Technique For Order Preference Research Articles

Evaluation of top tourism destinations according to their AI development with the entropy-TOPSIS-integrated method

PurposeThis study aims to determine and evaluate the artificial intelligence (AI) development and competitiveness of the top 20 countries that receive the highest number of tourists with the entropy technique for order of preference by similarity to the ideal solution (TOPSIS)-integrated method.Design/methodology/approachThis study is based on Global AI Index data published by Tortoise Media. Based on this index, according to the World Tourism Organization (UNWTO) report, the top 20 destinations that will host the highest number of tourists in 2022 were evaluated in seven different subpillars, which are talent, infrastructure, operating environment, research, development, government strategy and commercial. These seven subpillars of the index were considered as criteria, and the top 20 tourist destinations were included in the research as decision alternatives.FindingsThe analysis results show that the three most important AI criteria are operating environment, infrastructure and government strategy. Furthermore, the first three countries with the best AI performance according to the weighted criteria were the USA, China and the UK, respectively.Practical implicationsConsidering that AI technologies will direct tourist behavior in a world where technology is rapidly developing, it is recommended that the countries that receive the highest number of tourists improve their AI performance.Originality/valueWhen the relevant literature is examined, there is a limited number of studies examining the AI development and competitiveness of the top tourist destinations and weighting the Global AI Index values. Therefore, this study contributes to the gap in the relevant literature.

Aviation Operators’ Total Loads Analysis by Multi-Criteria Decision-Making

The aviation operator's role and duties undergo a dramatic change from direct engagement to information management and surveillance in today's advanced aviation environment. In this modern and highly automated working environment, operators must handle various information, data, and transmission during operations, particularly while making decisions in specific situations. The total load system of operators (information, communication, mental, task, and workload) has been fundamentally reshaped in response to these dynamic changes. The highly automated system is linked to unbalanced operator loads, unanticipated losses in situation awareness, inappropriate decision-making quality, and higher stress levels. This research employs the Analytical Hierarchy Process (AHP) as a foundational framework, integrating the Fuzzy Analytical Hierarchy Process (FAHP) on the AHP model to address uncertainties and applying the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) for ranking the aviation operators' total loads. online Survey distributed among 63 experts across four aviation operator groups: less experienced pilots, expert pilots, less experienced Air Traffic Controllers (ATCOs), and experienced ATCOs. serves to evaluate the pivotal characteristics of the operators' total loads model. The results highlighted the communication load's effect on the aviation operators specifically the level of the language as well the workload specifically both practicing referring to the experience and skills.

Integration of proton exchange membrane fuel cell with air gap membrane distillation for sustainable electricity and freshwater cogeneration: Performance, influential mechanism, multi-objective optimization and future perspective

Proton exchange membrane fuel cell (PEMFC) converts vast majority of hydrogen energy into waste heat, resulting in energy wasting, membrane drying and even lifetime shortening. Herein, air gap membrane distillation is proposed to immediately remove and harness the waste heat from PEMFC for additional freshwater production. By quantifying the irreversible thermodynamic and electrochemical losses, mathematical formulas for the water production, power generation and overall efficiency are derived to evaluate the system performance. After a rigorous model validation, the performance feature, feasibility and competitiveness of the proposed system are examined. The integration system achieves a peak power density 23.69 % higher than that of a single PEMFC at 353 K, with a corresponding increase in exergy efficiency by 3.61 %. In addition, the influential mechanisms of the PEMFC operating conditions, air gap thickness, coolant temperature, and properties of the proton exchange membrane and hydrophobic porous membrane are investigated to discern potential avenues for further performance improvement. The gradient-based local sensitivity analyses further determine the optimal parameter regulation strategies for different output targets. The economic study indicates that the levelized costs of water and electricity over the total lifecycle are 21.53 $ m−3 and 0.061 $ kWh−1, respectively. The multi-objective optimization, integrating genetic algorithm and the technique for order preference by similarity to an ideal solution, maximizes the exergy efficiency and output power density while minimizing the initial investment cost per unit area, with values of 42.84 %, 0.975 W cm−2, and 27.33 $ cm−2, respectively.

A Combined Ranking and Sensitivity Analysis of Power Generation Using Multi-Criteria Decision-Making and Monte-Carlo Simulation

This research examined energy sources that can be employed in a region to assist policymakers in determining energy priorities. Three key components were analyzed in this research to rank these energy sources: Levelized Cost of Energy (LCOE), CO2 emissions, and power density. A combination of multi-criteria decision-making (MCDM) methods, namely the Analytical Hierarchy Process (AHP)-Entropy-the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), was used to assess these criteria, which had not been previously applied to rank energy sources. Additionally, the Monte-Carlo method was utilized to detect changes in sensitivity throughout the rankings. Results of the study indicated that gas energy topped the list, followed by Solar Photovoltaic (PV)-crystalline, geothermal, wind, nuclear, Solar PV Commercial & Industrial (C&I), Solar Thermal Tower with Storage, and residential PV rooftop solar. Moreover, nuclear energy ranked the highest when looking at the sensitivity of parameters, while utility-scale Solar PV and wind energy ranked the next highest. Thus, this research can be used to increase objectivity in the assessment and selection of power generation technology to be implemented.

Use of measurement results from a mobile laboratory in the diagnostics of anthropogenic environment - linear ordering of objects using the TOPSIS method

The measurements of the concentrations of gaseous and dust pollutants in the anthropogenic environment are an important element of environmental monitoring and for determining directions of preventive activities in the field of health protection. The article presents the results involving the concentrations of suspended dust and gaseous pollutants in the outdoor air, which were recorded at three measuring stations of air quality in the Silesian and Opole voivodeships (Wodzisław Śląski, Zabrze, Kędzierzyn-Koźle). The results were supplemented with the values recorded by the mobile laboratory located at the Center for Continuing Education - Branch of the Silesian University of Technology in Rybnik. The research results were used for a synthetic assessment of the threat level to the anthropogenic environment. In the computational layer, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed, which is included in the group of methods for solving multi-criteria decision-making problems (Multi Attribute Decision Making).

Multi-Criteria Optimization of Al2O3-ZrO2(Y2O3)-based Self-lubricating Composite Cutting Tools for Increasing Shear Angle, and Wear Resistance in Machining Processes

Abstract In the present investigation, self-lubricating cutting tools were fabricated by adding various elements such as NiCr, Ag, Mo, SrSO4, and CaF2 to Zirconia Toughened Alumina in order to overcome the difficulties that occurred in dry machining. Three significant process parameters; cutting speed (50, 125, 200, and 275 m/min), feed rate (0.1 and 0.2 mm/rev), and depth of cut (0.1 and 0.2 mm) were selected for the machining process. The performance measures such as chip thickness ratio, shear angle, and tool wear, were investigated. The experimental runs were conducted with Taguchi L8 mixed-level orthogonal array. Technique for Order of Preference by Similarity to Ideal Solution is a multi-criteria decision-making method used to determine the best alternative from a set of options. Turning experiments have been performed on the AISI 4340 steel workpiece with various combinations of machining parameters. The findings revealed the incorporation of SrSO4 and Mo at 10Wt.% and CaF2 at 5Wt.% (i.e., self-lubricating cutting tool-4) achieved better mechanical properties and wear resistance due to the formation of a self-lubrication layer on the rake face of the cutting tool. A confirmation experiment is executed in order to verify the outcomes in the end. Finally, there is a noticeable improvement in the outcomes compared to the base cutting tool, i.e., chip thickness ratio is 35.77%, shear angle is 27.31%, and tool wear is 58%. The mechanism for improving performance measures is discussed in detail.

Matrix‐phase material selection for shape memory polymer composites: A comparative analysis of multi‐criteria decision‐making

AbstractIn the present study, the selection of suitable shape memory polymers (SMPs) to be used as the matrix‐phase material with various (In)organic filler materials to achieve the required optimum multi‐stimuli response in shape memory polymer composites (SMPC) systems is analyzed. The selection of these materials is based on their mechanical and physical properties as well as other underlying factors such as cost, availability, shape recovery rate, and aesthetic characteristics. In this study, the entropy method was applied to estimate the weightages of the various criteria while the gray relation analysis (GRA) and the technique for order preference by similarity to ideal solution (TOPSIS). Multi‐criteria decision‐making (MCDM) approaches have been used to rank the suitable matrix‐phase polymer materials for manufacturing shape memory polymer composites (SMPC) system. A total of eight alternative SMP matrix‐phase materials based on a set of nine criteria were analyzed and ranked. The proposed methodology and the result obtained thereof have been illustrated in detail. The results obtained from TOPSIS and GRA methods have been compared to conclude the effects of the material properties on the ranking and the selection of the SMP materials. Among all the eight alternatives considered, thermoplastic polyurethane (TPU) was found to be the best material in both the MCDM methods. The material cost, resistivity, % elongation, and hydrophobicity present the most influencing properties on the SMP material selection, whereas density presents no effect on the SMP matrix material selection. The robustness of the results for the comparative analysis was verified using TOPSIS methodology to validate its reliability. It was revealed that the TPU, polycarbonate, polypropylene, and epoxy‐resin/poly(lactic acid) are the most dominant matrix‐phase SMP material alternatives when a deviation in the entropy weights of the primary evaluation criteria is applied. The novelty of this study is the exploration and application of statistical MCDM methods of engineering material selection problems based on a set of decision criteria, which can be time‐consuming and costly while using experimental analysis methods.Highlights SMPCs are applicable engineering materials thus making their research viable. SMPC systems can undergo various shape changes under external stimuli. Selection of matrix‐phase polymer is critical in achieving desired objectives. GRA and TOPSIS MCDM approaches have been applied in the selection process. TPU was found as the best material in both methods.

A BWM-TOPSIS Linear Programming Model for Evaluating the Performance of Health-Promoting Hospitals with McKinsey 7s Framework in Organizational Management

This research addresses the critical aspect of evaluating operational performance in health promotion hospitals, which play a vital role in providing medical services to local communities. The research proposes an integrated method for performance assessment, utilizing the Best-Worst Method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) linear programming model. Taking the case of health promotion hospitals in Maha Sarakham province, Thailand, and considering the McKinsey 7s framework's seven criteria, BWM is employed to determine the criteria weights. Subsequently, the TOPSIS linear programming model selects the ideal health promotion hospital based on these weights. The BWM analysis reveals criteria weights in the following order: system, staff, skill, style, structure, strategy, and shared value. The TOPSIS linear programming model identifies SH12 as the top-performing health promotion hospital with a closeness coefficient value of 0.8821. Additionally, a Spearman's rank correlation test validates this proposed method against the original TOPSIS approach, yielding a correlation value of 1.0. These findings provide valuable guidance for organizations, particularly in shaping strategic policies and resource allocation within medical service units, medical equipment, and personnel management in organizational settings. This study offers that the proposed method is simpler and will aid in the ongoing analysis of strengths and weaknesses in the improvement of organizations and development, helping organizations adapt to changes.

Genetic Algorithm Optimization with Selection Operator Decider

AbstractGenetic Algorithm (GA) is a powerful and flexible meta-heuristic tool to deal with the complexity of optimization problems, as they are directly related to real-life situations. The primary goal of an optimization problem could be to obtain a solution with less effort and near-optimal rather than slow, improbable optimal. GAs serve this purpose by broadly exploring the possible solution space and using genetic operators. The performance of GAs can vary significantly depending on the genetic operators. Although each operator type has upsides and downsides, the selection operator greatly influences the GA’s performance. Conventional GAs initialize with predetermined genetic operators and continue with the same throughout all iterations. In this paper, dynamically adjusting the selection operators to the current progress of the iteration will be shown to be a crucial strategy to improve the performance of the GA. This study aims to propose a novel GA capable of harnessing multiple selection operators by a self-deciding operator structure, which is more advantageous at the current iteration. For this, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which is known as a simple and effective multi-criteria decision-making method, will be integrated into the GA by a proposed dynamic decision matrix. The proposed Selection Operator Decider Genetic Algorithm (SODGA) has unique properties with varying selection processes and is capable of using TOPSIS as a decider of the operator inside the iterations. The effectiveness of the presented SODGA framework will be analyzed by a Capacitated Vehicle Routing Problems (CVRPs) benchmark set.

A Hybrid MCDM Approach Using the BWM and the TOPSIS for a Financial Performance-Based Evaluation of Saudi Stocks

This study presents a hybrid multicriteria decision-making approach for evaluating stocks in the Saudi Stock Market. The objective is to provide investors and stakeholders with a robust evaluation methodology to inform their investment decisions. With a market value of USD 2.89 trillion dollars in September 2022, the Saudi Stock Market is of significant importance for the country’s economy. However, navigating the complexities of stock market performance poses investment challenges. This study employs the best–worst method and the technique for order preference by similarity to identify an ideal solution to address these challenges. Utilizing data from the Saudi Stock Market (Tadawul), this study evaluates stock performance based on financial criteria, including return on equity, return on assets, net profit margin, and asset turnover. The findings reveal valuable insights, particularly in the banking sector, which exhibited the highest net profit margin ratios among sectors. The hybrid multicriteria decision-making-based approach enhances investment decisions. This research provides a foundation for future investigations, facilitating a deeper exploration and analysis of additional aspects of the Saudi Stock Market’s performance. The developed methodology and findings have implications for investors and stakeholders, aiding their investment decisions and maximizing returns.

Optimizing COVID-19 medical waste management using goal and robust possibilistic programming

During the global Coronavirus Disease (COVID-19) pandemic, the exponential rise in Hazardous Medical Waste (HMW) due to increased demand for personal protective equipment and heightened medical requirements posed significant threats to public health. This study proposes an innovative approach using a reverse logistics supply chain network that comprehensively integrates sustainability factors (e.g., cost, working conditions, exposure risks, and environmental impact) to manage the risks associated with medical waste effectively amid the pandemic. This research focuses on employing a guideline-based allocation of medical waste to specific technologies, leveraging the Torabi-Hassini (TH), Lp-metric (Lebesgue metric), and Goal Attainment (GA) approaches and robust possibilistic programming to address uncertainties. A real-case study validates the proposed model, demonstrating its ability to balance multiple objectives by optimizing the flow among treatment centers and introducing new Temporary Treatment Centers (TTCs). Also, we analyze broad sensitivity through weights assigned to the objective functions to obtain Pareto solutions. The convexity of the Pareto front confirms the conflict among the objective functions. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach specifies that the Lp-metric approach outperforms the others, and the TH approach is regarded as the second rank. The study's findings highlight the model's efficacy and provide crucial managerial insights for health organization administrators in efficiently managing the HMW supply chain network.

Assessing the Impact of Green Finance on Sustainable Tourism Development in China

The escalating urgency to address environmental degradation and promote sustainable development globally has emphasized the critical role of Green Finance (GF) in fostering responsible practices across industries. The tourism sector has drawn significant attention due to its substantial environmental impact, necessitating the implementation of robust financial mechanisms to mitigate its ecological footprint. China, recognized as a key player in the global tourism arena, the convergence of rapid economic expansion and the imperative for environmental conservation presents a distinctive set of challenges and opportunities. The study employed the Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies to systematically evaluate the impact of GF. The findings of fuzzy AHP indicate the critical role of environmental sustainability measures (GF2) as the topmost impacting factor. This refers to the practice of utilizing financial mechanisms and instruments to support initiatives that promote environmental conservation, minimize ecological footprints, and foster sustainable development. While financial allocation and effectiveness (GF1) and policy and regulatory framework impact (GF4) also hold significant importance in shaping sustainable tourism development. Moreover, the results of fuzzy TOPSIS identified top strategies such as green bond innovation for ecosystem regeneration (S1) and circular economy integration (S4), which can play a significant role in promoting sustainable tourism practices by facilitating initiatives aimed at ecosystem regeneration and integrating circular economy principles into the tourism industry.

Dynamic decision making for situational awareness using drones: Requirements, identification and comparison of decision support methods

Decision makers increasingly operate in real-time information-rich environments, where limited time is available for interpreting data to inform decisions. These environments are driven by static or mobile sensing devices that can provide numerous dynamic data points. A prominent approach in this space is to utilise drones, which can be deployed to gather targeted information. However, deciding how best to deploy available drones is nontrivial, and stands to benefit from decision support aids that plan routes. Such a system must operate under time constraints created by the changing attributes of routes as the situation unfolds. This study describes a dynamic decision support system (DSS) for situational awareness with drones. The system applies Multi-Criteria Decision Making (MCDM) methods within a dynamic genetic algorithm to provide a continuously revised ranking of routes. Five desiderata for dynamic decision support are presented. It is shown how a dynamic DSS can be equipped with declarative specification of preferences (Desiderata 1), dynamic revision of recommendations (Desiderata 2), and high diversity of options (Desiderata 3). The study then compares four MCDM methods, namely the Weighted Product Model (WPM), the Analytic Hierarchy Process (AHP), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), with regards to how consistently they trade-off between criteria (Desiderata 4) and the stability of results under small changes to criteria values (Desiderata 5). To evaluate the trade-offs between criteria we analyse the smoothness of change in criteria outcomes as criteria weightings increase for each algorithm. The outcomes are calculated by automating the selection of routes in a case study that applies drones to the task of harbour management. The stability of results for the different MCDM methods are compared. Perturbations were applied to sets of routes ranked by each algorithm then each algorithm was reapplied and the magnitude of the changes in ranking was assessed. Overall, TOPSIS was found to be the algorithm which made the most consistent trade-offs between criteria, only under-performing another algorithm with respect to a single criterion. AHP and WPM were the next most consistent algorithms and PROMETHEE was the least consistent algorithm. TOPSIS was also found to be the most stable method under small changes to criteria values. AHP was the second most stable, followed by PROMETHEE and WPM respectively. The results show that TOPSIS achieves the best result for both Desiderata 4 and 5 and consequently the study finds TOPSIS to be an appropriate MCDM method for dynamic decision support.

Fuzzy-AHP based optimal RSU deployment (Fuzzy-AHP-ORD) approach using road and traffic analysis in VANET

Vehicular Ad Hoc Network (VANET) is a proven technology in Intelligent Transportation Systems (ITS) that provides various safety and non-safety applications. The Roadside Unit (RSU) is one of the essential components of the VANET, which allows vehicles to disseminate and exchange safety and infotainment information in a broader range. RSUs are also valuable for offloading the internet data traffic of vehicular users from cellular networks. However, due to high deployment and maintenance costs and security overhead, it is essential to optimally deploy these RSUs and place them in prominent places to increase the overall network coverage and efficiency. The existing deployment strategies either analyzed the road network on one or a few topological parameters or vehicular traffic parameters and considered equal or random weights for each parameter. None of them deal with the uncertainty of parameter weightage. Thus, this paper proposes a Fuzzy-Analytic Hierarchy Process-based optimal RSU deployment (Fuzzy-AHP-ORD) approach to finding the highly influential intersection nodes for RSU deployment. The Fuzzy-AHP-ORD considers different static and dynamic parameters concerning road topology and the vehicle’s traffic behavior. The weights of the parameter are calculated through Fuzzy-AHP, then the various intersection nodes of the road networks are ranked using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based MADM (Multiple Attribute Decision Making) method. The simulation results reveal the effectiveness of the proposed method in comparison to other benchmark methods in terms of coverage ratio, connection time, packet delivery ratio, and delay.

A hybrid MCDM optimization for utilization of novel set of biosynthesised nanofluids on thermal performance for solar thermal collectors

Recent advancements in solar technology have spurred researchers to develop a precise and cost-effective method for monitoring solar radiation across diverse environmental conditions. This study focuses on optimizing performance parameters by replacing conventional nanofluids, produced through chemical and physical processes, with biosynthesized counterparts. Biosynthesized nanoparticles, derived from biological sources like microorganisms or plants, offer a promising avenue for enhancing solar panel efficiency. Through the construction and testing of an integrated photovoltaic thermal system, varying operating parameters have been explored to maximize photovoltaic efficiency. The highest overall efficiency, reaching approximately 62%, was achieved with biosynthesized Graphene oxide nanofluid under higher Direct Normal Irradiance (DNI) levels, while the lowest was observed with Aluminium oxide nanofluid at lower DNI values. Employing Multi-Criteria Decision methods such as Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), nanofluids were ranked based on criteria including Exergy loss, Surface temperature, Overall efficiency, and Electrical efficiency, with weights of 49.58%, 28.43%, 13.45%, and 8.54% respectively. TOPSIS prioritized Graphene Oxide as the most favourable nanofluid, followed by Copper-Oxide and Cerium-Oxide, while Aluminium-Oxide received the lowest priority in optimizing PV panel performance.

Assessment of consolidative multi-criteria decision making (C-MCDM) algorithms for optimal mapping of polymer materials in additive manufacturing: A case study of orthotic application

ObjectiveThe objectives of this research are twofold. The primary goal is to introduce, investigate, and contrast consolidative multi-criteria decision-making (C-MCDM) approaches. The second objective is the investigation of five alternative additive manufacturing materials. MethodsIt integrates the subjective and objective weights using the Bayes hypothesis in conjunction with a normal method. Chang's Extent Analysis Method under fuzzy logic is used to estimate subjective weights and the CRITIC approach is used for assessing objective weights. Ranking techniques, including the simple ranking process (SRP), multi-objective optimization based on ratio analysis (MOORA), measurement alternatives and ranking according to compromise solution (MARCOS), and technique for order preference by similarity to ideal solution (TOPSIS) are applied. It also encompasses sensitivity analysis based on Kendall's coefficient of concordance and rank reversal phenomenon analysis. Spearman's rank correlation coefficient, a weighted rank measure of correlation, and rank similarity coefficient are among the metrics used to evaluate agreement between different approaches. It entails gathering expert opinions regarding the importance of various criteria as well as conducting extensive experiments. ResultsThe findings of the study indicate that polylactic acid is the best material to use for orthoses. When compared to the other MCDM approaches being discussed, SRP is the most reliable approach. It is also demonstrated that the SRP, MARCOS, and TOPSIS methods are rank reversal-free. Furthermore, SRP exhibits a very poor association with the TOPSIS technique but a strong correlation with the MOORA and MARCOS approaches. ConclusionsTo ensure results reliability, it is necessary to consider both the subjectivity and objectivity of weights as well as apply multiple MCDM methodologies in addition to sensitivity analysis.

A Combined Decision-Making Analysis Under Single-Valued Neutrosophic Set for Selecting Favorable Knowledge Management Strategy

The Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are pivotal methods in multi-criteria decision-making, addressing causal relationships among criteria and ranking alternatives, respectively. This paper introduces a novel approach, integrating DEMATEL and TOPSIS under the framework of single-valued neutrosophic set (SVNS) to handle indeterminacy in decision-making. Applied to a knowledge management strategy case, the method utilizes eight criteria and four alternatives derived from the literature. The process involves DEMATEL for expert weight determination and direct-relation matrix creation through linguistic variable influence. Subsequently, TOPSIS is employed to rank alternatives based on distance measures. The combined method identifies the ‘Human Resource Department’ as the most crucial in knowledge management strategy. This integrated approach facilitates organizations in pinpointing vital criteria and alternatives for effective knowledge management. Comparative analyses with existing methods are also presented.

Disparities of Central and Eastern European Countries of European Union in innovation potential: A multi-criteria assessment

The primary objective of the article is to evaluate the innovation potential of Central and Eastern European Union countries. The Technique for Order of Preference by Similarity to Ideal Solution method was employed to assess and rank this potential. The research was based on 10 indicators that characterize the main areas related to innovation, namely intellectual capital (as a measure of social innovative capacity), financial capital (as a measure of the potential for financing research and development) as well as research and innovation activities. Three objective approaches were used to calculate weights of these evaluation criteria (in the TOPSIS method): Shanon–Entropy, criteria importance through intercriteria correlation and equal weights. As a evaluate of the level of innovation potential of studied CEE countries between 2013 and 2022, the determined values of the Innovation Potential Index were used to evaluate the studied countries and divide them into four groups. With the use of the two non-parametric test, the relationships of this index with selected parameters were also measured. The research results show that the innovation potential level of CEE countries is highly differentiated. Slovenia, the Czech Republic and Estonia, countries investing in R&D and social development, were found to have a high level of innovation. The remaining countries need to take more decisive action in the areas studied to improve their level of innovation. The methodology developed for the research, the conducted study, and the resulting findings offer opportunities to analyze the indicators and areas studied, and through this to implement strategies for building an innovative knowledge-based economy both by the countries in question and the EU as a whole.

A novel zende's-TOPSIS method towards estimation of measurement uncertainty in hole diameters

Abstract The 'Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)' is one of the best methods for 'Multi-Criteria Decision-Making' and 'Multi-Objective Optimization'. The traditional TOPSIS method determines the best alternative under fixed conditions. However, it cannot determine the best upper limit and lowest limit values. This work explains the detailed methodology of the newly developed Zende's-TOPSIS method which was used to estimate the measurement uncertainty in hole diameters. Four identical holes and one center hole in an industrial component were measured to investigate measurement uncertainty. According to the experimental results, Zende's-TOPSIS method performed better than the traditional TOPSIS method. The percentage improvement in the Zende's-TOPSIS method over the traditional TOPSIS method ranges from 0.0209% to 0.3053%. Using Zende's-TOPSIS method, the percentage maximum measurement uncertainty for four identical holes varies from 0.8067% to 1.0222%, whereas for the center hole, it varies from 0.5261% to 0.5576%. Similarly, the percentage minimum measurement uncertainty for four identical holes varies from 0.3839% to 0.6406%, whereas for the center hole, it varies from 0.4014% to 0.4041%. The proposed method is also capable of estimating the machined tolerances of the component, which ranges from 18.0772 mm to 18.1708 mm for four identical holes and 49.2215 mm to 49.2572 mm for the center hole. The proposed method can solve various 'Multi-Objective Optimization' problems.

Development of RAMDOE: a new method for rapidly ranking alternatives with supplementary options and considering changes in criteria values

This paper delves into the development and validation of the RAMDOE method, a pioneering approach in multi-criteria decision making (MCDM) that seamlessly integrates the root assessment method (RAM) and design of experiments (DOE) techniques, addressing the inflexibility of traditional MCDM methods in accommodating adjustments in criteria ranges and the addition of new alternatives without necessitating a complete overhaul of the decision framework. Through empirical analysis, the study demonstrates the RAMDOE method's remarkable efficacy in precisely ranking alternatives, as illustrated through a practical case study focused on the selection of a supplier from a pool of seven candidates. One of the most notable aspects of the RAMDOE method lies in its capacity to formulate a regression equation that accurately captures the intricate relationship between alternative scores and criteria values, enabling decision-makers to seamlessly integrate new alternatives into the decision-making process without the cumbersome task of recalibration, thereby distinguishing it from conventional MCDM techniques such as TOPSIS (technique for order of preference by similarity to ideal solution), COPRAS (complex proportional assessment), MOORA (multiobjective optimization on the basis of ratio analysis), EDAS (evaluation based on distance from average solution) and CODAS (combinative distance-based assessment). The practical implications of these findings are profound, offering decision-makers across various domains a more efficient and adaptable framework to navigate complex decision scenarios. Particularly in contexts like supplier selection, where criteria ranges may vary significantly, the RAMDOE method provides decision-makers with a robust toolset to make informed decisions, presenting a promising avenue for addressing the dynamic nature of decision-making environments and enhancing the overall robustness and flexibility of MCDM processes in real-world applications