Technique For Order Of Preference By Similarity To Ideal Solution Research Articles

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.

An analysis of social acceptance of energy in Iran: case study of fossil, nuclear, solar and wind energy

Purpose The purpose of this paper is to investigate the social acceptance of renewable and non-renewable energies in Iran using the social acceptance pyramid. Design/methodology/approach Today, social acceptance is considered a very important phenomenon in the development, implementation and achievement of energy policy goals. Low acceptance will make it difficult to achieve energy development goals; therefore, social acceptance must be taken into account when making policy. Firstly, the model criteria, using data obtained from questionnaires, are weighted by the Shannon entropy method and, finally, four sources of fossil, nuclear, wind and solar energy were ranked by means of VIKOR, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Findings The results show that, in Iran, the social acceptance criterion and trust sub-criterion are the most important criteria for energy acceptance. The results of the ranking of options based on multiple-criteria decision-making (MCDM) techniques show that, given Iran's specific energy requirements, social acceptance of fossil energy is higher than wind, solar and nuclear, and wind, solar and nuclear energy come later in the rankings. Originality/value This research contributes to the literature in two ways: Firstly, social acceptance is considered a very important phenomenon in the development, implementation and achievement of energy policy goals; thus social acceptance must be taken into account when making policy. The results of the ranking of options based on MCDM techniques show that, given Iran's specific energy requirements, social acceptance of fossil energy is higher than wind, solar and nuclear, and wind, solar and nuclear energy come later in the rankings. Also, the social acceptance criterion and trust sub-criterion are the most important criteria for energy acceptance in Iran.

Balancing Flood Control and Economic Development in Flood Detention Areas of the Yangtze River Basin

Serving as a crucial part of the Yangtze River Basin (YRB)’s flood control system, Flood Detention Areas (FDAs) are vital in mitigating large-scale floods. Urbanization has led to the development of urban FDAs, but significant losses could ensue if these FDAs are activated. With improved reservoirs and embankments, flood pressure in the middle reaches has lessened, posing challenges in balancing flood control and economic benefits. This paper presents a comparative analysis of land use, GDP, and population in FDAs and adjacent cities, enhancing our understanding of their disparities and interrelations. Using the Analytic Hierarchy Process (AHP)–Entropy Weight Method (EW)–Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) comprehensive evaluation method, we assess changes in flood control and economic values in FDAs. The results show a conflict between flood control and economic policies in FDAs, highlighting their underestimated economic potential, especially in urban areas. This study identifies differences in economic development across FDAs and a strong correlation between flood control value and inundation rates. Based on evaluations and simulations of the 1954 flood, we provide recommendations for the FDAs’ construction plan, which serves the development and flood management of the YRB and offer insights for similar assessments elsewhere.

A Hybrid Approach Based on Consensus Decision Making for Green Supplier Selection in Automotive Industry

With increased global commerce, businesses must manage their supply chains while taking into account not only costs but also environmental implications. The decision-making of Green Supplier Selection (GSS) is a strategic priority for companies to survive in challenging market conditions and to effectively and sustainably manage their supply chains in the increasingly polluted and resource-depleted world. Environmental sustainability can be enhanced with the appropriate criteria when choosing green suppliers. Based on these motivations, it is necessary to determine the correct criteria, classify the chosen criteria and employ an effective evaluation method in the GSS process. In particular, evaluating each criterion at its own level is of strategic importance. In this paper, the GSS model, handled by group decision-making, is constructed with multi-sub-criteria to increase the competitive advantage of businesses in challenging market conditions for the purpose of ensuring a sustainable future. A novel hybrid methodology of the Interval Type-2 Fuzzy (IT2F) Analytical Hierarchy Process (AHP) and IT2F Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is presented for the GSS model to deal with uncertainty. This study provides decision-makers with an effective method that performs fuzzy calculations at all steps until a solution is found, especially in areas that may have a complex hierarchical structure, such as the automotive industry. In the proposed method, unlike most studies in the literature, if a criterion has sub-criteria (or multi-sub-criteria) in the hierarchy considered, each criterion is evaluated with other criteria at its own level, without the need for all other criteria to have sub-criteria (or multi-sub-criteria). The effectiveness of the proposed method has been demonstrated by testing it with an application taken from the automotive industry with a complex-structured multi-level hierarchy. Additionally, sensitivity analysis has been conducted to assess the impact of changes in subjective input by means of scenarios.

Optimal Planning for Wind Turbines in Mega Seaports Considering Practical Application Constraints: A Case Study of Ningbo-Zhoushan Port

In the context of global carbon neutrality, ports face significant electricity demand for cargo handling and pressure to reduce carbon emissions. The abundant wind energy resources in port areas make wind power highly promising for port applications. The optimal selection of site and turbine types for wind power systems can effectively reduce emissions in ports, achieving sustainability and improving economic benefits. The practical implementation of wind energy systems considering practical constraints holds significant research significance. Taking Ningbo-Zhoushan Port as an example, this paper analyzes the wind energy resources in the port area and provides an overview of wind power system construction sites. Based on the actual conditions of the port area, this paper comprehensively reviews the site selection of wind turbines from the perspectives of wind resources, specific climates, and noise impacts. With the consideration of engineering preferences, this paper selects performance indicators based on the four mainstream turbine models and proposes a comprehensive weight determination method using the entropy weight method and analytic hierarchy process (AHP) to determine the weights of the indicators. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is then employed to score and compare four turbine plans, enabling the turbine selection process to consider both engineering preferences and objectivity, thereby enhancing the accuracy and reliability of wind turbine planning and achieving significant ecological and economic benefits through benefit analysis.

GB5mCPred: Cross-species 5mc Site Predictor Based on Bootstrap-based Stochastic Gradient Boosting Method for Poaceae

Background: One of the most prevalent epigenetic alterations in all three kingdoms of life is 5mC, which plays a part in a wide range of biological functions. Although in-vitro techniques are more effective in detecting epigenetic alterations, they are time and money-intensive. Artificial intelligence-based in silico approaches have been used to overcome these obstacles. background: One of the most prevalent epigenetic alterations in all three kingdoms of life is 5mC, which plays a part in a wide range of biological functions. Although in-vitro techniques are more effective in detecting epigenetic alterations, they are time and money intensive. Artificial intelligence-based in silico approaches have been used to overcome these obstacles. Aim: This study aimed to develop an ML-based predictor for the detection of 5mC sites in Poaceae. Objective: The objective of this study was the evaluation of machine learning and deep learning models for the prediction of 5mC sites in rice. Method: In this study, the vectorization of DNA sequences has been performed using three distinct feature sets- Oligo Nucleotide Frequencies (k = 2), Mono-nucleotide Binary Encoding, and Chemical Properties of Nucleotides. Two deep learning models, long short-term memory (LSTM) and Bidirectional LSTM (Bi-LSTM), as well as nine machine learning models, including random forest, gradient boosting, naïve bayes, regression tree, k-Nearest neighbour, support vector machine, adaboost, multiple logistic regression, and artificial neural network, were investigated. Also, bootstrap resampling was used to build more efficient models along with a hybrid feature selection module for dimensional reduction and removal of irrelevant features of the vector space. Result: Random Forest gains the maximum accuracy, specificity and MCC, i.e., 92.6%, 86.41% and 0.84. Gradient Boosting obtained the maximum sensitivity, i.e., 96.85%. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique showed that the best three models were Random Forest, Gradient Boosting, and Support Vector Machine in terms of accurate prediction of 5mC sites in rice. We developed an R-package, ‘GB5mCPred,’ and it is available in CRAN (https://cran.r-project.org/web/packages/GB5mcPred/index.html). Also, a user-friendly prediction server was made based on this algorithm (http://cabgrid.res.in:5474/). Conclusion: With nearly equal TOPSIS scores, Random Forest, Gradient Boosting, and Support Vector Machine ended up being the best three models. The major rationale may be found in their architectural design since they are gradual learning models that can capture the 5mC sites more correctly than other learning models.

Are Indian ports safe? Identifying, analysing and prioritizing the risks affecting India's major ports

Seaports are exposed to multiple risks that may affect security and operations. This research aims to develop a framework for identifying, ranking, and prioritizing the risks. Also, this study ranks the 12 major Indian ports based on the identified risk factors. The study follows an extensive literature review, prioritization by Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and sensitivity analysis for robustness. Thirty-two risks were identified and classified into five categories: natural disasters, geographical risks, security risks, operational and financial risks, and socio-political risks. The analysis suggests that the most important risk that the ports in India should consider is natural disasters. The risk of cyclones and tsunamis occupied the first two ranks among the global risks. Operational and financial risks, such as complex and lengthy approval procedures and infrastructure risks, are ranked third and fourth. Mumbai is ranked first as the riskiest Indian port, followed by Kolkata, JNPT (Jawaharlal Nehru Port Trust), and Vishakapatnam Port

Measurement and Policy Optimization of Regional Preschool Education Development Level Based on Generalized Orthogonal Fuzzy Sets and Prospect Theory

Preschool education belongs to non-compulsory enlightenment education, and it is difficult to measure and analyze the development of preschool education in different regions because of its multiple attributes and diversity of influencing factors. In addition, decision makers will be limited by their own cognition when facing multi-attribute factors and uncertain factors, and there is a big gap between the decision results given and the actual situation. Therefore, this chapter introduces generalized orthogonal fuzzy sets and prospect theory into the measurement model of preschool education development level based on technique for order of preference by similarity to ideal solution (TOPSIS) method to improve the decision accuracy of decision makers. The experimental results show that the model can effectively deal with uncertain information and improve the analysis accuracy, and the results are more in line with the actual situation than other models. At the same time, it can intuitively compare and analyze the development of preschool education in different regions, and provide reliable data support for decision makers.

Evaluation of high quality and full employment based on CRITIC-entropy-TOPSIS multi-criteria framework

PurposeThe main aim of this paper is to establish a reasonable and scientific evaluation index system to assess the high quality and full employment (HQaFE).Design/methodology/approachThis paper uses a novel Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria framework to evaluate the quality and quantity of employment, wherein the integrated weights of attributes are determined by the combined the Criteria Importance Through Inter-criteria Correlation (CRITIC) and entropy approaches.FindingsFirstly, the gap in the Yangtze River Delta in employment quality is narrowing year by year; secondly, employment skills as well as employment supply and demand are the primary indicators that determine the HQaFE; finally, the evaluation scores are clearly hierarchical, in the order of Shanghai, Jiangsu, Zhejiang and Anhui.Originality/valueA scientific and reasonable evaluation index system is constructed. A novel CRITIC-entropy-TOPSIS evaluation is proposed to make the results more objective. Some policy recommendations that can promote the achievement of HQaFE are proposed.

Investigation of flow behaviour in the nozzle of a Pelton wheel: Analysis of influencing parameters and their impact

The performance of a Pelton wheel is influenced by the jet created by the nozzle. Therefore a Computational Fluid Dynamics (CFD) simulation was proposed in this study to examine the significant output parameters (outlet velocity, outlet pressure, and tangential force component) and input parameters (different pressures and spear locations). In addition, the influencing parameters and their contributing percentages to the performance of the Pelton wheel were calculated using different optimization techniques such as Taguchi Design of Experiments (DoE), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Grey Relational Analysis (GRA) and Criteria Importance Through Intercriteria Correlation (CRITIC). The effect of input factors on the output response was examined with DoE, and the results show that the inlet pressure had the most significant impact (97.38%, 99.18%, and 97.38%, respectively, for all different spear sites with 99% confidence level). In terms of preference values, the TOPSIS and GRA results are comparable (best ranks for simulation runs #24 and #25 and least ranks for simulations #2 and #3, respectively). The CRITIC results for the pressure parameter are in good agreement with the Taguchi ANOVA analysis. The last spear location (5 mm after the nozzle outlet), with an inlet pressure of 413685 Pa generated the best result when employing the TOPSIS and GRA techniques. The outlet pressure of the nozzle was found to have a significant impact on the flow pattern of the Pelton Wheel based on the analysis of the CRITIC, Taguchi, and CFD results.

A study on the effects of objective weighting methods on TOPSIS-based parametric optimization of non-traditional machining processes

Developing advanced engineering materials with enhanced mechanical and metallurgical properties has caused non-traditional machining (NTM) processes to emerge as cost-effective and energy-efficient options. Improper settings of the input parameters of those processes may often lead to inferior quality and accuracy of the finished products. Multi-criteria decision-making (MCDM) methods appear as potent tools for multi-objective optimization of those processes. Determining criteria weights is a crucial step in any MCDM problem; subjective approaches for evaluating criteria weights may result in inconsistent judgments. In this paper, the effects of ten different objective weighting methods, i.e., equal weight, entropy weight, Criteria Importance Through Inter-criteria Correlation (CRITIC), Method based on Removal Effects of Criteria (MEREC), Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), Symmetry Point of Criterion (SPC), Criteria Impact Loss (CILOS), Integrated Determination of Objective Criteria Weights (IDOCRIW), Principal Component Analysis (PCA) and Weighted Principal Component Analysis (WPCA) are studied on Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)-based optimization of two NTM processes. In the first example, the optimal parametric combination computed using equal weight, entropy, MEREC, LOPCOW, PCA, and WPCA is noticed to be the same. In contrast, a different set of optimal parameters is derived by applying CRITIC, SPC, CILOS, and IDOCRIW. In the second example, seven out of ten approaches obtain a unique set of parametric combinations, while CILOS, IDOCRIW, and PCA lead to a different set of intermixes. It can be concluded that the ranks of the experiment trials for both the examples are almost similar irrespective of the objective criteria weighting technique employed.

Mechanical performance, impact behavior and environmental assessment of coal furnace slag based low-carbon ultra-high performance engineered cementitious composites (UHP-ECC)

This study aimed to optimize the utilization of industrial solid waste through developing a high-performance cement-based composite material (UHP-ECC), incorporating coal furnace slag (CFS) and waste rubber powder (RP). The investigation concentrated on the effects of introducing 10% RP into UHP-ECC and replacing ordinary Portland cement (OPC) with CFS at varying proportions (0%, 20%, 40%, 60%) on mechanical properties, impact resistance, and sustainability. The mixtures underwent a comprehensive characterization, including mechanical tests, thermogravimetric analysis (TGA), and scanning electron microscopy (SEM), to clarify hydration mechanisms and microstructural features. The results revealed that the addition of 10% rubber led to flaws, resulting in a minor reduction in compressive strength from 143.2 MPa to 132.2 MPa. Substituting 20% with CFS resulted in a denser matrix, raising the compressive strength to 135.5 MPa. CFS decreased the initial cracking strength and matrix fracture toughness, with tensile strain peaking at 8.05% and narrower crack widths. More CFS content yielded a slight decrease in peak impact force but induced more fine cracks and enhancing ductility. Moreover, as CFS is a waste product generated at high temperatures, the mixture demonstrated outstanding impact performance at 150 °C, absorbing rather than converting more energy into kinetic energy. Comparative analysis against similar mixtures highlighted UHP-ECC's strengths in embodied carbon, embodied energy, and material cost. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) assessment identified the CFS40-R10 as better mixture, achieving a balance between mechanical performance, cost-effectiveness, and sustainability. Microscopic analysis revealed that up to 40% CFS facilitated hydration, and the 60% CFS mixture exhibited the lowest total mass loss at high temperatures.

Global future potential evapotranspiration signal using Penman-Monteith and Hargreaves-Samani method by latitudes based on CMIP6

This study estimated global potential evapotranspiration (PET) using six climate variables of 14 CMIP6 GCMs at different latitudes of both hemispheres. Hargreaves-Samani (HS) and Penman-Monteith (PM) were used to estimate historical and future PETs. To evaluate the historical reproducibility of the six climate variables of the CMIP6 GCMs, five evaluation indicators were used and compared with the NCEP/NCAR reanalysis data. Based on the evaluation metrics, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to calculate the weights for Multi-Model Ensemble (MME) for each band of latitude. After that, the projected annual and seasonal PETs were estimated for the near (2031–2065) and far (2066–2100) futures. The change rate of PET for five timeframes (Annual, Spring, Summer, Fall, and Winter) was calculated for four socioeconomic shared pathways (SSPs). As a result, the PM's PET and HS's PET for all scenarios showed the highest increased signals at 75.5° ∼ 90° in the northern hemisphere (NH) compared to the other latitudes. Especially, the change in the HS of SSP5–8.5 was increased by 26.4% in NH latitude 45.5° to 60° (NL4) compared to the base period. Furthermore, the changes in PET of PM and HS for December, January, and February (DJF) and September, October, and November (SON), and June, July, and August (JJA) lead to remarkably high increases in both hemispheres, irrespective of the level of the emission scenario, completely ignoring the seasonal water cycle in the historical period (1950–2014).

A life cycle decision framework of China offshore wind turbines with ANP-Intuitionistic fuzzy TOPSIS method

In China, offshore wind energy is a popular source of green energy. Selecting offshore wind turbine design schemes is a decision problem based on multiple criteria. However, the selection is hardly made from the point of view of the life cycle due to the complex association of evaluation criteria, especially in the conceptual design stage. To solve this problem, a new multi-criteria decision-making framework for selecting the life cycle design scheme of offshore wind turbines is designed. The design information of the life cycle process of these turbines is expressed using Function-Structure-Material-Process-Transportation model in this framework. The life cycle evaluation index system of offshore wind turbines was established and mapped with this model to carry out rapid evaluation of various schemes. In light of this, an intuitive fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method based on analytic network process (ANP) was proposed to process fuzzy decision information and establish criteria correlation. Finally, a case study was conducted, and sensitivity and comparative analyses were carried out. The proposed method can effectively select the optimal scheme from six different design schemes for the life cycle of offshore wind turbines.

Measuring risk of renewable diesel production processes using a multi-criteria decision strategy

This study proposes measuring the risk of five alternative renewable diesel production technologies using a multi-criteria decision matrix strategy. Evaluated criteria include environmental, economic, technological, social, and process safety risks. The subjective Analytical Hierarchy Process (AHP) with stakeholder input provides criteria and sub-criteria weightings and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) ranks alternatives. Alternative renewable diesel options are Green Diesel from first, second, and third-generation feedstocks, Fischer-Tropsch Diesel from second-generation biomass, and the transesterification of vegetable oils (VO) to make biodiesel. This study is a response to an earlier work measuring the sustainability of the same renewable technologies. While the previous work indicated Fischer-Tropsch Diesel as the most sustainable, this current work indicated the process as the “most risky,” suggesting that risk is a significant driver of decision making over sustainability, and newly developed decision tools should address both perspectives.

Offshore wind power plant site selection in the Baltic Sea

The selection of suitable sites for offshore wind power plants is an important marine spatial planning problem in the Baltic Sea. The application of multiple and different decision support tools (DSTs) can facilitate site selection. This study employs a geographical information system (GIS) integrated with the levelized cost of energy (LCOE) model, and GIS with multi-criteria decision analysis (MCDA) using an analytical hierarch process (AHP), fuzzy logic, weighted linear combination (WLC), and the technique for order of preference by similarity to ideal solution (TOPSIS) as an extension of MCDA. The core novelty of the study is the use of several concurrent state-of-the art techniques to quantify the detailed spatial pattern of suitability of different sea areas for offshore wind power plant installations for the entire Baltic Sea at an unprecedented, for such a large sea area, resolution. Important parameters were identified and data from the Global Wind Atlas, HELCOM, and EMODnet portals were used. The most appropriate areas exhibit LCOE values between 42 and 58 €/MWh, MCDA values in the range of 0.78–0.92, and capacity factor values from 0.52 to 0.55. The areas identified as the least appropriate were characterized by LCOE values ranging from 107 to 210 €/MWh, MCDA values between 0.34 and 0.59, and capacity factor values within the range of 0.30–0.43. The outcomes using both methodologies were similar. The decision preferred are mostly locations close to shore in shallow water and with a high capacity factor. The largest suitable areas are in the south-west Baltic Sea whereas some suitable locations are identified further to the north and east.

Multi-attribute decision making for deep learning-based typhoon disaster assessment

Emergency rescue decisions in case of a typhoon disaster can be considered multi-attribute decision-making problems. Considering the need for the timeliness and authenticity of decision-making information sources after such a disaster, this study proposed using learning methods to process real-time online data and interval-valued neutrosophic numbers (NNs) to express the classification results. Using Typhoon Hagupit as an example, a trained text classification model was used to classify real-time data (online comments), following which the classification results were used as weights to convert these data into interval-valued NNs. Finally, the technique for order of preference by similarity to ideal solution (TOPSIS) method was adopted to rank the extent of damage caused by the typhoon in each region; the sorting results were consistent with the official statistical data, proving the effectiveness of the proposed method. A detailed sensitivity analysis was conducted to determine the optimal parameter settings of the classification model. Furthermore, the proposed method was compared with existing methods in terms of data conversion and deep learning efficiency; the results confirmed the superior capabilities of the proposed method. Notably, the proposed method can provide support to disaster management professionals in their post-disaster emergency relief work.

Experimental investigation on titanium alloys for machining of stepped circular holes using ultrasonic-assisted hybrid ECM

In the recent development of high-performance gas turbine engine, there is a tendency to design the cooling holes in order to improve the heat transfer and cooling efficiency. Titanium alloy is the most preferred material for these blades. It is hard material, and hence, the traditional drilling is not appropriate for “Ti” alloy. This research presents the design and optimization required to manufacture contoured holes on a titanium-based superalloy using hybrid electrochemical machining (ECM) under different operating conditions. The circular holes are developed and are analyzed by ANOVA. The experimental results are further optimized using a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The voltage (V), feed rate (f1), and feed rate (f2) were identified as the most influencing factors which are further used for stepped circular hole machining. A design of the experiment is also optimized using the TOPSIS method. The obtained experimental results are verified using a SEM analysis to confirm the uniformity of the machined surfaces and the inverse relationship of the overcut with the increasing values of the voltages and feed rates. Optimal machining conditions for the stepped hole machining were determined for voltage 18 V, feed rate “f1” at 0.8 mm/min, and feed rate “f2” at 1.35 mm/min.Graphical

Multi-objective optimization of twisted tri-lobed tube geometry for enhanced heat transfer and reduced resistance in turbulent flow

This study presents a detailed multi-objective optimization analysis of turbulent flow and heat transfer within a twisted tri-lobed tube, employing an integrated approach that combines Central Composite Design (CCD), Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto frontier analysis, Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The investigation focuses on optimizing key design parameters: Reynolds number (Re), twist pitch (P), and minor circle radius (r), targeting Nusselt number (Nu), Poiseuille number (fRe), and Performance Evaluation Criterion (PEC) as optimization objectives. Our major findings reveal that the developed second-order regression model precisely predicts the influences of these parameters on Nu, fRe, and PEC, with sensitivity analysis showing Re's strong positive impact on Nu and fRe, P's negative effect on fRe, and r's minimal impact. The study notably highlights the significant Nu enhancement with Re increase. Utilizing multi-objective optimization, we identified a Pareto optimal set, and through LINMAP and TOPSIS methods, derived compromise solutions that optimally balance heat transfer efficiency, flow resistance, and overall performance. These outcomes underscore the critical importance of parameter selection to meet specific engineering needs, offering a strategic framework for enhancing twisted tri-lobed tube designs.

Fuzzy Divergence Measure Based on Technique for Order of Preference by Similarity to Ideal Solution Method for Staff Performance Appraisal

Fuzzy set theory has extensively employed various divergence measure methods to quantify distinctions between two elements. The primary objective of this study is to introduce a generalized divergence measure integrated into the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. Given the inherent uncertainty and ambiguity in multi-criteria decision-making (MCDM) scenarios, the concept of the fuzzy α-cut is leveraged. This allows experts to establish a broader spectrum of rankings, accommodating fluctuations in their confidence levels. To produce consistent criteria weights with the existence of outliers, the fuzzy Method based on the Removal Effects of Criteria (MEREC) is employed. To showcase the viability and effectiveness of the proposed approach, a quantitative illustration is provided through a staff performance review. In this context, the findings are compared with other MCDM methodologies, considering correlation coefficients and CPU time. The results demonstrate that the proposed technique aligns with current distance measure approaches, with all correlation coefficient values exceeding 0.9. Notably, the proposed method also boasts the shortest CPU time when compared to alternative divergence measure methodologies. As a result, it becomes evident that the proposed technique yields more sensible and practical results compared to its counterparts in this category.