIS method into the site selection of integrated energy stations. •Identify the best location and construction area of the integrated energy stations. •Establish a MCDM index system for siting integrated energy stations. •Construction of "GIS-AHP-improved TOPSIS" siting decision model. •Meet the integrated energy stations current and future transformation needs. [ABSTRACT FROM AUTHOR]

Most Multi-Criteria Decision Analysis (MCDA) methods identify only discrete, defined sets of alternatives. Such identification does not provide an opportunity to consider a more significant number of samples acquired over long periods, which is not a desirable effect. An additional aspect of identification is the frequent application of expert knowledge, whereby a continuous model would have to be defined to consider further alternatives that would be suitable for further use. This paper considers a new approach for identifying continuous decision models named SITCOM. The purpose of this paper was to conduct a study on the accuracy of the proposed approach. The accuracy of Stochastic IdenTifiCation Of Models (SITCOM) was tested using sample evaluations obtained from fuzzy reference models (FRM). Spearman's weighted correlation coefficient was used to measure the accuracy of the created models. The experiments showed the dependence of accuracy on the number of characteristic objects. Furthermore, the obtained results prove the reliability of the obtained results, where the trends of accuracy decrease based on the number of characteristic values. [ABSTRACT FROM AUTHOR]

Hadoop is an open-source framework that is widely used to store and process large amounts of data. Its core component is called YARN that is responsible for resource management and job scheduling for Big Data applications. Although YARN has very advanced processing performance. It suffers from resource allocation problems when used in a heterogeneous cluster serving multiple users. To deal with this heterogeneity and overcome the degradation of cluster, load balancing has become a major challenge for Hadoop run-time management. In this paper, an efficient resource allocation strategy combining multi-criteria decision-making and accurate clustering of nodes in a heterogeneous Hadoop environment is proposed. A multi-criteria decision approach relying on a modified analytical hierarchy process method is applied to assign a score to jobs depending on the resources requirements at run time. First, the proposed system profiles the available nodes by grouping them into clusters with similar performance. Second, the system performs a dynamic multi-criteria selection on resource requests by assigning scores to jobs according to their resource usage. Third, node groups and job scores are used to dynamically allocate jobs to the most appropriate resources in real time while keeping load balance in the heterogeneous cluster. The aims of this present paper are to optimize the resource utilization and achieve a high performance of the Yarn resource management by considering a heterogeneous environment. The experimental results demonstrate that the proposed algorithm provides the best utilization of available resources by 45% and 12% compared to fair and H-fair respectively, which implies a minimum job execution time in a heterogeneous cluster. [ABSTRACT FROM AUTHOR]

To evaluate some real-life multi-criteria decision-making (MCDM) problems, the decision-makers (DMs) need an interactive and flexible MCDM approach that works well for extended decision-making space and has the ability to apply any suitable operational laws to fuse the information under incomplete criteria weighting information. The objective of this study is to propose a multi-criteria group decision-making (MCGDM) approach to evaluate any real-life MCDM problem under incomplete criteria weighting information. To achieve the objective, the paper proposes an integrated MCGDM approach in which the q -rung orthopair fuzzy set (q-ROFS) is used to provide the extended decision-making space to the DMs, a transformative approach is applied to find the DMs' weights, the maximizing deviation method is implemented to find the criteria weights, Archimedean t -norm and t -conorm (ATU)-based operational laws are adopted to fuse the information, and the TOPSIS method is applied to rank the alternatives. The proposed integrated MCGDM approach has been applied to a real-life MCDM problem associated with the evaluation of different strategies implemented by governments to contain the COVID-19 pandemic. A sensitivity analysis is conducted to examine the impact of changes in the parameters involved in the proposed MCGDM approach on the results. A comparative analysis is also performed with some existing classical and aggregation operator-based MCDM methods to demonstrate the consistency of the proposed approach. Findings suggest that the proposed MCGDM approach is general and flexible enough to evaluate any real-life MCDM problem under unknown criteria weighting conditions. • The paper proposes ATU based qROF-TOPSIS method for unknown criteria weights. • The method applies q-ROFS to quantify uncertainty and ATU to fuse the information. • The maximizing deviation method is used to evaluate the criteria weights. • The TOPSIS method is applied to rank the alternatives. • The method is applied to evaluate government strategies against COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

• Introduces the Fuzzy Analytic Hierarchy Process (AHP)-VIKOR technique for risk ranking in the nuclear power industry. • Emphasizes the need for thorough examination and sequential assessment of each component's risks to minimize failures and risks. • Utilizes expert evaluations based on Severity, Occurrence, and Detection criteria to calculate risk weights and rankings. • Provides a higher-level risk analysis that incorporates multiple performance parameters. The critical systems in industries such as nuclear power plants rely on various preventive methods to minimize failures or risks through efficient strategies and equipment. However, in many businesses, maintenance tasks are carried out infrequently, improperly, and without consideration for the overall state of the plant or its equipment. To choose the appropriate risk preventive approach, a thorough examination of each component's risks in a sequential manner becomes imperative. This paper introduces the Fuzzy Analytic Hierarchy Process (AHP)-VIKOR technique, a Multicriteria Decision Making Approach employed to rank the various risks prevalent in the nuclear power industry. By identifying the proper sequence of risks, this approach aims to reduce the occurrence of unfortunate mishaps, along with minimizing recovery time and costs. Five experienced researchers and experts assessed the impact of risk based on three risk criteria: Severity, Occurrence, and Detection. Utilizing the opinions and judgments of these experts, the Fuzzy AHP-VIKOR approach was employed to calculate the weight of each performance criterion and the ranking of each risk or hazard. The suggested method is designed to assist supervisors in resolving discrete problems characterized by incommensurable and conflicting criteria. This study contributes to the industry by providing a higher risk analysis incorporating various performance parameters. The paper concludes by presenting a result with the priorities of all risks in the industry using the fuzzy AHP-VIKOR method. [ABSTRACT FROM AUTHOR]

This paper proposes a new linguistic model called Linguistic q-Rung Orthopair fuzzy Z-number (LqROFZN), which combines the advantages of linguistic variables, Z-number and q-Rung Orthopair Fuzzy numbers. It can be used as a powerful tool for uncertain decision-making, which can effectively improve the accuracy and reliability of the decision-making results, and has a notable application prospect for the fields of information decision-making, risk assessment, diagnosis and so on. In this paper, firstly, the definition of LqROFZN and its operational rules are given, a new distance measure and the concept of entropy are given under LqROFZN, and the entropy of LqROFZN can assess the credibility situation of LqROFZN. Next, two aggregation operators under LqROFZN are given, namely the Linguistic q-Rung Orthopair fuzzy Z-number weighted aggregation (LqROFZWA) operator and the Linguistic q-Rung Orthopair fuzzy Z-number weighted Geometric aggregation (LqROFZWGA) operator. Finally, the MCDM method under LqROFZN is given and the credibility of the evaluation results is assessed using the entropy of LqROFZN. In a set of actual airline aircraft selection cases, the feasibility and advantages of the proposed method are verified through comparative analysis with other methods. [ABSTRACT FROM AUTHOR]

Many companies are now moving their business to the cloud because the cloud provides user with unlimited resources and uninterrupted service. The aim of the paper is to rank services provided by a set of cloud-providers based on multi-criteria decision-making (MCDM) methods. The ranking of the cloud-providers set is based on the Interval neutrosophic set theory (INS). The paper proposes INS and TOPSIS techniques to help users and experts express their evaluations and rank cloud-providers based on various criteria. INS is used to deal with the imprecise knowledge, generated by the ambiguity from the experts' evaluations. To demonstrate the effectiveness of the framework, the closeness index (CI) values of CSPs were ranked in decreasing order. A numerical example for a case study for a set 6 CSP and 10 criteria is presented. The numerical results show that SoftLayer's is the best cloud-provider with a CI value equal to 0.69. The worst cloud-provider is Microsoft Azure with a CI value equal to 0.20. A sensitivity analysis was performed to check the consistency and robustness of the model. [ABSTRACT FROM AUTHOR]

We present the scientific results of the call for papers in SEPS titled 'Decision-Making Models for Common Goods in Environmental, Health, and Logistics Contexts.' As guest editors in Socio-economic Planning Sciences (SEPS), we describe the domain of our academic initiative and present the collected papers. Interestingly, our collection includes papers employing various methodologies such as multi-criteria decision making, game theory, operations research, microeconomics, computational models and insurance mathematics. We received more than fifty submissions from Asia, Europe and America. However, only thirteen papers were accepted for publication in SEPS. This demonstrates the extensive investigation of the problem of common goods and the ongoing academic debate surrounding it. [ABSTRACT FROM AUTHOR]

This paper proposes a rectified complex spherical fuzzy set (rCTSFS) model that enables the phase term of a complex number to function truthfully to its inherent meaning of representing directions, phases, or color hues. In addition, this paper proposes the score and accuracy functions for rectified complex spherical fuzzy numbers (rCTSFn), which allows the three types of membership degrees of an rCTSFn to fulfill human judgment/intuition. The proposed rCTSFS model proves to be a productive extension of the complex spherical fuzzy set (CSFS), complex fuzzy set (CFS), and spherical fuzzy set (SFS) models. On the other hand, Dombi t -norms prove more flexible and comprehensive than some of the other families of triangular norms, such as the algebraic t -norms and the Einstein t -norms, due to the presence of a parameter γ. The parameter γ determines the amount of aggressiveness at estimating the maximum and the minimum of a population based on a sample obtained. Therefore, this paper proposes two Dombi-Choquet integral operators, namely, the rectified complex t -spherical fuzzy arithmetic Dombi-Choquet integral (r C T S F A γ , w λ ) operator and the rectified complex t -spherical fuzzy geometric Dombi-Choquet integral (r C T S F G γ , w λ ) operator. A multi-criteria decision making (abbr. MCDM) algorithm utilizing the two Dombi-Choquet integral operators is innovated. The proposed Dombi-Choquet MCDM algorithm for the rCTSFS model is applied to an MCDM problem related to diabetic retinopathy detection on five real-life fundus images of different severity levels taken from the Messidor2 dataset. Our newly proposed algorithm proves to be the only algorithm that yields the correct diagnostic results that match the hard truth. On the other hand, none of the 50 algorithms observed among recent works in literature can produce the correct diagnostic results, even after lending the fuzzification procedure innovated in this work to them. [ABSTRACT FROM AUTHOR]

In contemporary healthcare systems, the selection and judicious choice of the right medical device becomes paramount consideration for accurate diagnosis, patient care, mitigate risk and cost incurred on diagnosis of disease. Moreover, medical devices are requisite indispensable tools to help healthcare professionals for effective diagnosis, selection of the right treatment amongst different options, and continuous monitoring of the patient. Thus, to select the best manufacturing company is as equally important to achieve the desired goals. This paper presents a decision making algorithm based on fuzzy theory that helps the decision maker in selecting the most suitable worldwide medical device manufacturer. Fuzzy theory is extensively used in managing databases and analyzing data to summarize information and draw meaningful conclusions. This paper utilizes the aggregation operators within the context of the complex q-rung orthopair fuzzy environment by utilizing Yager t-norm and t-conorm operations. Some aggregation operators are applied, namely, complex q-rung orthopair fuzzy Yager weighted geometric operator, complex q-rung orthopair fuzzy Yager ordered weighted geometric operator, complex q-rung orthopair fuzzy Yager weighted averaging operator and complex q-rung orthopair fuzzy Yager ordered weighted averaging operator. Additionally, the notable properties of these operators are proved. These aggregation operators are generated specifically for multi-criteria decision-making. Decision making algorithm is used to choose the most relevant medical device manufacturer (alternative) to validate its efficacy. The results demonstrate the versatility of parameters, adaptability to multi-criteria decision making, and efficiency of the proposed aggregation operators and algorithm. Test with specific criteria is also conducted to verify the legality and validity of the decision making algorithm. At last, the acquired results underwent a comparative analysis against various established methodologies, confirming the method's capacity to produce precise and accurate outcomes. • Multi criteria decision making algorithm is devised in fuzzy environment. • Aggregation operators are introduced and notable properties are investigated. • Test with specific criteria is conducted for the validity of proposed operators. • The algorithm is utilized in medical industry to choose best device manufacturer. [ABSTRACT FROM AUTHOR]

• Life Cycle Engineering has been demonstrated as a viable approach for space mission design. • The life cycle sustainability impacts of SmallSats have been benchmarked for the first time. • Multi-Criteria Decision Analysis is a useful technique for identifying significant hotspots. • Improvements measures are more successful with increased levels of participant interaction. The application of Life Cycle Engineering (LCE) within the concurrent engineering process presents a viable method for assessing environmental, social and economic impacts of space missions. Despite this, the novelty of the concept within space mission design has meant that the approach has not yet been widely implemented. This paper successfully demonstrates this technique for the first time and presents LCE results of three SmallSat missions designed at the University of Strathclyde using the concurrent engineering approach. The Strathclyde Space Systems Database (SSSD) was deployed to calculate the total life cycle impacts of each mission, including the identification of common design hotspots. A novel technique called Multi-Criteria Decision Analysis (MCDA) was also trialled, whereby several impact categories were converted into single scores as a test case to reduce the learning curve for engineers. Overall, the LCE results indicate that the manufacturing & production of the launcher dominate the majority of impact categories. Other common hotspots were found to relate to the use of germanium as a substrate as well as the launch event. As an additional observation, in terms of the behavioural aspects, it was clear that study participants were more open to the concept of LCE with each new concurrent engineering session, evidenced by increasing levels of interaction amongst study participants. These findings are intended to provide industrial stakeholders with a preliminary benchmark relating to the general sustainability footprint of SmallSats, whilst demonstrating the viability of integrating LCE within the concurrent engineering process of space missions. [ABSTRACT FROM AUTHOR]

Models of Fuzzy Multi-Criteria Decision Analysis (FMCDA) are based, as a rule, on different approaches to fuzzy extension of source MCDA methods. For this, simplified models are used to approximate the functions of fuzzy variables with propagation of parametric fuzzy numbers (FNs) through all calculations. In this paper, authors suggest a novel approach to fuzzy extension of MCDA methods, for PROMETHEE-I/II, through development of fuzzy PROMETHEE-I/II (FPOMETHEE-I/II) models of different complexity: in addition to simplified models, the standard fuzzy arithmetic (SFA), and transformation methods (TMs) are implemented for assessing functions of FNs corresponding to these models. For ranking of alternatives, two defuzzification based, and one pairwise comparison ranking methods are implemented within the developed models. Special attention is paid to analysis of the overestimation problem, which can occur when using SFA in the presence of dependent variables in corresponding expressions, and to "proper fuzzy extensions" of PROMETHEE-I/II (i.e., results of all functions of FNs within the model are in accordance with the extension principle) based on TMs and, for some models, on the SFA. One of the key goals of this contribution is comparison of the distinctions in ranking alternatives by different FPROMETHEE-II models. It is demonstrated by evaluating a large number of scenarios based on Monte Carlo simulation that the probability of distinction in ranking alternatives by "proper" and "approximated" FPROMETHEE-II models may be considered as significant for ranking multicriteria problems. Another goal of this paper is analysis of the correctness of FPROMETHEE-I/II models with respect to the basic MCDA axiom related to ranking of dominated and dominating alternatives. Authors demonstrate that the basic axiom can be violated, in the general case, by all developed FPROMETHEE-I/II models and suggest an approach to fix this problem. [ABSTRACT FROM AUTHOR]

• Conservation priority areas (CPAs) was established by balancing ecosystem services. • Multi-criteria based CPAs selection was reliably verified by using single strategies. • In the CPAs, 4 services can achieve high conservation efficiencies simultaneously. • Under the workflow of the study, the proportion of biodiversity can be improved. • Various vegetation should be taken into account for future ecological management. Facing rapid ecological degradation in the Qinghai Tibet Plateau (QTP), the determination of priority areas for conservation (CPAs) based on ecosystem services (ESs) is a key step for nature conservation and sustainable development. However, the trade-off between services makes the plan of maintaining protected areas (PAs) a challenge. In this study, by quantifying a variety of essential ESs in the QTP from 2000 to 2015, we used the multi-criteria ordered weighted average method to balance the trade-offs and determine the key CPAs for ES conservation. We found that the multi-criteria based selection of CPAs leads to higher (1.35) and more balanced ES protection efficiency (PE) than the single strategy. The CPAs obtained based on the logical framework of this paper are mainly located in the southeastern QTP and cover an area of 12.78 % of the QTP (330,124.76 km2). The comprehensive PE of ES was 1.68, and the PE of carbon storage, water yield, soil conservation, and cultural service were 2.20, 2.17, 4.54, and 1.59, respectively. Additionally, compared with the existing PA settings, our results can increase the proportion of biodiversity of amphibians and birds by more than 12 %. The framework of CPAs selection in this paper can provide a reference for the government's growing nature conservation affairs, which will contribute to sustainable development. [ABSTRACT FROM AUTHOR]

It is common practice in the MCDA to use several multi-criteria decision methods and then compares obtained rankings with one or two different rank correlation coefficients. The problem is that different rank correlation coefficient gives different values for the same pair of rankings, and the number of studies which tries to investigate it is small. Studying the similarity of rankings is a very important challenge in multi-criteria decision support, and the coefficients themselves seem to be the most practical ways of evaluating rankings. This paper compares chosen rank correlation coefficients to show how much different they are. Spearman's, Weighted Spearman's, Kendall Tau and Rank similarity correlation coefficient are compared statistically. The paper confirms that the coefficients are closely related, and their dependence is graphically represented, which initiates research towards allows for their better selection in the future. In conclusions, directions of further development are indicated. [ABSTRACT FROM AUTHOR]

• Study analyzes 1374 papers on sensitivity analysis in Scopus. • RStudio, CiteSpace, and VOSviewer are used. • China has most articles published; India leads in international cooperation. • Top three cited journals: J. Clean. Prod., Expert Syst. Appl., and Comput. Ind. Eng. • Most cited authors are Pamučar D, Kahraman C, and Zavadskas EK. In the present era, the implementation of scientifically grounded Multi-Criteria Decision Making (MCDM) has emerged as a pivotal solution to diverse decision-making challenges across various domains. Although a substantial body of exploratory, conceptual, and experimental studies exists, only 9.457% studies have incorporated sensitivity analyses to assess the robustness of MCDM methods. An exhaustive scientific exploration of sensitivity analysis within the scope of MCDM is thus lacking. This research aims to address this gap through Bibliometric Analysis while examining 1374 articles published between January 2000 and March 2023 from the Scopus database. Using RStudio (Biblioshiny), CiteSpace, and VOSviewer software, the study constructs a visual representation of the most prolific countries, institutions, and authors. Impressively, China takes the lead in article publications, while India excels in international collaboration. "An extended TODIM multi-criteria group decision-making method for green supplier selection in an interval type-2 fuzzy environment," featured in the Journal of Environmental Management, emerges as the most cited paper with a total citation of 455. The study also identifies the top three most cited journals, namely "Journal of Cleaner Production," "Expert Systems With Applications," and "Computers and Industrial Engineering." "North China Electric Power University" is the leading institute with the highest research outputs. "Pamučar D" is the most cited author, with 2594 citations and 39 articles, followed by "Kahraman C" and "Zavadskas EK. This study sheds light on trends in scientific developments and collaborations, providing a model for the application of sensitivity analysis in MCDM research and highlighting global trends. An understanding of the current state of sensitivity analysis research can assist researchers working in the entire domain of MCDM. Additionally, the visualization provides prescriptive data for future work and applications related to sensitivity analysis. [ABSTRACT FROM AUTHOR]

• The centroid distance of PFNs is proposed and its properties are discussed. • Propose a score function based on the centroid distance and discuss its properties. • Applications of Pythagorean fuzzy clustering, ranking and MCDM are developed. • Some advantages of the proposed methods are showed through comparative analysis. By using Pythagorean fuzzy set (PFS), people can quantitatively describe and process information with fuzziness, and then make reasonable and effective judgments or decisions. In this process, distance measure often serves as a useful and important tool. The purpose of this paper is to explore some information processing methods and techniques in Pythagorean fuzzy environment. Firstly, the research progress of PFS is reviewed, and the main research directions and some hot issues in relevant research fields are discussed. Secondly, by analyzing the information of hesitation degree of Pythagorean fuzzy number (PFN), definitions of hesitation factor and centroid distance measure based on the representation of centroid coordinates of the hesitation regions are proposed, and properties of hesitation factor and centroid distance measure are discussed. Finally, the novel clustering algorithm, ranking method and multi criteria decision making (MCDM) method are developed in Pythagorean fuzzy environment, which are based on the new centroid distance measure. The effectiveness of the proposed algorithm and methods are verified through numerical examples and comparative analysis. The research results indicate that the centroid distance measure proposed in this paper can effectively characterize the distance between PFNs, thus making the clustering algorithm of PFNs, the ranking method of PFNs, and the Pythagorean fuzzy MCDM method have obvious superiorities and advantages. [ABSTRACT FROM AUTHOR]

In exploring alternative and more sustainable sources, hydrogen has played a dominant role as an energy vector and is suitable for reducing carbon emissions. However, given the properties of hydrogen and existing transportation challenges for this substance, studies that involve assessing risk when transporting hydrogen still need attention. Therefore, this paper presents a multi-criteria decision model to support organizations responsible for transporting hydrogen in a pipeline in risk management under a multidimensional perspective. In particular, we developed a search algorithm to select sections within the set that makes up the pipeline network (a portfolio-based approach), with a view to identifying the most critical areas in terms of risks and to optimizing the allocation of resources for mitigation while respecting current requirements. In addition, these risks are evaluated under three dimensions of loss: environmental, financial, and human. Hence, a numerical application is presented to illustrate the benefits of the model proposed. The results demonstrate that the model can help managers make more informed and strategic decisions about where to direct their risk management efforts to maximize pipeline safety and minimize incident risks. [ABSTRACT FROM AUTHOR]

Failure mode and effect analysis (FMEA) is one of the most powerful reliability analysis techniques for identifying and preventing potential risks across various fields. Current FMEA methods, while effective, still present several shortcomings. For example, using experts' subjective pairwise comparisons of risk factors to determine their weights reduces the stability of the result; different relationships among failure modes are often ignored. To improve the performance of FMEA, multi-criteria decision-making (MCDM) methods have been employed to support risk evaluation and prioritization in recent years. This paper proposes a novel FMEA method by exploring several MCDM techniques. First, the Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) are utilized to generate group risk assessments under uncertainty. Then, grey relation analysis (GRA) is incorporated into the decision-making trial and evaluation laboratory (DEMATEL) method to objectively determine the weight of risk factors. Afterward, the traditional ORESTE (organísation, rangement et Synthèse de données relarionnelles (in French)) method is generalized to the ELICIT environment to prioritize the failure modes, in which the Besson's ranking is replaced by deviation measure for more accurate ranking results. Finally, a case study of FMEA for electro-mechanical actuators is presented to illustrate the effectiveness of the proposed method. The results indicate that our approach can express risk information more flexibly, determine the weight of risk factors more objectively, and prioritize failure modes more reasonably. • The ELICIT information is utilized to flexibly assess the risk level of failure modes. • The weight of risk factors is more objectively determined by GRA-DEMATEL. • The traditional ORESTE is improved and extended into the ELICIT environment. • The proposed method is applied to analyze the risk of electro-mechanical actuators. [ABSTRACT FROM AUTHOR]

The probabilistic hesitant fuzzy sets (P-HFSs) allow experts to express their preferences regarding different membership degrees with probabilistic information. Fuzzy entropy is an important concept to measure the uncertainty of probabilistic hesitant fuzzy information. In this paper, we propose a novel fuzzy entropy named as the exponential probabilistic hesitant fuzzy entropy for the P-HFEs. This novel fuzzy entropy not only observes the basic property of fuzzy entropy, but also has the same characteristics and change tends with the existing fuzzy entropy proposed and studied by other scholars. Then, based on the cardinal reconciliation method and TODIM(Tomada de decisao interativa multicriterio) method, we propose a multi-criteria decision-making (MCDM) problem, where the attribute weight is determined by the exponential probabilistic hesitant fuzzy entropy of the integration results. Finally, a application case in green building is given for the sake of illustrating the efficiency of the MCDM problem. Numerical and theoretical results show that a MCDM problem based on the proposed novel fuzzy entropy and the TODIM method have a wide range of application. [ABSTRACT FROM AUTHOR]

Supply chain management is critical for any organization. To improve the performance of a firm's supply chain, it is important to have a freight forwarder evaluation process in place. This paper develops an analytical hierarchy process (AHP) methodology to perform a multi-criteria evaluation of freight forwarders. It analyzes different criteria that would be employed in the evaluation and selection of forwarders. The problem of choosing the best freight forwarder is evaluated in terms of quality of service, specialized service, network, competitive price, information and technology, and sustainability. A total of fourteen sub-criteria are considered including quality of service, network, information and technology, and sustainability. The study revealed that specialized service is the most important criterion with a weight of 54.3% followed by quality of service, competitive price, information and technology, network, and sustainability. Proven experience is the most important sub-criterion with overall weight of 36.2% followed by knowing the regulations with overall weight of 18.1%. Freight forwarder B is the best option with the highest weight with respect to proven experience and knows the regulations with values of 3.62 and 1.629, respectively. [ABSTRACT FROM AUTHOR]

Multi-criteria decision making (MCDM) methods have become increasingly popular in site selection decision-making of renewable energy power plants because they consider multiple conflicting goals and decision-maker preferences. In this paper, a systematic literature review of MCDM applications for renewable energy site selection is performed, covering a total of 85 papers published from 2001 to 2018 in high-level journals (chosen from the ScienceDirect database). This paper has summarized the exclusion criteria and evaluation criteria of site selection for five energy sources. The five site selection stages, criteria selection, data normalization, criteria weighting, alternative evaluation and result validation, are revealed by content analysis. All papers are further classified by the date, energy source, journal of publication, country of author affiliation, and study area. It is found that different energy sources emphasize different criteria; however, some similarities exist. Literature surveys and expert opinions are most common criteria selection method. Reclassification is most frequently used data normalization method. The analytic hierarchy process is popular for weighting. In alternative evaluation, geographic information systems and weighted linear combination are the most popular tools. Varying the criteria weights is most commonly used result validation method. • This is the first review of MCDM applications for renewable energy site selection. • It covers five energy sources: solar, onshore wind, offshore wind, wave and tidal. • It focuses on two types of criteria and methods in the five site selection stages. • It also involves methods for data normalization and result validation. • The combination of GISs and MCDM is the most popular tool in this research area. [ABSTRACT FROM AUTHOR]

TOPSIS and VIKOR are two well-known and widely-used multiple attribute decision making methods. Many extensions of these methods have been proposed that either use different techniques to rank alternatives or utilize fuzzy logic to handle alternatives and criteria that are unquantifiable and/or incomplete. In this paper, we present the implementation of a web-based decision support system that incorporates TOPSIS and VIKOR to solve multicriteria decision making problems either in a nonfuzzy or in a fuzzy environment. The aim of this paper is to present a tool that will be used by decision makers to compare various alternative solutions and understand how robust a decision will be. The proposed system can be used both in single and in group decision making problems. In addition, we review several variations for each step of these methods and implement different techniques when applicable. Hence, decision makers can experiment with different techniques in each method. We implement ten normalization techniques, three methods for the calculation of the ideal and anti-ideal solutions, fifteen distance metrics, five fuzzy distance metrics, and eight defuzzification techniques. An illustrative example is presented to highlight the key features of the implemented system and the different scenarios that can be built using the proposed DSS. [ABSTRACT FROM AUTHOR]

Hydrogen production from renewable energy sources appears to be an interesting solution for reducing greenhouse gas emissions and ensuring the energy security supply. This paper develops an integrated framework to evaluate land suitability for hydrogen production from solar energy site selection that combines multi-criteria decision making (MCDM) with geographical information systems (GIS); an application of the proposed framework for Algerian country. In GIS two types of criteria will be taken: constraints and weighting criteria. Constraints criteria will make it possible to reduce the area of study by discarding those areas that prevent the implementation of installing solar hydrogen production systems. These criteria will be obtained from the legislation (land use, water bodies, waterways, roads, railways, power lines, and also their buffer around them). Weighting criteria will be chosen according to the objective to be reached, in this case they will be the hydrogen demand, potential solar hydrogen production, digital elevation models (DEMs), slope, proximity to roads, railways, and power lines. Through the use of MCDM the criteria mentioned will be weighted in order to evaluate potential sites to locate a solar hydrogen production installation system. Analysis and calculation of the weights of these criteria will be conducted using Analytic Hierarchy Process (AHP). As a result, the final index model was grouped into four categories as "very low suitability", "low suitability", "moderate suitability" and "high suitability" with a manual interval classification method. The results indicate that 10.34% (246,272.02 km2), of the study area has very low suitability, 60.75% (1,446,907.65 km2) has low suitability, 6.68% (159,100.3 km2) has moderate suitability and 0.49% (11,669.21 km2) has high suitability for a solar-powered hydrogen production installation system. The other 21.74% (517,790.5 km2) of the study area is not suitable for such projects. The sensitivity analysis highlights that the suitable sites for solar hydrogen production installation system are dependent on the weights of the criteria that influence the decision. The MCDM methodology integrated with GIS is a powerful tool for effective evaluation of the solar-powered hydrogen production sites selection. • The paper addresses the problem of site selection for solar hydrogen projects. • Land suitability index is computed to determine the best sites. • MCDM coupled with GIS is a powerful tool for effective evaluation of the solar hydrogen production sites selection. [ABSTRACT FROM AUTHOR]

Considering sustainable development, technology selection is a multi-criteria decision-making (MCDM) problem that influences the advantages of enterprises or a country. This paper investigates existing wave energy technologies as alternatives to green and sustainable technologies to apply them in the seas and oceans. A framework is presented, and major technologies to extract wave energy are identified. Each technology has been examined in scopes of strategic factors, availability, technical knowledge, technological features, economic, social, and environmental factors as the main criteria. Totally, 20 converters and 52 criteria are considered. The Caspian Sea is considered as a case, and a decision matrix is established by integrating the gathered data and the experts' opinions. By integrating the fuzzy Delphi method (FDM) and preference ranking organization method for enrichment evaluations (PROMETHEE) method, Pelamis is considered the most appropriate technology to harvest wave energy in the Caspian Sea. [ABSTRACT FROM AUTHOR]

• Evaluated the suitable sites for implementing utility-scale Photovoltaic farms in Saudi Arabia. • Multiple technical and economic factors are incorporated. • Fuzzy AHP method is used to weight the criteria. • PROMEETHEE II method is used to evaluate the alternatives. Although Saudi Arabia can benefit from implementing photovoltaic (PV) solar power projects to generate power, there are some environmental, economic and technical challenges which can affect the efficiency and cost effectiveness of these facilities. The goal of this paper is to build a site suitability model to identify the suitable sites for implementing solar PV solar projects in Saudi Arabia. Fuzzy analytical hierarchy process (AHP), as a weighting technique, and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE II) method are combined to appropriately evaluate the suitable sites. 12 factors divided into two criteria (technical and economical) were incorporated to ensure minimization of construction costs while maximizing power output from the PV power plants. The resulting suitability map shows that Saudi Arabia has huge potential for implementing solar PV projects with approximately 376,623 km2 (65.1 %) of the total studied area considered, "most to highly suitable". In addition, a validation of the model's predictivity was conducted through an evaluation of the suitability map with respect to the future solar PV projects that Saudi Arabia is developing. The results showed that 90.6 % of the future projects fell within, "most and highly suitable" areas provided by PROMETHEE II suitability map. Furthermore, a sensitivity analysis was carried out by using different preference functions and higher weights for the economic criteria to examine the effect of economic factors toward the suitability results. [ABSTRACT FROM AUTHOR]

The fuzzy set theory as one of the key agents of artificial intelligence has been used to deal with vagueness and imprecision during the decision-making process. The software requirements selection is a multicriteria decision making problem which has a key importance for several software development companies. Few methods have been developed to select the software requirements from the list of the elicited requirements using fuzzy analytic hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) methods. Based on our review, we found that little attention is given on the comparison between the fuzzy AHP and fuzzy TOPSIS methods in the context of the software requirements selection problem. To address this issue, this paper presents a comparative analysis of fuzzy AHP and fuzzy TOPSIS methods by considering the small and large set of requirements of an institute examination system based on the following factors: agreement measure, time complexity, rank reversal issue, and number of judgments by decision makers. Based on the comparative study, we found that both fuzzy AHP and fuzzy TOPSIS methods produce the same set of functional requirements based on agreement measure metric in both dataset-1 and dataset-2. Fuzzy AHP requires less time to generate the ranking order in dataset-1; and fuzzy TOPSIS performs better then fuzzy AHP in dataset-2. Fuzzy AHP causes the rank reversal issue; and there is no rank reversal issue in fuzzy TOPSIS and it produces the consistent results. Fuzzy TOPSIS requires less judgment by decision makers then fuzzy AHP. Finally, we discuss the future research directions in the area of SRs selection problem. [ABSTRACT FROM AUTHOR]