Weights Of Criteria Research Articles

Digital Measuring Instruments for Height and Weight with Microcontroller-Based Sound Output

Height and weight measuring devices simultaneously provide ideal weight information that will be handy for users. This study aims to provide an instrument that can be used as a measuring instrument for height and weight and whether or not a person's body is ideal. Therefore, in research, measuring instruments that can simultaneously measure height and weight and provide ideal information or not measured weight. This measuring instrument uses Arduino Uno as its brain, ultrasonic sensors to measure height, and strain gauge/load cell sensors to measure weight. Arduino processes data from both sensors to get the ideal body weight (BBI). The height, weight, and ideal body criteria values will be displayed on the LCD. Furthermore, the speaker will issue sound information regarding the condition of body weight, namely ideal, fat, or thin. Based on the testing and data analysis results, the average error percentage value in height measurement is 0.45%, and in weight measurement is 1.65%. At the same time, the success rate of the appearance of voice information is 99%.

Penentuan Lokasi Prioritas untuk Pengembangan Pelabuhan Perintis Menggunakan Metode Analytical Hierarchy Process

This article discusses the application of the Analytical Hierarchy Process (AHP) method by considering the weight of criteria to determine the best location alternative for pioneer port development in Indonesia. The criteria for choosing the best alternative of ports is based on Government Regulation number 61 / 2009; they are Availability of Port Facilities, Distance to Regional Port, and Economic Growth. The first step is to rank the criteria for all port alternatives based on each port’s current condition. These criteria are given weight relative to each other. This weighting benefits from prioritizing the location of pioneer ports based on the investor's point of view. Priority scores are given based on a comparison of qualitative data for each port, and then they are calculated in the Adjusted Matrix. The results determine the priority location of pioneer ports to be developed first because they are considered to provide the best benefits for development and for investors. On the other hand, other alternative locations that are not a priority do not mean they are rejected for development. To validate the consistency in giving scores, the consistency ratio was calculated, which score is less than one. The result of this article is to give the most priority to Kalimantan Tengah Port, followed by Nusa Tenggara Barat Port and Bangka Belitung Port.

Sustainable Optimization of Mold Heating: A Dual Approach with SWARA and MARCOS Methods

There are many methods and raw materials in the manufacturing of fiber reinforced plastics (FRP) with hot press, such as Sheet Molding Compound (SMC), Bulk Molding Compound (BMC), and Prepreg fabrics. In most applications, it is commonly used to put the new dough into the mold without cooling the mold, press it again and cure it. Putting the mold into the dough without cooling causes the surface of the poured product to cure faster than the inner region, thus causing a structurally discontinuous structure in the product. Therefore, in more professional manufacturings, the mold is lowered to approximately 120 oC and the dough is put into the mold at this stage. However, this increases energy consumption and carbon emissions for the heating and cooling phases. In this study, the energy efficiency in the manufacturing of fiber reinforced plastics (FRP) produced by the hot press method was investigated. At the end of this study, by using alternative energy methods in manufacturing processes, results such as investment cost, depreciation expenses, reduction in invoices, and carbon emissions were achieved. In order to find the best alternative among these results, the criteria weights were determined with SWARA, and the founded alternatives ranked by the MARCOS method. As a result of this ranking, the best alternative was determined as a 40 kWh battery and a 25 kW solar panel option among the solar panel power and battery capacity alternatives.

A Hybrid Triangular Fuzzy SWARA-MAROCS Approach for Selecting Optimal and Smart Logistic Enterprise Based on IoT, Blockchain, and UAVs

With the development of technology and the Internet of Things (IoT), the use of electronic commerce has become necessary. There are many logistical enterprises now use the IoT to support their delivery process via drones, due to the numerous advantages they offer over traditional methods. However they face some restrictions and security concerns, so blockchain technology was used to overcome these restrictions and follow up on the delivery process to meet the requirements of e-commerce. In light of this, choosing the most suitable logistics enterprises to meet the needs of customers in a given situation has become a challenging task for decision-makers. To facilitate this task, we have introduced a systematic approach that uses triangular fuzzy step-wise weight assessment ratio analysis (SWARA) and triangular fuzzy measurement of alternatives and ranking according to compromise solution (MAROCS) methods to evaluate and rank alternatives. Fuzzy SWARA is used to determine the weights of evaluative criteria, while Fuzzy MARCOS is used to rank alternatives and select the best of them. This method eliminates ambiguity and imprecision in the decision-making process.

Sustainable Afforestation Strategies: Hybrid Multi-Criteria Decision-Making Model in Post-Mining Rehabilitation

This article describes an effective approach for selecting suitable plant species for afforestation in post-mining rehabilitation. The research was conducted in the Western Black Sea region of Turkey. The aim of the research is to perform accurate criteria weighting and species prioritization for afforestation in post-mining degraded areas. This helps to ensure consistent conditions for the future use of the site as a forest, sustainability of nature, and selection of appropriate species adapted to the difficult post-mining conditions. In this study, which is a multi-criteria decision-making problem (MCDM), the weights of the criteria were determined by stepwise weight assessment ratio analysis (SWARA), and the priority ranking of the species was determined by the analytic hierarchy process (AHP). Analyses were carried out with 10 afforestation criteria and five tree species. According to the analysis, the top three ranked criteria are Economic Efficiency > Carbon Stock and Credit > Reducing Afforestation Cost. The five species’ priority ranking is Robinia pseudoacacia L. (0.456) > Alnus glutinosa subsp. glutinosa (0.248) > Populus nigra subsp. nigra (0.146) > Salix alba L. (0.103) > Quercus robur subs. robur (0.048). The hybrid approach is expected to increase the effectiveness of post-mining rehabilitation works.

CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction

To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network, we propose a convolutional neural network (CNN)-long short-term memory (LSTM) prediction model based on the incremental attention mechanism. Firstly, a traversal search is conducted through the traversal layer for finite parameters in the phase space. Then, an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria (DWC). The phase space parameters that best meet DWC are selected and fed into the input layer. Finally, the constructed CNN-LSTM network extracts spatiotemporal features and provides the final prediction results. The model is verified using Logistic, Lorenz, and sunspot chaotic time series, and the performance is compared from the two dimensions of prediction accuracy and network phase space structure. Additionally, the CNN-LSTM network based on incremental attention is compared with LSTM, CNN, recurrent neural network (RNN), and support vector regression (SVR) for prediction accuracy. The experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction accuracy. Also, the algorithm to estimate the phase space parameter is compared with the traditional CAO, false nearest neighbor, and C–C, three typical methods for determining the chaotic phase space parameters. The experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks, including CNN-LSTM, LSTM, CNN, RNN, and SVR.

Adaptation challenges of edge computing model in educational institute

Use of the Internet of Everything (IoE), the number of smart gadgets increasing rapidly giving the side effect of huge data, which has led to issues with traditional cloud computing models like inadequate security, slow response times, poor privacy, and bandwidth overload. Conventionally, cloud computing is no longer adequate for supporting the diversified needs of the user and the extraordinary society of data processing, so edge computing technologies have been revealed. This study considers edge computing in an educational institute in a scientific way. Multi criteria decision making (MCDM) is one of the most suitable decision making processes that propose to choose optimal alternatives by considering multiple conflicting criteria. Entropy weighted method is considered to evaluate factor weight. Weighted Aggregated Sum Product Assessment (WASPAS) and Combined Compromise Solution (CoCoSo) based MCDM methodologies examine the ranking of alternatives for this study. Multiple decision makers (DMs) give opinions with Pentagonal Fuzzy Soft Set (PFSS) to express the uncertainty and fuzziness of the data set. The set operations and arithmetic operations of PFSS are discussed in detail. Also, a new de-fuzzification method of PFSS is proposed in this study. Calculated the criteria weight and prioritized the alternative based on source data. Lastly, sensitivity analysis and comparative analysis are conducted to check the stability of the result.

Behavioral changes in patients with Prader-Willi syndrome receiving diazoxide choline extended-release tablets compared to the PATH for PWS natural history study

BackgroundPrader-Willi syndrome (PWS) is a rare neurobehavioral-metabolic disease caused by the lack of paternally expressed genes in the chromosome 15q11-q13 region, characterized by hypotonia, neurocognitive problems, behavioral difficulties, endocrinopathies, and hyperphagia resulting in severe obesity if energy intake is not controlled. Diazoxide choline extended-release (DCCR) tablets have previously been evaluated for their effects on hyperphagia and other behavioral complications of people with PWS in a Phase 3 placebo-controlled study of participants with PWS, age 4 and older with hyperphagia (C601) and in an open label extension study, C602.MethodsTo better understand the longer-term impact of DCCR, a cohort from PATH for PWS, a natural history study that enrolled participants with PWS age 5 and older, who met the C601 age, weight and baseline hyperphagia inclusion criteria and had 2 hyperphagia assessments ≥ 6 months apart, were compared to the C601/C602 cohort. Hyperphagia was measured using the Hyperphagia Questionnaire for Clinical Trials (HQ-CT, range 0–36). The primary analysis used observed values with no explicit imputation of missing data. A sensitivity analysis was conducted in which all missing HQ-CT assessments in the C601/C602 cohort were assigned the highest possible value (36), representing the worst-case scenario. Other behavioral changes were assessed using the Prader-Willi Syndrome Profile questionnaire (PWSP).ResultsRelative to the PATH for PWS natural history study cohort, the DCCR-treated C601/C602 cohort showed significant improvements in HQ-CT score at 26 weeks (LSmean [SE] -8.3 [0.75] vs. -2.5 [0.43], p < 0.001) and 52 weeks (LSmean [SE] -9.2 [0.77] vs. -3.4 [0.47], p < 0.001). The comparison between the cohorts remained significant in the worst-case imputation sensitivity analysis. There were also significant improvements in all domains of the PWSP at 26 weeks (all p < 0.001) and 52 weeks (all p ≤ 0.003) for C601/C602 participants compared to the PATH for PWS participants.ConclusionLong-term administration of DCCR to people with PWS resulted in changes in hyperphagia and other behavioral complications of PWS that are distinct from the natural history of the syndrome as exemplified by the cohort from PATH for PWS. The combined effects of administration of DCCR should reduce the burden of the syndrome on the patient, caregivers and their families, and thereby may benefit people with PWS and their families.Trial RegistrationClinical study C601 was originally registered on ClinicalTrials.gov on February 22, 2018 (NCT03440814). Clinical study C602 was originally registered on ClinicalTrials.gov on October 22, 2018 (NCT03714373). PATH for PWS was originally registered on ClinicalTrials.gov on October 24, 2018 (NCT03718416).

Impact of Normalization on Entropy-Based Weights in Hellwig's Method: A Case Study on Evaluating Sustainable Development in the Education Area.

Determining criteria weights plays a crucial role in multi-criteria decision analyses. Entropy is a significant measure in information science, and several multi-criteria decision-making methods utilize the entropy weight method (EWM). In the literature, two approaches for determining the entropy weight method can be found. One involves normalization before calculating the entropy values, while the second does not. This paper investigates the normalization effect for entropy-based weights and Hellwig's method. To compare the influence of various normalization methods in both the EWM and Hellwig's method, a study evaluating the sustainable development of EU countries in the education area in the year 2021 was analyzed. The study used data from Eurostat related to European countries' realization of the SDG 4 goal. It is observed that vector normalization and sum normalization did not change the entropy-based weights. In the case study, the max-min normalization influenced EWM weights. At the same time, these weights had only a very weak impact on the final rankings of countries with respect to achieving the SDG 4 goal, as determined by Hellwig's method. The results are compared with the outcome obtained by Hellwig's method with equal weights. The simulation study was conducted by modifying Eurostat data to investigate how the different normalization relationships discovered among the criteria affect entropy-based weights and Hellwig's method results.

Assessing the performance of banks through an improved sigma-mu multicriteria analysis approach

The σ-μ efficiency methodology has been recently introduced for multicriteria evaluation problems, based on the framework of Stochastic Multi-Attribute Acceptability analysis (SMAA), to address the uncertainty in the performance of a set of decision alternatives. The methodology builds, iteratively, a set of Pareto-Koopmans efficiency frontiers, which are used to assess the alternatives with respect to their expected performance and its variability, measured across different scenarios for the weights of the evaluation criteria. This paper presents an improved algorithmic implementation of this methodology that provides results that are consistent with the Pareto dominance relation between the alternatives. The proposed approach is employed to evaluate the performance of a sample of European banks which participated in the European stress tests conducted by the European Banking Authority, over the last five years available (2017–2021). The performance and efficiency of the banks is analyzed using financial criteria along with environmental, social, and governance (ESG) factors. Results from comprehensive and disaggregated analysis reveal performance disparities among banks in financial and ESG factors, highlighting the influence of country-specific green policies and individual bank practices. Valuable for the banking sector and regulators, the findings help identify operational inefficiencies and propose areas for performance enhancement, operational improvement, and innovation, with a focus on green practices.

What makes a better hotel selection? A hybrid approach considering tourists’ preference and review helpfulness

ABSTRACT Hotel selection is crucial in planning trips. Several studies constructed the decision methods, which assumed that people are completely rational and neglected decision-maker’s (DM’s) behavioural aspects, based on text reviews or numerical ratings with the subjective or objective weights of criteria. Therefore, this study proposes a picture fuzzy gained and lost dominance score (PF-GLDS) method based on prospect theory, which integrates user-generated text reviews and multi-criteria ratings to facilitate hotel selection while considering tourists’ preference and review helpfulness. First, review helpfulness is analyzed to enhance data quality. Second, a decision matrix is constructed by simultaneously considering sentiment in helpful text reviews as well as multi-criteria ratings. Third, the criteria are derived by the maximising deviation and best worst methods (BWM), respectively, for tourists’ preferred objective and subjective weights. Finally, to address hotel selection problems, an extended GLDS method with picture fuzzy information, considering bounded rationality behaviour, is developed. Empirical evaluation on five hotels from the TripAdvisor website demonstrates the reasonableness and advantages of our proposed hybrid approach in achieving more objective and reliable decision making. Concurrently, this study can serve as a reference for tourists seeking well-suited hotels and tourism website optimisation.

An integrated multi-attribute decision framework for sustainability assessment of renewable diesel fuel production pathways

Ascertaining the sustainability of a fuel production technology in the presence of conflicting factors out of different existing production pathways is a typical and time-consuming exercise. Thus, it is necessary to follow a resourceful and systematic methodology to decide the best fuel production method for any application. The present work proposes a systematic decision framework that utilizes various MCDM schemes for identifying suitable renewable diesel production technology to replace conventional diesel fuel. Sustainability assessment for a total of five production methods was conducted, based on fifteen criteria. The MOORA, VIKOR, and COPRAS techniques are utilized to compute ranking schemes for different alternatives according to integrated criteria weights based on AHP and CRITIC methods. Qualitative assessment in terms of rank reversal test and sensitivity analysis employed to compare and check the robustness of applied methodologies. The results demonstrated that FT diesel is the most suitable alternative followed by green diesel-I and feedstock price as the most persuasive criteria followed by PM2.5.The qualitative assessment revealed that the integrated weight-based MOORA technique is the most robust framework for the present decision problem, followed by VIKOR and COPRAS strategies respectively. Further, the proposed MCDM techniques can be suggested independently to encounter similar types of decision-making problems.

Synergistic Strategy of Sustainable Hospital Site Selection in Saudi Arabia Using Spherical Fuzzy MCDM Methodology

Site selection for a hospital is demanding and challenging work. It’s become more complicated when considering various infectious diseases. Multiple criteria and sub-criteria are considered for selecting the most suitable and efficient hospital location. In this paper, we find the most suitable location for multiple disease-related hospitals using the multi-criteria decision-making (MCDM) methodologies. The proposed study is done by the MCDM techniques, namely spherical Entropy and spherical VIekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. The decision-making for site selection is based on multiple experts’ opinions. Also, we use real data sources in spherical fuzzy numbers (SFN) to capture all its uncertainty. We proposed a new score function and an accuracy function of the SFN to evaluate crisp value from fuzzy numbers. The most prioritized criteria and sub-criteria are determined with their weights. We optimized the proposed site based on demand and criterion weight. Finally, sensitivity analysis and comparative analysis are conducted to check the stability and robustness of the result.

Energy Efficiency Evaluation in Automotive Industry with AHP Method and Best&amp;Worst Method

Energy is one of the most important elements for countries to exist in the competitive market in social, economic, technological and international areas. With the developing technology and increasing energy consumption, the importance of energy efficiency comes to the fore. In this context, the automotive industry, which is an important part of the industrial sector where energy is used intensively, was preferred in the study. Besides the automotive industry's need for energy, its interaction with other industries is the reason for preference. In the study, Energy Efficiency Strategy criterion weights in the Automotive Industry were calculated by using Multi-Criteria Decision Making methods. As a result of the literature research, nine main criteria and thirty-three sub-criteria were determined. The criteria weights of the Energy Efficiency Strategy in the automotive industry are analyzed separately with AHP, which is frequently used in the literature, and BEST &amp;amp; WORST, which is newly introduced to the literature. Comparing the solution results of the two methods is the aim of the study. In the light of the results obtained, it has been determined that the AHP and BEST &amp;amp; WORST methods give close results.

Risk Analysis of the Use of Drones in City Logistics

Drone delivery in city logistics is gaining attention due to road congestion, environmental threats, etc. However, there are risks associated with using drones which can result in hazardous events, such as conflicts in the air, loss of control, and system failures. It is crucial to assess the risks involved in using different types of drones and choose the option with the lowest risk. The existence of different criteria important for this decision imposes the need to apply the multi-criteria decision-making (MCDM) method(s). This paper proposes a new hybrid model that combines the fuzzy Factor Relationship (FARE) method for obtaining the criteria weights and the Axial Distance-based Aggregated Measurement (ADAM) method for obtaining the final ranking of the alternatives. A single-rotor microdrone weighing up to 4.4 lb was chosen as the optimal solution, and after that, the most favorable are also the drones of this size (multi-rotor and fixed-wing microdrones). The establishment of a novel hybrid MCDM model, the identified risks, the set of criteria for evaluating the least risky drones, and the framework for prioritizing the drones are the main novelties and contributions of the paper.

Optimization of Obstructive Sleep Apnea Management: Novel Decision Support via Unsupervised Machine Learning

This study addresses Obstructive Sleep Apnea (OSA), which impacts around 936 million adults globally. The research introduces a novel decision support method named Communalities on Ranking and Objective Weights Method (CROWM), which employs principal component analysis (PCA), unsupervised Machine Learning technique, and Multicriteria Decision Analysis (MCDA) to calculate performance criteria weights of Continuous Positive Airway Pressure (CPAP—key in managing OSA) and to evaluate these devices. Uniquely, the CROWM incorporates non-beneficial criteria in PCA and employs communalities to accurately represent the performance evaluation of alternatives within each resulting principal factor, allowing for a more accurate and robust analysis of alternatives and variables. This article aims to employ CROWM to evaluate CPAP for effectiveness in combating OSA, considering six performance criteria: resources, warranty, noise, weight, cost, and maintenance. Validated by established tests and sensitivity analysis against traditional methods, CROWM proves its consistency, efficiency, and superiority in decision-making support. This method is poised to influence assertive decision-making significantly, aiding healthcare professionals, researchers, and patients in selecting optimal CPAP solutions, thereby advancing patient care in an interdisciplinary research context.

Rough Neutrosophic Shapley Weighted Einstein Averaging Aggregation Operator and its Application in Multi-Criteria Decision-Making Problem

The study of aggregation operators has played a crucial role in various decision-making methods. The primary function of the aggregation operator is to combine multiple numbers into a single value. While Einstein operators offer a compact notation and can handle complex and large datasets, they do not consider the interaction involved in determining the criteria weights or account for imprecise and indeterminate data. To overcome this limitation, this paper introduces an improved aggregation operator, the rough neutrosophic Shapley weighted Einstein averaging aggregation operator. The rough neutrosophic sets offer a method for effectively managing the fuzziness and uncertainty that commonly occur in real-world scenarios and the Shapley fuzzy measure helps us understand the importance or value of different elements in each scenario. This operator combines the Shapley fuzzy measure with Einstein operators under rough neutrosophic sets, which are an effective tool for handling incomplete, indeterminate, and inconsistent information. The proposed operator satisfies essential algebraic properties such as idempotency, boundedness, and monotonicity. This paper also presents a decision-making methodology based on the proposed operator, with attribute values derived from the rough neutrosophic set. Finally, the applicability of the suggested aggregation operator is illustrated with a numerical example.

Assessing Dandelion Seed Dispersal and Environmental Impact: A Comprehensive Study

The study investigates the dispersal range of dandelion seeds influenced by wind, considering fruit ripening and invasive species’ impact. CFD fluid dynamics simulation using Ansys Fluent software, integrating Navier-Stokes equations and turbulence theory, analyzes seed motion characteristics and dispersion dynamics in an ideal fluid domain. Parameters like air density and viscosity, representing various seasons, were adjusted in diffusion range across different time scales.We obtained the approximate distribution of dandelion seeds across different seasons and climates, finding larger dispersal areas in arid climates due to stronger winds. In comparison, temperate climates exhibit smaller areas due to gentler winds. To assess the impact of an alien plant on the local environment, we constructed judgment criteria using the Analytical Hierarchy Process (AHP). Through pair-wise comparisons, we calculated weightings for criteria encompassing environmental interference, economic influence, and effects on human health. By assigning ratings to each criterion based on logical reasoning and scientific evidence, we quantified the plant’s impact, yielding a score of 5.74 out of 10 for dandelion, indicating an overall positive effect in the community. Sensitivity testing confirmed the robustness of our model.

Testing the suitability of vector normalization procedure in topsis method: application to wheel loader selection

The object of the research consists of testing the suitability of the vector normalization procedure (NP) in the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method. One of the most problematic steps of the Multi-Criteria Decision Making (MCDM) process is related to the application of NPs by default to transform different measurement units of criteria into a comparable unit. This is because of the absence of a universal agreement that defines which NP is the most suitable for a given MCDM method. In the literature, there are thirty-one available NPs, each one of them has its strengths and weaknesses and, accordingly, can efficiently be applied to an MCDM method and even worst to another. Let’s note that many NPs (e. g., NPs of sum, max-min, vector, and max) have been used by default (i. e., without suitability study) in the TOPSIS method. Consequently, outcomes of multi-criteria evaluation and rankings of alternatives considered in the decision problems could have led to inconsistent solutions, and, therefore, decision-makers could have made irrational or inappropriate decisions. That’s why suitability studies of NPs become indispensable. Moreover, a description of the methodology, proposed in this research, is outlined as follows: 1) method of weighting based on an ordinal ranking of criteria and Lagrange multiplier (for determining criteria weights); 2) TOPSIS method (for ranking considered alternatives); 3) a statistical approach with 3-estimate (for comparing effects generated by the used NPs). In the research, twelve different NPs are compared to each other in the TOPSIS method via a numerical example, which deals with the wheel loader selection problem. The results of the comparison indicate that, amongst the twelve different NPs analyzed in this suitability study, vector NP has the lesser effect on the considered alternatives’ evaluation outcomes, when used with the TOPSIS method. The vector NP-TOPSIS approach can therefore be applied to solve multi-criteria decision problems. Its application further allows the decision-makers and users to better select efficient solutions and, consequently, to make conclusive decisions.

Interval-valued T-spherical fuzzy extended power aggregation operators and their application in multi-criteria decision-making

Abstract As an effective tool to show the fuzziness of qualitative information, the interval-valued T-spherical fuzzy set can utilize three kinds of information, namely, membership, abstinence, and non-membership, to show the opinions of decision-maker. Given this advantage, many interval-valued T-spherical fuzzy multi-criteria decision-making (IVTSF-MCDM) methods have been designed. However, most of the existing IVTSF-MCDM methods have a common limitation that the inability to effectively show the impacts of extreme data. To address this limitation, this study develops a novel MCDM method based on interval-valued T-spherical fuzzy extended power aggregation operator. First, interval-valued T-spherical fuzzy cross-entropy (CE) and interval-valued T-spherical fuzzy symmetrical CE are defined to measure the difference between two interval-valued T-spherical fuzzy numbers, which are used to determine criteria weights in MCDM. Second, interval-valued T-spherical fuzzy extended power average operator and interval-valued T-spherical fuzzy extended power geometric operator are proposed, and their properties are investigated. Moreover, in view of that criteria may be assigned to different weights, this study defines interval-valued T-spherical fuzzy extended power weighted average operator and interval-valued T-spherical fuzzy extended power weighted geometric operator to derive the order of alternatives. Finally, the applicability of the proposed method is validated by the case about investment country selection, while the sensitivity and comparison analyses are also conducted to further prove its advantages and effectiveness.