Vise Kriterijumska Optimizacija I Kompromisno Resenje Research Articles

Performance comparison of several explainable hybrid ensemble models for predicting carbonation depth in fly ash concrete

The carbonation of fly ash concrete critically impacts the lifespan of structures, necessitating precise prediction of carbonation depth for the construction industry. This study establishes an original database comprising 883 cases, which are divided into training and testing sets in a 4:1 ratio. The Sand Cat Swarm Algorithm (SCSO) was developed to optimize the hyperparameters of three ensemble models: Gradient Boosting Decision Trees (GBDT), Light Gradient Boosting Machine (LGBM), and Categorical Boosting (CatBoost), resulting in the development of three hybrid ensemble models: SCSO-GBDT, SCSO-LGBM, and SCSO-CatBoost. Five classic models were included in the comparison, and all models used five-fold cross validation. Models’ performance was rigorously evaluated using Correlation coefficient (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Variance Accounted For (VAF), with VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method and Taylor diagrams utilized for optimal model selection. The SCSO-CatBoost model demonstrated superior performance, with R2 = 0.9657, MAE = 2.0062, RMSE = 8.2147, and VAF = 96.6177. Shapley additive explanations (SHAP) analysis of the SCSO-CatBoost model identified time of exposure as the most significant factor influencing carbonation depth, followed by fly ash content and carbon dioxide concentration. To facilitate practical application by non-algorithm engineers, an intelligent program was developed, allowing for straightforward testing with the three hybrid ensemble models. This study presents three precise models for predicting the carbonation depth of fly ash concrete. These models serve as valuable tools for estimating the service life of concrete structures and can be utilized as simulation instruments in durability engineering.

A systematic framework for the design and material selection of composite for tennis racket upon impact

The present study explores the comprehensive composite material selection methodology of a tennis racket involving coupled Multi-Criteria Decision-Making (MCDM) and numerical analysis. Initially all the possible material alternatives from the composite family were shortlisted using CES Selector 2016. A design space was developed incorporating MCDM technique naming VICKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), PROMETHEE (Preference Ranking Organisation Method for Enrichment Evaluation) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to rank the most suitable composite material based on critical mechanical properties to identify the most suitable composite materials for applications requiring optimal combinations of rigidity, strength, and structural performance. Using finite element analysis, the structural integrity of each of the materials was evaluated on static and dynamic stress analysis modules of ANSYS. Out of all material alternatives, T300 carbon fiber epoxy was considered as the most suitable for tennis rackets. Further, a comparison between the selected material was also made with conventional materials and were found in good agreement with selected material.

Selection of Marketing Staff Using Simple Additive Weight and VIKOR Algorithm

In today’s rapidly evolving technological landscape, decision-making processes within organizations are increasingly relying on advanced computational methods to enhance efficiency and accuracy. This is particularly relevant in human resource management, where selecting suitable candidates for key positions is critical. Traditional methods of staff recruitment often rely on subjective assessments, which may lead to biases and inconsistencies. To address these challenges, this study proposes the use of the Simple Additive Weighting (SAW) and VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) algorithms as multi-criteria decision-making tools for selecting marketing staff. The SAW method offers a straightforward approach by assigning weighted scores to various criteria. In contrast, the VIKOR method provides a ranking system that considers ideal and compromise solutions for candidate selection. Integrating these two algorithms makes the selection process more objective and data-driven, reducing the risk of human error and improving overall decision quality. This paper outlines implementing the combined SAW-VIKOR model in the marketing staff recruitment process, highlighting its potential to optimize candidate evaluation and selection. The results demonstrate that utilizing these algorithms enhances the decision-making process, leading to better alignment of selected staff with organizational goals. This approach is valuable for organizations looking to leverage technology in their recruitment strategies.

The combination of Multi-Criteria Decision-Making (MCDM) and morphometric parameters for prioritizing the erodibility of sub-watersheds in the Ouljet Es Soltane basin (North of Morocco)

Preserving water and soil resources ranks among the top priorities outlined in the national water strategy. Indeed, the integrated management of water resources in vulnerable territories, particularly in Morocco, requires a deep knowledge of the hydrological functioning and use of water resources in these regions. The diverse hydroclimatic and morphological features within the Ouljet Es Soltane watershed, which is a sub-basin of the extensive Oued Sebou watershed, present significant challenges in managing its water and soil resources. Identifying areas susceptible to soil erosion is crucial for implementing preventive measures in the Ouljet Es Soltane basin and ensuring its sustainable development. Morphometric analysis plays an important role in the effective management and sustainable utilization of the basin's resources. This study used four MCDM models, including the CF (Compound Factor), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and SAW (Simple Additive Weighing), to prioritize 20 sub-watersheds of the Ouljet Es Soltane watershed.Based on the sub-watershed prioritization results obtained from the VIKOR, TOPSIS, and SAW models, sub-watershed 16 achieved scores of 0, 0.59, and 0.8, respectively, positioning it as the first rank. These findings highlight that sub-watershed 16 exhibits a high susceptibility to erosion and is classified as one of the most vulnerable areas in terms of erosion risk.Based on the results obtained from the VIKOR, TOPSIS, and SAW models, the susceptibility of the sub-watersheds to erosion can be classified into four categories: low, moderate, high, and very high. On the other hand, the CF model only has two categories: low and moderate susceptibility. Overall, the findings suggest that morphometric parameters are highly effective in identifying areas at risk of erosion. Furthermore, the VIKOR, TOPSIS, and SAW methods exhibit greater predictive accuracy compared to the CF model. The comparison of these models involved the use of Spearman correlation coefficient test (SCCT).The findings of this study can provide valuable insights for making informed decisions in developing an effective framework for soil erosion control strategies.

Combining improved DFMEA with knowledge graph for component risk analysis of complex products

With the development of information technology in manufacturing, product lifecycle iteration is accelerating. Efficiently analyzing and identifying component risks is important to ensure the reliability of products. This paper proposes a new method that combines improved design failure mode and effect analysis (DFMEA) with the knowledge graph for component risk analysis of complex products. A risk evaluation knowledge graph is built for the risk identification and knowledge provision in the process of DFMEA. Graph analysis is used to identify to-be-evaluated objectives based on historical maintenance data, which helps designers avoid the heavy task of identification. The graph matrix of the historical evaluations is used to calculate the comprehensive weights. A Dempster-Shafer (DS) evidence fusion method based on comprehensive weight evaluation information is proposed for the effective fusion of evaluation evidence. As an alternative to the exact risk ranking, we offer a risk trade-off VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method for components classification that classifies components into different risk classes for different risk treatment measures. Finally, the proposed method is validated using a tunnel boring machine as an example, and comparison and sensitivity analyses are performed to demonstrate that the risk value shows a consistent and stable change with expert evaluation.

A Grid-Wide Comprehensive Evaluation Method of Power Quality Based on Complex Network Theory

To achieve a hierarchical and quantitative evaluation of grid-wide power quality in the distribution network, reflecting the overall power quality level of the distribution network, a comprehensive evaluation method for power quality in a grid-wide system based on complex network theory is proposed. Firstly, based on the propagation characteristics of power quality disturbances, a power quality evaluation index system is constructed. Secondly, to reflect the constraint effect of the local power quality level of nodes on the overall power quality level of the distribution system, corresponding indices such as improved node degree, improved node electrical betweenness, and node self-healing capability are proposed based on complex network theory, and the power quality influence degree of nodes is calculated. Then, the GRA-ANP (Grey Relational Analysis–Analytic Network Process) subjective weight calculation method is improved by introducing grey relational analysis to address the impact of differences in different decision-making results. Based on power quality monitoring data, the entropy weight method is used for objective weighting. To avoid the partiality of a single weight evaluation result, the game equilibrium algorithm is employed to calculate the comprehensive weight of each power quality index. Subsequently, considering the correlation and dependency among indices, the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method is used to obtain the power quality grade of each node. Combining this with the calculation of the power quality influence degree of nodes, the overall power quality grade of the distribution network is determined, achieving a hierarchical and quantitative evaluation of power quality in the entire distribution system. Finally, through a case study analysis of an improved 13-node distribution network, it is verified that the proposed method can fully extract data information and produce comprehensive and accurate power quality assessment results by comparing it with other methods. This provides strong support for the safe and stable operation of the distribution system and the subsequent optimization and management of power quality.

Decision Support System for Ranking Active Waste Bank in Makassar City Using TOPSIS and VIKOR Methods

In the city of Makassar, there were initially around 1000 waste banks, but this number has decreased significantly, and by 2023 only 381 waste banks remain active. The decline in the number of waste banks is primarily due to the society's lack of knowledge regarding the utilization of waste banks. This research aims to rank active waste banks in Makassar using the MCDM (Multi-Criteria Decision Making) technique. Two MCDM methods will be utilized in this study: the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method and the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method. Both methods share a common goal of finding the closest value to the ideal solution, but they differ in their normalization and aggregation functions. TOPSIS calculates the criteria weight values first, followed by the criteria values, whereas VIKOR starts with the highest criteria values and then calculates the criteria weights. The results of this research indicate that some alternatives received the same ranking using TOPSIS and VIKOR methods. The criteria used to calculate data for Waste Banks are Operational Hours, Operational Schedule, Total Customers, Total Employees, and Amount of Collected Waste. These criteria are determined based on Regulation Minister of Environment and Forestry Republic of Indonesia Number 14 of 2021 concerning Waste Management at Waste Banks.

Multi-criteria decision-making techniques for groundwater potentiality mapping in arid regions: A case study of Wadi Yiba, Kingdom of Saudi Arabia

Freshwater demand is further stressed due to the impacts of climate change and the rapid expansion of the population, particularly in dry and extremely dry regions where groundwater serves as a crucial water source. This study employed various multi-criteria decision making (MCDM) techniques to identify areas with potential for groundwater in Wadi Yiba, Kingdom of Saudi Arabia (KSA). A total of 14 parameters, including Geology, land use and land cover (LULC), Drainage Density (DD), Lineament density (LD), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), rainfall, and others, were selected to create thematic layers for the delineation of groundwater potential zones (GWPZ). Using three MCDM techniques, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacijaik Ompromisno Resenje (VIKOR), and Evaluation Based on Distance from Average Solution (EDAS) methods, maps were generated to indicate the GWPZ in the study area. These maps were categorized into five categories: very low, low, medium, high, and very high. Results indicate that the GWPZ assessed by TOPSIS in Wadi Yiba categorizes 3% of the area as having very low groundwater potential, 9% as low, 29% as medium, 36% as high, and 23% as very high. In contrast, VIKOR classified the study region as 7%, 20%, 18%, 28%, and 26% for the five classes; while the EDAS study reveals a more balanced distribution, with 5.81%, 24.80%, 33.74%, 22.32%, and 13.33% for the five classes. We evaluated the accuracy of the final maps using the receiver operating characteristic-area under the curve (ROC-AUC) evaluation method. TOPSIS exhibited the highest accuracy (0.703), closely followed by VIKOR (0.701) and EDAS (0.557). TOPSIS and VIKOR were rated "excellent," while EDAS as "good". The findings are crucial for sustainable groundwater management in Wadi Yiba and underscore the importance of selecting appropriate methods in environmental modeling.

Effective multi-attribute group decision-making approach to study astronomy in the probabilistic linguistic q-rung orthopair fuzzy VIKOR framework

This study employs a novel fuzzy logic-based framework to address multi-attribute group decision-making problems commonly encountered in modern astronomy. Our approach utilizes the probabilistic linguistic q-rung orthopair fuzzy set (PLq-ROFS) to handle the inherent uncertainties associated with astronomical data. The PLq-ROFS offers significant advantages over existing fuzzy sets like probabilistic hesitant, linguistic intuitionistic, and linguistic Pythagorean fuzzy sets, which comprise both stochastic and non-stochastic uncertainties simultaneously. To aggregate the probabilistic linguistic decision information effectively, we propose two novel operators: the PLq-ROF weighted power average (PLq-ROFWPA) and the PLq-ROF weighted power geometric (PLq-ROFWPG). These operators form the foundation of a novel method within the PLq-ROF environment. Furthermore, this study integrates the PLq-ROF framework with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model, a widely used decision-making (DM) tool known for its ability to balance group utility maximization with individual regret minimization. This integration leads to the PLq-ROF-VIKOR model, a novel approach for ranking alternative solutions based on the subjective preferences of decision-makers. The effectiveness of the proposed method is demonstrated through a real-world case study in astronomy, accompanied by both parameter and comparative analyses. These analyses highlight the efficiency and accuracy of the PLq-ROF-VIKOR model, ultimately leading to the conclusion that cosmology is the most optimal key finding in this case study.

Construction and application of economic resilience evaluation model for megacities.

Economic resilience provides a new perspective for megacities to achieve sustainable development when facing multiple shocks, and its accurate evaluation is an essential prerequisite for optimizing urban governance. There are currently no generally accepted methods for empirical evaluation or measuring economic resilience, and the present study aims to contribute to in both the research field and methodology. The present study sets dimensions and indicators based on economic resilience's theoretical and empirical research and used Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interactive Structural Modeling (ISM) methods to exclude the effect indicators and divide the indicator hierarchy, respectively. Subsequently, the present study conducts model validation using Chinese megacities as a case study. The game theory weighting method, which combines the Analytic Hierarchy Process (AHP) and Entropy methods, is used to calculate indicator weights, and the VIKOR (VIseKriterijumska Optimizacija i KOmpromisno Resenje) method is used to evaluate and compare economic resilience of megacities. The research findings indicate that the evaluation model constructed in the present study included 15 indicators (after excluding three effect indicators) divided into four levels. After merging the levels, they correspond to three dimensions: resistance, recoverability, and adaptability. In addition, using Chinese megacities as a case study, the evaluation results found that Beijing, Shanghai, and Shenzhen have high economic resilience, Tianjin and Guangzhou have moderate economic resilience, Chengdu has low economic resilience, and Chongqing has the lowest economic resilience. This result is consistent with previous studies and verifies the model's effectiveness. The present study also found that megacities with lower levels of economic resilience exhibit a more significant upward trend, as well as the highest and higher proportion of economic resilience in Chinese megacities depending on time passes, indicating that megacities' economic resilience is weakening. The evaluation result obtained in the present study is more specific, precise, and focused on depicting the distribution differences and development trends of economic resilience at the urban level.

IVPF-AHP integrated VIKOR methodology in supplier selection of three-dimensional (3D) printers

Complex geometries, fine details, and various designs that are difficult to create using traditional methods can easily be turned into a tangible object with Three-Dimensional (3D) printers. 3D printers have advantages such as providing design flexibility, obtaining prototypes in the shortest possible time, allowing for personalization, and reducing waste through the use of advanced technology. These advantages emphasize the significance of 3D printers in a sustainable production model. The widespread usage of 3D printers leads to increased efficiency and cost reduction in production. When the literature is examined, it is observed that there are limited studies on the evaluation of supplier performances for company using 3D printers. The aim of this study is to address 3D printers, which are highly significant for sustainable production, and to reveal the criteria that companies utilizing these printers need to consider for determining their suppliers. As a result of the literature review and expert interviews, a model has been developed that gathers the criteria to be considered for supplier selection, which is an important cost factor for companies involved in designing and producing 3D printers under five main and 18 sub-criteria. The importance weights of the criteria have been determined using the Interval Valued Pythagorean Fuzzy Analytic Hierarchy Process (IVPF-AHP) method, and the most suitable supplier among alternative suppliers has been selected using the Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Finally, the supplier scores have been statistically analyzed to show the validation of the results of the proposed method. According to the results, it has been concluded that for company using 3D printers, quality and technical service criteria are more important in the supplier selection. Additionally, cost of the material/equipment, product price and easy maintenance criteria also play a critical role in the supplier selection of 3D printer.

Evaluating the supply chain resilience strategies using discrete event simulation and hybrid multi-criteria decision-making (case study: natural stone industry)

ABSTRACT One of the challenges that organisations face is the risk reduction through creating resilient supply chains. There are different strategies to reach the resiliency. In this paper, we analysed five important strategies: redundancy, backup supplier, reserved capacity, reserved inventory, and increasing work-shifts. These supply chain resilience (SCR) strategies are evaluated using discrete event simulation and hybrid multi-criteria decision-making methods. For this end, Bayesian best-worst method (BWM) and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were used for weighting the SCR evaluation criteria and ranking the SCR strategies, respectively. Six discrete event simulation models were generated and run for evaluating each strategy in order to achieve quantitative criteria. On the other hand, the ideas of experts were used for weighting qualitative criteria and also ranking the alternatives. Results showed that the increasing work-shift strategy has the first rank and the reserved capacity strategy has the lowest rank.

An integrated framework for assessing renewable-energy supply chains using multicriteria decision-making: a study on Bangladesh

Abstract The depletion in non-renewable energy sources and a fast-growing population in Bangladesh are exacerbating the already existing energy scarcity, highlighting the need for an efficient and robust renewable-energy supply chain. The primary goal of this study is to evaluate the most optimized renewable-energy supply chain based on natural resource availability and government policies of Bangladesh. In the present study, four renewable energy resources, including solar, biomass, wind and hydropower, are studied and nine subcriteria are defined under four primary criteria for each supply chain. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) are multicriteria decision-making approaches used in this study to compare and choose the best renewable-energy supply chain. The relative significance of four supply-chain criteria for primary renewable energy in this study, namely energy procurement, production, operations and maintenance costs, and social and environmental impact, is gathered via a survey. The results of this research, supported by a comprehensive sensitivity analysis, indicate that hydropower is the best renewable-energy supply chain, followed by wind as a compromise solution, biomass and solar. The study also demonstrates that no energy source can satisfy all supply-chain criteria alone; each resource is better for a specific criterion—solar is better for procurement, hydropower is significant for production and wind is remarkable for operations and social impact. Therefore, to maximize output, renewable energy sources must be integrated. From Bangladesh’s perspective, for the first time, by using TOPSIS and VIKOR together, this study offers significant insights to establish an efficient and sustainable renewable-energy supply chain for practitioners, academics and policymakers.

An integrated fuzzy multi-criteria decision-making model for determining the interdependencies among the african swine fever spread factors

African swine fever (ASF) is a disease outbreak that has substantially impacted agriculture, public health, and food security. This study tackles the lack of knowledge regarding the interactions between multiple factors causing the spread of ASF. We also present a potential for cross-fertilization between applying soft computing to nonmeasurable factors of different natures through investigating the relationships and significance of ASF spread factors in the Philippine setting using Multi-criteria Decision Making (MCDM) methodologies and fuzzy logic. We develop a list of ASF spread factors through a literature review. Fuzzy Analytic Hierarchy Process (AHP)-VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is used for the dimensionality reduction of factors. These elements are then classified as either net causes or net effects using the Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL), which also shows their interdependencies. The results show how socioeconomic characteristics, particularly those connected to biosecurity lapses, significantly influence other parameters through a causal graph. This knowledge can be used to develop effective ASF preventive and management methods as it provides a systematic and unbiased decision-making framework for policymakers, breeders, and manufacturers.

Evaluation of new scintillation crystals with MCDM methods for brain PET

Brain Positron Emission Tomography (PET) imaging has been increasingly important in recent decades for improved detection and staging of brain cancer and other brain illnesses. Many elements, including the choice of radiation detecting medium, contribute to the overall image quality used to characterize a PET system's performance. In our previous study, novel transparent optical scintillator crystals for brain PET system were simulated. Therefore, there is a need to evaluate these materials using advanced computational techniques. Hence, in this study, we evaluated and compared them using two Multi-Criteria Decision-Making (MCDM) techniques, namely Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Visekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The crystals used in this study were strontium hafnate (SHO), gadolinium aluminum gallium garget (GAGG), gadolinium yttrium gallium aluminum garget (GYGAG), gadolinium lutetium gallium aluminum garget (GLuGAG), and lastly lutetium oxyorthosilicate (LSO) for comparison. Density, effective atomic number, energy resolution, light output, and decay time were selected as important criteria. Importance weights of each criteria were then assigned by considering the high resolution and high sensitivity detectors. With both MCDM methods, the results showed that SHO outranked the other scintillator materials followed by LSO and GLuGAG. GYGAG, and GAGG is revealed as the least favorable crystals, in agreement with the previous simulation studies. This study can be extended by including more scintillators as they become available in the future.

A MAGDM approach for evaluating the impact of artificial intelligence on education using 2-tuple linguistic q-rung orthopair fuzzy sets and Schweizer-Sklar weighted power average operator.

The impact of artificial intelligence (AI) in education can be viewed as a multi-attribute group decision-making (MAGDM) problem, in which several stakeholders evaluate the advantages and disadvantages of AI applications in educational settings according to distinct preferences and criteria. A MAGDM framework can assist in providing transparent and logical recommendations for implementing AI in education by methodically analyzing the trade-offs and conflicts among many components, including ethical, social, pedagogical, and technical concerns. A novel development in fuzzy set theory is the 2-tuple linguistic q-rung orthopair fuzzy set (2TLq-ROFS), which is not only a generalized form but also can integrate decision-makers quantitative evaluation ideas and qualitative evaluation information. The 2TLq-ROF Schweizer-Sklar weighted power average operator (2TLq-ROFSSWPA) and the 2TLq-ROF Schweizer-Sklar weighted power geometric (2TLq-ROFSSWPG) operator are two of the aggregation operators we create in this article. We also investigate some of the unique instances and features of the proposed operators. Next, a new Entropy model is built based on 2TLq-ROFS, which may exploit the preferences of the decision-makers to obtain the ideal objective weights for attributes. Next, we extend the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique to the 2TLq-ROF version, which provides decision-makers with a greater space to represent their decisions, while also accounting for the uncertainty inherent in human cognition. Finally, a case study of how artificial intelligence has impacted education is given to show the applicability and value of the established methodology. A comparative study is carried out to examine the benefits and improvements of the developed approach.

Effect of RAP fractionation and dosage on design and mechanical behaviour of cold asphalt mixes

This study examines the effect of reclaimed asphalt pavement (RAP) material dosage and incorporation method on the mechanical characteristics of Cold Asphalt Mix (CAM). The RAP materials were fractionated into fine and coarse in addition to the unfractionated method. Four levels of RAP dosage (25, 50, 75, and 100%) were considered for each category. The mix design parameters, such as total fluid and emulsion content, were evaluated using the Indirect Tensile Strength (ITS) test. Overall, 455 specimens were prepared for mix design. The Optimum Emulsion Content (OEC) is the design emulsion content determined at peak value of ITS test. ITS, Indirect Tensile Stiffness Modulus (ITSM), and moisture susceptibility tests were conducted. Incorporating RAP in CAM increased the average tensile strength predominantly by 34% in coarse mixes, 28% in unfractionated, and < 10% in fine mixes. The average ITSM of the mixture increased by 30%, 60%, and 80% for fine, coarse, and unfractionated RAP. Further, emulsion demand was reduced by 53%, 43%, and 79% for fine, coarse, and unfractionated RAP, which showed that the RAP incorporation method profoundly influenced OEC. However, regardless of fractionation, CAM mixes showed lower cracking resistance and higher moisture susceptibility with increased RAP dosage. Two different conditioning protocols were used to capture the variation in moisture susceptibility for all 13 mix combinations. Coarse and unfractionated RAP mixes significantly differed in ITS, moisture susceptibility, cracking tolerance index, and ITSM. VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach was selected to rank the outperforming combination. The overall ranking of RAP incorporation showed that the coarse RAP mixes outperformed fine and unfractionated RAP mix characteristics. The study recommends restricting RAP usage by up to 75% to ensure the required performance of CAM mixtures.

Recommendation of Machine Learning Techniques for Software Effort Estimation using Multi-Criteria Decision Making

&amp;nbsp;For the development of the software industry, Software Effort Estimation (SEE) is one of the essential tasks. Project managers can overcome budget and time overrun issues by accurately estimating a software project&amp;#39;s development effort in the software life cycle. In prior studies, a variety of machine learning methods for SEE modeling were applied. The outcomes for various performance or accuracy measures are inconclusive. Therefore, a mechanism for assessing machine learning approaches for SEE modeling in the context of several contradictory accuracy measures is desperately needed. This study addresses selecting the most appropriate machine learning technique for SEE modeling as a Multi-Criteria Decision Making (MCDM) problem. The machine learning techniques are selected through a novel approach based on MCDM. In the proposed approach, three MCDM methods- Weighted Aggregated Sum Product Assessment (WASPAS), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were applied to determine the ranking of machine learning techniques on SEE performance based on multiple conflicting accuracy measures. For validating the proposed method, an experimental study was conducted over three SEE datasets using ten machine-learning techniques and six performance measures. Based on MCDM rankings, Random Forest, Support Vector Regression, and Kstar are recommended as the most appropriate machine learning techniques for SEE modeling. The results show how effectively the suggested MCDM-based approach can be used to recommend the appropriate machine learning technique for SEE modeling while considering various competing accuracy or performance measures altogether.

A Hybrid Group Multi-Criteria Approach Based on SAW, TOPSIS, VIKOR, and COPRAS Methods for Complex IoT Selection Problems

The growth of Internet of Things (IoT) systems is driven by their potential to improve efficiency, enhance decision-making, and create new business opportunities across various domains. In this paper, the main selection problems in IoT-type systems, criteria used in multi-criteria evaluation, and multi-criteria methods used for solving IoT selection problems are identified. Then, a Hybrid Group Multi-Criteria Approach for solving selection problems in IoT-type systems is proposed. The approach contains the Best Worst Method (BWM) weighting method, multi-criteria Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Complex Proportional Assessment Method (COPRAS), and a method that combines the solutions obtained using the four considered multi-criteria methods to obtain a single solution. The SAW, TOPSIS, VIKOR, and COPRAS methods were analyzed in relation to their advantages, disadvantages, inputs, outputs, measurement scale, type of normalization, aggregation method, parameters, complexity of implementation, and interactivity. An application of the Hybrid Group Multi-Criteria Approach for IoT platform selection and a comparison between the SAW, TOPSIS, VIKOR, and COPRAS solutions and the solution of the proposed approach is realized. A Spearman correlation analysis is presented.

Design modification and selection of improved stem design of the conical stem tibial implant for TAR using FE analysis and different MCDM methods.

The conical stem tibial design of total ankle replacement (TAR) has high implant-bone micromotion. This may lead to aseptic loosening which can be avoided by improving the tibial design. The objective was to propose the best stem design parameters to reduce implant-bone micromotion along with minimizing stress shielding using an integrated Finite Element-Multi Criteria Decision Making (FE-MCDM) approach. FE models of implanted tibia bones were prepared by changing the height of the stem, the diameter of the stem, and the slant of the stem. Weighted Aggregated Sum Product Assessment (WASPAS), Technique for Order of Preference by Similarities to Ideal Solution (TOPSIS), Evaluation based on Distance from Average Solution (EDAS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) MCDM techniques with equal weights for micromotion and stress shielding were considered. The micromotion and stress shielding were greater when the height of the stem was increased. Whereas, the increase in diameter and slant affected them marginally. The best-performing design was the Model with stem height 6 mm (diameter 6.4 mm and slant 4°) and after that was the Model with stem height 8 mm (diameter 6.4 mm and slant 4°), and then the Model with stem height 10 mm (diameter 6.4 mm and slant 4°). The height of the stem is the most important stem design parameter. Shorter height, moderate thickness, and moderate slanting stem designs are recommended.