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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]

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 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]

Properly designed and installed interior stairs provide safe and convenient access to all residents of the house, including children and elderly people. While safety considerations are paramount for stair design and placement decisions in a two-storey single-family house, sustainability becomes another important factor in these deliberations. Nevertheless, safety and sustainability of the interior stairs can be seen as conflicting objectives in decision-making processes (i.e., while a larger staircase area positively affect residents' climbing ability, it also causes higher energy consumption and material usage). Therefore, increased awareness of stair-project selection processes is required when multi-objective tasks are involved. Seven criteria affecting stair safety and sustainability in a single-family house are identified in this paper. The integrated AHP-MEREC criteria-weighting approach is proposed, to determine their weights in decision-making processes. Four different multi-criteria decision-making (MCDM) approaches, ARAS, SAW, TOPSIS and PROMETHEE, were used to determine alternative evaluations. Since the selection of the most suitable MCDM method is full of uncertainties, the novel combined-decision-making approach formally called CORST (COmbining MCDM appRoaches using method STability coefficient) is proposed in this paper. The new method stability coefficient is used to determine the stability and reliability of the specific MCDM approach. Nine U-shaped inside-staircase projects dedicated to a single-family house were evaluated, to test the effectiveness of the CORST approach. [ABSTRACT FROM AUTHOR]

This paper presents the application of a Multi-Criteria Decision Making (MCDM) method for the ranking of smart cities. During the construction of the MCDM techniques, the importance of the decision-making approach for the linear ordering of 66 Polish cities with powiat status was presented. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used for evaluation. The method has been verified by applying it to measure urban smartness. The TOPSIS method allowed compilation for a final ranking, taking into account publicly available indicators of the smart cities concept. The work uses data from the Local Data Bank Polish Central Statistical Office (LDB). The author conducted a literature review of research papers related to smart cities and MCDM methods dated from 2010 to 2020. Based on calculations using the TOPSIS method, the results obtained that the city of Krakow has the highest value to become a smart city. [ABSTRACT FROM AUTHOR]

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

Personnel evaluation and selection is an essential part of modern business. Appropriate candidate selection can significantly contribute to companies in terms of increased profit, good culture, reputation, reduced costs, etc. This paper addresses the personnel evaluation and selection problem at the University of Pardubice, Faculty of Transport Engineering (UPCE). Since this is a typical ranking alternative problem where multiple criteria affect the decision, the Z-numbers-based Alternative Ranking Order Method Accounting for the two-step Normalization (AROMAN) is applied. Four Ph.D. candidates are assessed, and the most appropriate is selected to be employed by the UPCE. The Z-numbers fuzzy AROMAN method ranks Ph.D. candidate number four as the most appropriate alternative. To investigate the stability and sensitivity of the Z-numbers fuzzy AROMAN method, the values of parameters β and λ used in the mathematical calculations of the method were changed. The results of sensitivity analysis revealed that the obtained solution is stable. To confirm the robustness of the proposed approach, a comparative analysis is performed. Simple Additive Weighting (SAW), Weighted Product Model (WPM), and Z-number fuzzy TOPSIS were applied. Besides, we applied the fuzzy inferior ratio method as well. The results confirm the high robustness of the proposed Z-numbers fuzzy AROMAN method. [ABSTRACT FROM AUTHOR]

Optimized production scheduling can greatly improve efficiency and reduce waste in the steel manufacturing industry. With the increasing demands on the economy, the environment, and society, more and more factors need to be considered in the production scheduling process. Currently, only a few methods are developed for the comprehensive evaluation and prioritization of scheduling schemes. This paper proposes a novel MCGDM (multi-criteria group decision making) method for the ranking and selection of production scheduling schemes. First, a novel indicator system involving both qualitative and quantitative indicators is put forward. Diverse statistical methods and evaluation functions are proposed for the evaluation of quantitative indicators. The evaluation method of qualitative indicators is proposed based on heterogeneous data, cloud model theory, and group decision-making techniques. Then, a novel Group AHP model is proposed to determine the weights of all evaluation indicators. Finally, a novel cloud-model-enhanced TOPSIS (technique for order of preference by similarity to ideal solution) method is proposed to rank alternative production scheduling schemes. A practical example is presented to show the implementation details and demonstrate the feasibility of our proposed method. The results and comparative analysis indicate that our hybrid MCGDM method is more reasonable, flexible, practical, and effective in evaluating and ranking production scheduling schemes in an uncertain environment. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid method based on a linear programming model for solving Multi-Attribute Decision-Making (MADM) problems by combining two new methods, the COmplex PRoportional ASsessment (COPRAS) and the Multi-Objective Optimization Ratio Analysis (MOORA) and also using the concept of discrimination intensity functions are presented. Further interaction with the Decision Maker (DM) to determine the weights of the attributes and calculate the weights by solving a linear programming problem without determining the predetermined weight are two of the advantages of the new method. In the proposed method, for each alternative, attributes are weighted with optimism for that alternative, and then alternatives are ranked through efficiency intervals. The proposed method is implemented on a real-world problem derived from the subject literature and compared with other MADM methods. The difference in the final results is evident due to the consideration of more details in determining the rankings. [ABSTRACT FROM AUTHOR]

In the current competitive environment and globalization, every organization is trying continuously to get better to stay competitive with their global rivals. So, most Manufacturing companies pursue to improve their productivity to meet the highly demanding business market by using effectively the resources, eliminating wastes, enhancement of process flow, and continuous improvement. There are many tools and strategies to improve productivity. Therefore, selecting appropriate strategies is an essential problem that faces most companies. Lean Manufacturing (LM) is a powerful system that improves efficiency, productivity, quality and reduces costs in any company. One of the reasons to fail the lean program's implementation is the lack of a clear understanding about what are lean performance and its evaluation. Lean level assessment can be considered a guide for the organization through their lean journey where it is the first step that led to the control and finally to improve the enterprise performances. Nowadays fuzzy Multicriteria Decision Making (FMCDM) can be considered one of the most efficient approaches to assess the lean performance level for any manufacturing enterprise to identify if the enterprise is near or far from the ideal implementation of the lean principles. The contributions of this review paper are; highlighting on Fuzzy Technique of order preferences by similarity to ideal solution( Fuzzy TOPSIS) and Fuzzy Multi criteria Optimization and compromise solution (FuzzyVIKOR) as distance-based methods for assessment the current lean performance for manufacturing enterprises and help them to improve continuously their performance ; review the most common lean assessment methods; highlighting lean dimensions and criteria that can be used in lean assessment by Fuzzy TOPSIS and Fuzzy VIKOR with illustrate the various criteria weighting methods. This review paper can be considered as a guide for researchers who interest in lean performance assessment that will fill some gaps in field of lean assessment level. [ABSTRACT FROM AUTHOR]

Due to increased demands of production capacity and higher quality requirements, industries are automating at a fast pace. Industrial robots are an important component of the industrial automation ecosystem. However, the selection of appropriate robots is a challenging task due to the sheer number of alternatives present and their varied specifications. The various characteristics or attributes of industrial robots that need due consideration before selection of an optimal robot for a given application are found to be conflicting in nature. Thus, in this paper, several multi-criteria decision-making (MCDM) methods are deployed to select an optimal robot depending on the application. Three different industrial robot selection problems are solved in this paper by using Simple Additive Weighing (SAW), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Linear Programming Technique (LINMAP), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Elimination and Choice Translating Priority III (ELECTRE-III), and the Net Flow Method (NFM). [ABSTRACT FROM AUTHOR]

An autonomous robot is often in a situation to perform tasks or missions in an initially unknown environment. A logical approach to doing this implies discovering the environment by the incremental principle defined by the applied exploration strategy. A large number of exploration strategies apply the technique of selecting the next robot position between candidate locations on the frontier between the unknown and the known parts of the environment using the function that combines different criteria. The exploration strategies based on Multi-Criteria Decision-Making (MCDM) using the standard SAW, COPRAS and TOPSIS methods are presented in the paper. Their performances are evaluated in terms of the analysis and comparison of the influence that each one of them has on the efficiency of exploration in environments with a different risk level of a “bad choice” in the selection of the next robot position. The simulation results show that, due to its characteristics related to the intention to minimize risk, the application of TOPSIS can provide a good exploration strategy in environments with a high level of considered risk. No significant difference is found in the application of the analyzed MCDM methods in the exploration of environments with a low level of considered risk. Also, the results confirm that MCDM-based exploration strategies achieve better results than strategies when only one criterion is used, regardless of the characteristics of the environment. The famous D* Lite algorithm is used for path planning. [ABSTRACT FROM AUTHOR]

The efficient collection of municipal solid waste poses a significant challenge for the prospective development of smart cities. Using Internet of Things (IoT) technology enables the detection of various kinds of waste-related information, facilitating the implementation of a comprehensive plan for efficient waste collection. In this paper, an innovative waste collection mechanism leveraging the IoT sensors and widely recognized Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a robust multi-criteria decision analysis method is introduced. Recognized for its ability to yield optimal solutions amidst multiple influencing parameters, TOPSIS proves particularly advantageous when confronted with diverse factors affecting decision making. A TOPSIS-based algorithm for optimal route selection is developed to collect waste using metrics such as toxicity, volume, and time duration of the waste while minimizing the route distance. To demonstrate the efficiency of our proposed algorithm, a comparative analysis with existing algorithms that are dependent on and optimized for single parameters is conducted. Through rigorous evaluation, it is shown that our proposed algorithm reduces the total distance traveled to collect the waste from all the bins by 14 % by effectively considering multiple criteria and optimizing waste collection routes. [ABSTRACT FROM AUTHOR]

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

This research paper presents an all-encompassing methodology for multi-criteria decision-making in the apparel sector, with the particular objective of aiding in the determination of the most appropriate location within the CLMV subregion. The research is conducted in three crucial stages. The process began with the administration of a survey to proprietors of garment businesses in both Thailand and the CLMV countries. This survey resulted in the compilation of an exhaustive list of site-selection criteria and sub-criteria. Based on the findings of subject matter-expert interviews, Cambodia (C), Vietnam (V), and Myanmar (M) were identified as feasible alternatives. Subsequently, the questionnaire criteria and sub-criteria were evaluated utilizing the Fuzzy Analytic Network Process (Fuzzy ANP), which involved the utilization of meticulously designed pair-wise comparison matrices and local priorities. Five specialists from the Thai entrepreneurial community affirmed the effectiveness of Fuzzy ANP and expressed interest in expanding manufacturing operations in the CLMV subregion. The optimal location for Thai apparel manufacturers was subsequently determined using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). The results indicated that Vietnam is the most favorable option. In order to improve the dependability of results, an amended mixture-design scenario analysis was implemented. This analysis assessed the sensitivity and dependability of the proposed model in different scenarios, ensuring its applicability in real-world situations. In contrast to traditional models, this study integrates managerial judgments and preferences into the decision-making procedure, thereby accounting for the complex interdependencies among numerous criteria. The suggested methodology functions as a beneficial instrument for decision-makers, both domestic and international, as it integrates effortlessly into the organizational structure of the CLMV region. By harmonizing objectives pertaining to data acquisition, manipulation, retention, and dissemination, this framework not only enables enhanced decision-making processes, but also optimizes system efficiency. [ABSTRACT FROM AUTHOR]

Websites of environmental content constitute an important tool for promoting environmental information, affect environmental attitudes and promote protected areas as touristic destinations. However, these websites have to be evaluated to ensure that they reach their final goal. The use of multi-criteria decision-making (MCDM) models in website evaluation is relatively new and not many models have been tested for this purpose. Comparisons of such models have been implemented in various domains but not for the purposes of environmental website evaluation. The main objective of this paper is on presenting the procedure of comparison of MCDM models spherical by providing in detail the steps that have to be followed. This process was implemented for website evaluation and investigated the comparative performance of the TOPSIS and VIKOR models. This comparison process involves reliability analysis of the questionnaire and the sample of decision makers, pairwise comparisons of the models by calculating the Pearson correlation coefficient and estimation of the Cohen's Kappa for testing the inter-rater comparability, using the models as raters. Furthermore, a sensitivity and robustness analysis of those models is implemented, which also has not been implemented before in the application of those models in website evaluation. The tests implemented and presented in this paper reveal that the reasonable disagreement that was often observed among the methods did not affect their reliability. As a result, MCDM models proved very effective for evaluating websites of environmental content. [ABSTRACT FROM AUTHOR]

Considering that moorings and anchorages for vessels have recently become an important factor in nautical tourism, the selection of their locations is a complex and demanding process. This paper examines numerous criteria from different perspectives to determine the most favourable/optimal locations for nautical anchorages, meeting the conditions and recommendations of professionals from several domains, by applying the methods of multi-criteria analysis. The goal of solving the problem this way is to meet the expectations of future users, spatial planners, possible investors, and concessionaires interested in doing business in these areas, as well as entities that strive to preserve and protect marine and underwater animal life and the environment by preventing their degradation and pollution. However, since there are no precisely defined recommendations for the establishment of nautical anchorages, in the procedures for determining the locations of nautical anchorages, it is possible to use general criteria they must fulfil. The best locations for nautical anchorages may be found, and this research represents a transparent, repeatable, and well-documented approach for methodically solving the problem. This is demonstrated by a comparison of many methods of multi-criteria analysis, utilizing a variety of parameters. On the other side, this calls for proficiency in a wide range of disciplines, including architecture, geodesy, marine safety and transport, architecture, biology, ecology, mathematical programming, operational research, information technology, environmental protection, and others. The best locations for nautical anchorages should be chosen based on the size and number of vessels, available space, depth, distance from the coast, level of protection of the anchorage waters, and many other limiting factors, keeping in mind that the spots which simultaneously satisfy a greater number of significant criteria are preferable. Using multi-criteria analysis methods (AHP (Analytical Hierarchy Process) and TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution)), evaluating and classifying criteria as well as assigning weight values to selected criteria, this paper investigates the possibility of obtaining the best locations from a group of possible ones. The most important factor when applying multi-criteria analysis methods refer to the following: vessel safety (navigation), hydrometeorological, spatial, economic, and environmental criteria. The main contribution of the paper displays in the proposal to optimize the decision-making process, when determining the optimal locations of nautical anchorages, in accordance with previously defined criteria. [ABSTRACT FROM AUTHOR]

Software testing plays a significant role in various software development phases. There are so many software testing techniques available. Selecting the most suitable software testing technique based on multiple factors is challenging for software practitioners. This paper proposes an MCDM-based hybrid approach for selecting the most appropriate software testing technique among various available software testing techniques, considering multiple factors such as cost, schedule, resources, etc. Because of the involvement of multiple factors, the problem of selecting the most appropriate software testing technique can be modeled as an MCDM problem. This study proposes a hybrid approach by integrating two MCDM methods BWM (Best-Worst Method) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), for evaluating various software testing techniques considering multiple factors altogether. For the applicability of the proposed approach, an experimental study was conducted using seven software testing techniques and six evaluation criteria. Results show the proposed approach can be used as an efficient tool for selecting the most suitable software testing technique among various available testing techniques in the presence of multiple factors. [ABSTRACT FROM AUTHOR]

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

The impact of disasters on the population and environment is an important research area. Multiple criteria need to be analyzed while making policy decisions in order to control the effect of a disaster. Researchers have used many variants of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Criteria Decision-Making (MCDM) method for prioritizing the alternatives. Additionally, the detrimental effects of disasters have compelled stakeholders to proactively prepare by strengthening crucial key elements of an Early Warning System (EWS) so that timely alerts can be produced. In this paper, a Disaster Information Provider (DIP) framework is proposed, which employs a TOPSIS variant to bolster weak elements of a people-centric EWS. Governments may utilize delivered rankings to strengthen the weak elements of the EWS in an affected area. Extensive experimentation proves the usability of the DIP framework for strengthening EWS. [ABSTRACT FROM AUTHOR]

Manufacturing resources allocation (MRA) is important area, and a significant challenge is encountered when considering high value, customized, complex structure and long lifespan of complex product system (CoPS). The relationship between uncertainty factors (i.e., inputs and outputs) of processes in CoPS's manufacturing, operation and maintenance needs comprehensive trade-offs in the preliminary MRA stage. Meanwhile, the CoPS's MRA schemes are contradictory from a customer's perspective with different emphasis on operating cost related to operation and maintenance stage. These problems are unavailable in traditional expressions for model and objective function. In this paper, a new variant of MRA multi-criteria decision-making (MCDM) model of CoPS (MRA&CoPS) is developed to evaluate MRA schemes with considering CoPS's lifecycle. Meanwhile, considering characteristics of CoPS and customer-involved MRA process, the three-layer criteria cumulative model is established. In the proposed method, intuitionistic fuzzy sets (IFSs) based subjective–objective hybrid fuzzy method is presented to deal with uncertainty of evaluation criteria. The weights of criteria are determined by the proposed intuitionistic fuzzy information entropy (IFIE). The hybrid IFIE-TOPSIS method is proposed to obtain the optimum MRA scheme by ranking results. An example of CoPS's MRA in a case enterprise is addressed to verify the rationality and validity of the proposed method. The results show that the proposed method is more preferable and robust in MCDM problem of MRA&CoPS. [ABSTRACT FROM AUTHOR]

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

Public–private partnership (PPP) infrastructure projects have attracted attention over the past few years. In this regard, the selection of private partners is an integral decision to ensure its success. The selection process needs to identify, scrutinize, and pre-qualify potential private partners that sustain the greatest potential in delivering the designated public–private partnership projects. To this end, this research paper proposes an integrated multi-criteria decision-making (MCDM) model for the purpose of selection of the best private partners in PPP projects. The developed model (HYBD_MCDM) is conceptualized based on two tiers of multi-criteria decision making. In the first tier, the fuzzy analytical network process (FANP) is exploited to scrutinize the relative importance of the priorities of the selection criteria of private partners. In this respect, the PPP selection criteria are categorized as safety, environmental, technical, financial, political policy, and managerial. In the second tier, a set of seven multi-criteria decision-making (MCDM) algorithms is leveraged to determine the best private partners to deliver PPP projects. These algorithms comprise the combined compromise solution (CoCoSo), simple weighted sum product (WISP), measurement alternatives and ranking according to compromise solution (MARCOS), combinative distance-based assessment (CODAS), weighted aggregate sum product assessment (WASPAS), technique for order of preference by similarity to ideal solution (TOPSIS), and FANP. Thereafter, the Copeland algorithm is deployed to amalgamate the obtained rankings from the seven MCDM algorithms. Four real-world case studies are analyzed to test the implementation and applicability of the developed integrated model. The results indicate that varying levels of importance were exhibited among the managerial, political, and safety and environmental criteria based on the nature of the infrastructure projects. Additionally, the financial and technical criteria were appended as the most important criteria across the different infrastructure projects. It can be argued that the developed model can guide executives of governments to appraise their partner's ability to achieve their strategic objectives. It also sheds light on prospective private partners' strengths, weaknesses, and capacities in an attempt to neutralize threats and exploit opportunities offered by today's construction business market. [ABSTRACT FROM AUTHOR]

Engineering and technological breakthroughs in sustainability play a crucial role in reducing carbon emissions. An important aspect of this is the active participation of enterprises in addressing carbon reduction as a systemic approach. In response to government incentives in the People's Republic of China, Chinese enterprises have developed carbon reduction systems to align their organizational goals with national long-term plans. This paper evaluates the carbon reduction schemes employed by six companies as a multi-criteria decision-making (MCDM) problem. To this end, we propose a new hybrid MCDM method called the grey-MEREC-MAIRCA method. This method combines the recently developed method based on the removal effects of criteria (MEREC) for weighting and multi-attribute ideal-real comparative analysis (MAIRCA) based on the grey system theory. The proposed hybrid method provides the additional benefit of accounting for uncertainty in decision making. Notable findings of this research, based on the decision-maker scores, are that the control of direct carbon emissions and energy-saving efficiency are top priorities. In contrast, committing to corporate social responsibility through carbon public welfare and information disclosure are considered lesser priorities. Furthermore, the ranking results obtained using this method are compared with those from the classical weighted sum model and the technique for order preference by similarity to ideal solution (TOPSIS), confirming the selection of the best company. Despite the limitation of the proposed method and the additional steps needed in the evaluation, it opens up opportunities for future research to develop simpler MCDM methods under uncertainty. [ABSTRACT FROM AUTHOR]

Purpose: The main purpose of the paper is to apply the TOPSIS method for selecting the optimal contractor for a passenger transfer center. Methodology: The methodology is based on the use of TOPSIS, which determines the distance of decision alternatives from the ideal and anti-ideal solutions using evaluation criteria. Results: Two different contractor rankings were obtained depending on the weights of the criteria. With equal weights, the best contractor is number 5, while with weights determined by formula (8), the best is number 4. Theoretical Contribution: The paper contributes to the field of investment project management by demonstrating how the TOPSIS method can aid in decision-making for optimal contractor selection, thereby reducing the risk of erroneous decisions. Practical Implications: The practical implications of this research are significant for investment projects, as the demonstrated methodology can be directly applied to the contractor selection process, potentially leading to more successful project outcomes. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) or 3D printing has been playing a very important role in the manufacturing sector in recent decades. The AM basic process is meant to produce an object layer by layer and has many advantages that include the occurrence of only minimal production waste during production and easy manufacture of even the most geometrically complex materials. However, there are many challenging decision-making situations in the production of AM for its users, for example, the build chamber, material specification, technology types, and application requirements. This includes the choice of the best AM machine (AMM) from many AMM with slightly different features that are identical on the market, especially on a real-time basis. This research explored ways that AMM is to be selected using multi-criteria decision-making (MCDM) on a real-time basis. This includes the use of the MCDM fussy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to help select the most suitable fusion deposition modeling (FDM) for an Indian nongovernment organization (NGO) from nine different machines based on contemporary. Practice in this research paper, an Indian NGO is considered as a decision-maker in the choice of the best FDM machine based on nine common criteria because an NGO has prescribed nine different FDM machines and the NGO needs the help for the purchase of the suitable FDM to produce different fields of prototypes. The outcome of this research is to recommend a suitable FDM machine from the nine similar to slightly different features of FDM machines by the suggestion of the field experts (AM machine users). The contribution of this research is not only to enable the purchase of the suitable AM machine but also to reveal the various contemporary FDM machines and the general criteria to be considered in choosing them. [ABSTRACT FROM AUTHOR]

Multicriteria methods have gained traction in academia and industry practices for effective decision-making. This systematic review investigates and presents an overview of multi-criteria approaches research conducted over forty-four years. The Web of Science (WoS) and Scopus databases were searched for papers on multi-criteria methods with titles, abstracts, keywords, and articles from January 1977 to 29 April 2022. Using the R Bibliometrix tool, the bibliographic data was evaluated. According to this bibliometric analysis, in 131 countries over the past forty-four years, 33,201 authors have written 23,494 documents on multi-criteria methods. This area's scientific output increases by 14.18 percent every year. China has the highest percentage of publications at 18.50 percent, followed by India at 10.62 percent and Iran at 7.75 percent. Islamic Azad University has the most publications with 504, followed by Vilnius Gediminas Technical University with 456 and the National Institute of Technology with 336. Expert Systems with Applications, Sustainability, and the Journal of Cleaner Production are the top journals, accounting for over 4.67 percent of all indexed works. In addition, E. Zavadskas and J. Wang have the most papers in the multi-criteria approaches sector. AHP, followed by TOPSIS, VIKOR, PROMETHEE, and ANP, is the most popular multi-criteria decision-making method among the ten nations with the most publications in this field. The bibliometric literature review method enables researchers to investigate the multi-criteria research area in greater depth than the conventional literature review method. It allows a vast dataset of bibliographic records to be statistically and systematically evaluated, producing insightful insights. This bibliometric study is helpful because it provides an overview of the issue of multi-criteria techniques from the past forty-four years, allowing other academics to use this research as a starting point for their studies. [ABSTRACT FROM AUTHOR]

Management of nonurban road network maintenance is a complex management process that requires the inclusion of many technical, economic, and other characteristics of the problem, as well as the continuous application of new knowledge and approaches, to maintenance management. To effectively manage the maintenance of the road network in conditions of limited financial resources, maintenance is examined through three interrelated management functions of planning: implementation, monitoring, and maintenance control. This paper includes an analysis of current and relevant papers on this topic. Based on the conducted analysis, the paper gives a theoretical framework and proposes a management model for road network maintenance between urban areas. This model supports the previously mentioned management functions using Multicriteria Decision-Making Methods. The AHP method and the TOPSIS method are engaged in the process of generating a priority road maintenance plan and Earned Value Analysis in maintenance monitoring and control. The AHP method is used to determine the criteria weight vector as a key role in defining the rank of alternatives and in identifying the optimal maintenance rank of nonurban roads using the TOPSIS method. All of the above aim to ensure the safe flow of traffic and the permanent preservation of the construction, traffic, and economic value of roads between urban areas. [ABSTRACT FROM AUTHOR]

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

This paper represents two stage fuzzy methodology for the optimal planning of order of establishment of temporary logistics hubs (TLHs). At the first stage, a q-rung orthopair fuzzy TOPSIS approach for formation and representing of expert's knowledge on the parameters of the order of establishment of temporary logistics hubs is developed, when resources (mobile storage units used as TLHs) are limited. At the second stage, based on the constructed fuzzy TOPSIS aggregation a new objective function is formulated. Constructed criterion maximizes TLHs' total identification level of the order of establishment. This criterion together with second criterion - minimization of number of selected TLHs', creates the multi-objective facility location set covering problem. Two stage approach is illustrated by the simulation example of emergency temporary logistics hubs' selection preferences identification for a city in Georgia. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Heating, Ventilation, and Air-Conditioning (HVAC) systems are critical components of maintaining an indoor air quality that ensures the thermal comfort of occupants in diverse building types. However, HVAC systems are also responsible for a substantial portion of the total energy consumption of commercial and industrial office buildings. This paper presents an integrated approach of two powerful MCDM techniques: the Best-Worst-Method (BWM) and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in order to evaluate and rank HVAC systems to ensure the best selection toward designing a sustainable office building. A set of conflicting criteria reported by international sustainable assessment centers were employed along with various HVAC systems to develop what is called herein a BWM-based TOPSIS model in order to conduct such a novel research attempt. Within the context of the investigated office building herein, the mini-package ductless system appears to be the best choice. This study could be further enhanced by including additional criteria and a larger sample size in future studies. [ABSTRACT FROM AUTHOR]

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) are among the most employed approaches for aggregating performances in Multi-Criteria Decision-Making (MCDM). TOPSIS and SAW are two MCDM methods based on the value function approach and are often used in combination with other MCDM methods in order to produce rankings of alternatives. In this paper, first, we analyse some common features of these two MCDM methods with a specific reference to the additive properties of the value function and to the sensitivity of the value function to trade-off weights. Based on such methodological insights, an experimental comparison of the results provided by these two aggregation methods across a computational test is performed. Specifically, similarities in rankings of alternatives produced by TOPSIS and SAW are evaluated under three different Minkowski distances (namely, the Euclidean, Manhattan and Tchebichev ones). Similarities are measured trough a set of statistical indices. Results show that TOPSIS, when used in combination with a Manhattan distance, produces rankings which are extremely similar to the ones resulting from SAW. Similarities are also Experimental results confirm that rankings produced by TOPSIS methods are closer to SAW ones when similar formal properties are satisfied. [ABSTRACT FROM AUTHOR]

This paper introduces and further applies an approach to support the decision makers in construction projects differentiating among a variety of deep excavation supporting systems (DESSs). These kinds of problems include dealing with uncertainty in data, multi-criteria affecting the decision, and multi-alternatives to select one from them. The proposed approach combines the analytic hierarchy process (AHP) with the fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) in a multicriteria decision-making (MCDM) model. The MCDM model emphasize the ability to combine expert knowledge, cost calculations, and laboratory test results for soil properties to achieve the scope. The model proved it had a superior ability to deal with the complexity and vague data that are related to construction projects. Furthermore, it was applied to a real case study for a governmental housing project in Egypt. Secant pile walls, sheet pile walls, and soldier piles and lagging are selected and studied as being the most common DESSs and as they satisfy the project requirements. The model utilized four criteria and fourteen comparing factors, including site characteristics, safety, cost, and environmental impacts. Based on the results of the model application on the investigated case study, a decision was reached that using secant piles as a supporting system in this project is mostly preferred. Furthermore, sheet pile wall, and soldier piles and lagging, come next in the ranking order. A sensitivity analysis is carried out to investigate how sensitive the results are to the criteria weights. In addition, the paper discusses in detail the reasons and factors which affect and control the decision-making process. [ABSTRACT FROM AUTHOR]

Sustainable design of infrastructures has become a major matter of study since the recent establishment of the Agenda 2030. This paper provides a systematic literature review on the use of multi-criteria decision making techniques used so far for the sustainable design of bridges. Special attention is put as well on how the reviewed studies assess the sustainable performance of bridge designs along their life cycle from the economic, the environmental and the social perspective. Although SAW and AHP are recurrently used in the sustainable assessment of bridges, the analysis of the most recent articles show that the application of TOPSIS and PROMETHEE techniques are gaining increasing relevance for such purpose. Most of the studies focus on the research of the construction and the maintenance stage of bridges. However, a need for further analysis is identified when it comes to the assessment of the impacts resulting from the End of Life cycle stage of bridges from a sustainable point of view. The use of intuitionistic and neutrosophic logic have been detected as emerging alternatives to the fuzzy approach of decision making problems. [ABSTRACT FROM AUTHOR]

This paper examines the performance evaluation and lockdown drivers of the UK's NHS (National Health Service) during the COVID-19 pandemic. The study aims to enhance the NHS's response to future health crises and guide government lockdown decisions. Lockdown drivers encompass vital resources like beds, ventilators, patients, and staff. A three-stage MCDM (Multiple-Criteria Decision Making) approach is employed to analyze performance scores. First, partial utility functions or partial distances are computed using COPRAS (Complex Proportional Assessment) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), respectively. Second, the Latent Vagueness and Randomness Components (LAVRA) method filters unbiased performance scores from uncertain components. Third, a bootstrapped neural network regression classifies lockdown drivers based on performance, deaths, and geographic regions. Crucial drivers relate to ventilated bed availability, while less critical ones include staff absence due to COVID-19 and a high admission rate of elderly inpatients. The results indicate performance scores range from 0.65 to 0.75 using TOPSIS, while COPRAS analysis significantly reduces the scores. Lockdown decisions are influenced by geographic regions, death tolls, and unbiased hospital performance scores. [ABSTRACT FROM AUTHOR]

Multi-Criteria Decision Making (MCDM) is a significant challenge across various domains, requiring adept resolution of conflicts arising from diverse objectives and criteria. This study proposes an innovative approach aimed at optimizing controllability, minimizing irreversibility, and maximizing overall effectiveness in control system design to address this challenge. The primary objectives of this study are to introduce a novel methodology for selecting Heat Exchanger Networks (HEN) using the well-established Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Additionally, a closeness coefficient is introduced to rank alternatives networks based on their proximity to the ideal solution. Two illustrative case studies are presented to showcase the methodology's effectiveness, adaptability, and robustness in discrete multicriteria decision-making problems, particularly in the context of HEN selection. Consistently identifying HEN configurations that fulfill controllability objectives, the methodology demonstrates its effectiveness and potential for broader applications beyond HEN optimization. The case study results affirm the adaptability and robustness of the proposed approach. In summary, this paper introduces an original and versatile approach to address the complexities of multi-criteria decision-making, specifically in the context of HEN selection. Rooted in the TOPSIS method and fortified by the closeness coefficient, the methodology holds promise for intricate decision-making processes and offers transformative possibilities for control system design. The study concludes by inviting further exploration of the proposed methodology, emphasizing its significant contribution to the field and its potential for widespread impact. Researchers and practitioners are encouraged to investigate and apply this innovative approach in diverse decision-making scenarios. The ranking results reveal that alternatives M and K is the optimum one among all the alternatives for both cases with a closeness coefficient equal to 0.651 and 0.971. [ABSTRACT FROM AUTHOR]

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

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]

The frequency of catastrophic natural disasters is rising, and much emphasis is being given to the Humanitarian Supply chain (HSC). The main goal of relief efforts is to get enough emergency supplies to the area hit by the disaster as quickly as possible. The decision of where to locate warehouses that will store relief supplies presents a significant obstacle for humanitarian relief organizations as they work to enhance their capacity for providing aid and their rescue plan. A non-optimal location could make the search and rescue efforts harder. More importantly, it has been seen that when these kinds of geographical sites are evaluated, social and environmental issues are not considered. This research paper aims to make humanitarian networks more accountable by determining the ideal warehouse site and considering both traditional and sustainable factors. A framework for selecting warehouses to keep relief goods was devised using the Multi-Criteria Decision Making (MCDM) approach. Best-Worst and TOPSIS ("Technique for Order Performance by Similarity to the Ideal Solution") methods were used to rank the potential locations based on Cost, Logistics, Environmental, and Social Criteria. A research study has been done in the State of West Bengal (District Arambagh). [ABSTRACT FROM AUTHOR]

Research background: Sustainable development of the modern world represents an opportunity to preserve economic growth and technological progress, as well as social development, without limiting the possibilities of this development for past generations. The directions of this development are included in the 17 goals and 169 tasks of the 2030 Agenda for Sustainable Development. The achievement of these goals and the implementation of the adopted tasks is a huge challenge for individual countries and regions. This also applies to the European Union (EU), where economic development is closely linked to environmental protection and social inclusion. Of key importance in this context is Objective 9 of Agenda 2030, and thus its level of implementation in the EU-27 countries is the aim of the research presented in this paper. Purpose of the article: The research involved assessing the level of EU countries in terms of building stable infrastructure, promoting sustainable industrialization and fostering innovation, i.e., the main areas of Goal 9 of Agenda 2030. Methods: The assessment was based on the EU–27 countries' sustainable development index (SDG9) determined with the use of 14 indicators characterizing these areas between 2015– 2020. The basis of the developed methodology was a multi criteria decision making approach (MCDM methods). TOPSIS, WASPAS and EDAS methods were used to determine the sustainability index, and the Entropy, CRITIC and standard deviation (SD) methods were used to determine weights for the adopted indicators. In addition, the use of the Spearman's and Kendall's Tau non-parametric tests enabled the analysis of the relationship between the SDG9 index and the basic economic, environmental and energy parameters, as well as the digitalization of the countries under study. Findings & value added: The results show that the EU–27 countries vary widely in terms of implementing Sustainable Development Goal 9 of Agenda 2030 over the analyzed period. Now, the most advanced in this respect are Denmark, Germany, Luxembourg, the Netherlands, Finland, and Sweden. By contrast, substantial problems are found in Bulgaria, Greece, Portugal, and Lithuania. The results also provide an opportunity to trace changes in the value of the designated index in individual countries, and in groups of countries of the "old" and "new" EU. These results significantly enrich the knowledge of the effectiveness of implementing Goal 9 of Agenda 2030 in the EU–27 countries and the relationship between the development of individual countries and sustainable development economy. These findings can also be used to create new EU–27 strategies for sustainable and solidarity-based development of the whole EU. In addition, the results can be helpful to decision-makers as they highlight important indicators related to innovation, industrialization and infrastructure that should be considered when formulating a country's sustainable development strategy. The added value of the study is the research procedure presented, which can be used in analyses on the study of various issues related to sustainable development for other groups of regions. [ABSTRACT FROM AUTHOR]

The inherent complexity of large ships makes it challenging to evaluate ship designs systematically and scientifically. Knowledge-based expert systems can be reasonable solutions. However, this problem needs more rationality and better operability, especially in complicated ship-equipment suitability evaluation problems with numerous indicators and complex structures. This paper presents a hybrid multi-criteria decision-making (MCDM) framework to extend the ship-equipment suitability evaluation to group decision-making settings, where individual consistency and group consensus are thoroughly investigated to improve rationality and operability. As a result, an improved Interpretive Structural Modeling (ISM) method is developed to construct the evaluation index systems. Furthermore, based on an applicability analysis of the selected MCDM methods, an improved Analytical Hierarchy Process (AHP) method is proposed to distribute the index weights, and an applicable Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) method is utilized to evaluate and select appropriate ship designs. Finally, a ship-equipment environmental suitability evaluation case is examined. The results indicate that the proposed framework improves the rationality and operability of the decision-making process and provides practical support to decision-makers for the systematic and scientific evaluation of ship designs. Therefore, it can also be applied to other ship design evaluation and selection problems. [ABSTRACT FROM AUTHOR]

Classification algorithm selection is an important concern for breast cancer diagnosis. The traditional routine of adopting a unique performance metric for evaluating classifiers is not adequate in the case of micro-array gene expression dataset. This paper introduces an MCDM technique to evaluate classification algorithms in breast cancer forecasting by seeing different performance measure along with feature space. An empirical study is designed to support an overall assessment of classifiers on micro-array datasets using well-known MCDM technique. TOPSIS is used to rank 11 prominent assessment criteria of different classifiers. First, the sequence order of 20 classifiers along with 11 assessment criteria is generated. Further topmost classifiers are grounded on their performances highlighting the role of feature selection in the overall process supporting the genuine assessment of classifiers over any solitary performance criteria. Result indicates that AdaBoostM1 and Iterative Classifier Optimizer are graded as topmost classifiers without and with feature selection, respectively, grounded on their performances on different measures. Furthermore, the proposed MCDM-based model can reconcile distinct or even inconsistent evaluation performance to grasp a group agreement in a complicated decision-making environment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Fibers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Researchers tend to develop cluster studies when the ways of turning to the circular economy are considered. Clusters are viewed as a network where different institutions, enterprises, and research centres are connected to share their knowledge and resources for better results of their performance. Efficient use of resources can be achieved in such networks through involvement in the circular economy. Clusters with their resources and knowledge as contributors in transition to a circular economy are analysed in this paper. The paper aims at literature analysis where clusters and circular economy are overviewed. The links between these two notions are traced, and the relation of clusters in transition to a circular economy is verified through the application of several multicriteria decision-making and mathematics-based information analysis methods. Scientific literature analysis works for the identification of the main concepts and definition of the object. The qualitative and quantitative analysis employs multi-criteria decisionmaking (MCDM) methods (SAW, TOPSIS) and regression analysis. A tool that enables verification of relation between clusters and transition to the circular economy was employed using these methods. The findings suggest that the tool used in the research can be applied when the relation of clusters and transition to a circular economy is being traced. The paper suggests experts' selection through their work experience with clusters and/or circular economy and their evaluation for certain clusters in transition to a circular economy set of criteria. The zero values of some indicators were eliminated by mathematically recalculating the weights so that distortion of the results after the application of MCDM methods is avoided. The results of MCDM methods application in regression analysis show that there is a possible relationship between clusters and transition to a circular economy. [ABSTRACT FROM AUTHOR]

Modern manufacturing's objective is to guarantee quality while lowering production costs and increasing productivity. In this context, the selection of cutting materials is important in increasing productivity due to their wide range of applications. In this paper, when machining AISI 4140 alloy steel, an experimental study is presented to find the cutting conditions that result in minimum Ra, Vb, and maximum MRR values. Different optimization methods are used, namely, Taguchi, grey relational analysis (GRA), technique by order of preference by similarity to ideal solution (TOPSIS), and multi-objective optimization ratio analysis (MOORA). Taguchi's L16 design was used to arrange 16 experiments. Cutting speed (Vc), feed rate (f), and depth of cut (ap) were the input parameters, with four levels for each cutting parameter. The feed rate and cutting speed had the greatest effect on Ra and Vb, according to an ANOVA analysis of the experimental results. MRR was significantly affected by the depth of cut and feed rate. To achieve the minimum surface roughness, flank wear, and maximum material removal rate, the cutting speed, feed rate, and depth of cut were required to be 250 m/min, 0.11 mm/rev, and 1.4 mm, respectively, according to the optimization results. [ABSTRACT FROM AUTHOR]

Reliable and effective selection of logistic handling operator is a particularly demanding process due to the short reaction time or high level of accompanying stress. Moreover, diversification of transported cargo makes use of classical indicators and methods of carrier selection highly unsatisfactory for decision-makers. To solve this problem, managers are seeking multi-criteria decision methods that improve the decision-making process related to the selection of the carrier and reduce the risk indicator related to the incorrect implementation of the transport order. Thus, in this paper, we present a Multi-Criteria Decision-Making (MCDM) approach for selecting logistic handling operators under partial or incomplete information (uncertainty) and taking into account the different type of transported cargo. The proposed approach comprises 2 main steps. In the 1st step, we identify the input parameters, mainly connected with criteria for carrier selection depending on the type of transported cargo. In the 2nd step, experts provide linguistic ratings to the potential alternatives against the selected criteria and the best alternative is chosen. At this stage, the fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) approach is used. Later, the applicability of the developed method is presented based on the chosen case company. The comparison of classical and fuzzy approaches to decision-making process is also given. [ABSTRACT FROM AUTHOR]

Site selection for an institute or a university is a challenging task. The selection of sites for setting up a new university depends on multiple criteria. In backward, under privileged area people's perception towards the co-educational universities and women universities are different. Poor families with their conservative mentality possess inhibitions while sending their girl child to co-educational universities as they have concerns about safety, security and family honor. Hence many attributes which are not so important for co-educational universities are more pertinent for women university. In this research paper, we have considered a model for selecting women's university sites in different backward locations in the state of West Bengal, India. This model incorporated different types of uncertainty related to site selection. Ten important criteria are chosen for the selection of sites. To capture the uncertainty of the problem, trapezoidal neutrosophic numbers are used along with the Multi-criteria Decision Making tool Analytic Hierarchy Process (AHP) for obtaining criteria weights. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and COmplex PRoportional ASsessment (COPRAS) are applied for ranking of the sites. Comparative and sensitivity analyses are conducted to check the steadiness of the techniques used. [ABSTRACT FROM AUTHOR]

The business process modeling tool selection problem has a significant impact on the overall performance of enterprise business process modeling, which will directly affect the development of enterprise information systems. Apart from that, the process to select the business process modeling tool from all alternatives is a Multi-Criteria Decision Making (MCDM) problem. This paper develops a methodology based on the hybrid fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method to help companies select the optimal business process modeling tool, where the business process modeling process is more efficient, economic and safe. The proposed method has the following state-of-the-art contributions and features: (1) the latest application of the MCDM methodology to the field of BPM tool selection, (2) addressing the direct and indirect impact between criteria in the selection of BPM tools, and (3) considering the hybrid fuzzy (uncertainty) decision-making issue in the BPM tool selection process. Meanwhile, the mathematical formula in TOPSIS can be regarded as a formula for solving a symmetric problem. The hybrid fuzzy DEMATEL method is used to obtain the weight for the criteria to be considered in the BPM tool selection process, and the TOPSIS method is used to obtain the final business process modeling tool. [ABSTRACT FROM AUTHOR]

One quarter of global energy consumption goes towards meeting transport needs. In Europe, the share of energy for transport is much higher and accounts for about a third. Therefore, it is very important to monitor the sustainable development and progress of the sector. This paper seeks to develop a framework for the sustainability assessment of road transport in EU countries and to evaluate the countries' achievements in the last decade. The research adheres to the provision that the developed framework should be easily applied in future studies. Therefore, significant attention is paid to the selection of indicators and their availability, as well as the selection of the research instrument itself. The multi-criteria decision-making (MCDM) technique TOPSIS has been applied for calculations and countries' ranking, in order to compare countries' achievements in the last decade (2010–2020). The last ten years' analysis allows us to identify the direction of individual countries in developing road transport. [ABSTRACT FROM AUTHOR]