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

Multi-Objective Optimization of Tribological Characteristics for Aluminum Composite Using Taguchi Grey and TOPSIS Approaches

In this study, a multi-objective optimization regarding the tribological characteristics of the hybrid composite with a base material of aluminum alloy A356 as a constituent, reinforced with a 10 wt.% of silicon carbide (SiC), size 39 µm, and 1, 3, and 5 wt.% graphite (Gr), size 35 µm, was performed using the Taguchi method, gray relational analysis (GRA), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making methods. Tribological tests were carried out on a “block on disc” type tribometer with lubrication. Load, sliding speed, and graphite mass concentration were analyzed as input parameters. As output parameters, wear rate and coefficient of friction were calculated. An analysis of variance (ANOVA) was conducted to identify all parameters that have a significant influence on the output multi-response. It was found that the normal load has the highest influence of 41.86%, followed by sliding speed at 32.48% and graphite addition at 18.47%, on the tribological characteristics of composites. Multi-objective optimization determined that the minimal wear rate and coefficient of friction are obtained when the load is 40 N, the sliding speed is 1 m/s, and the composite contains 3 wt.% Gr. The optimal combination of parameters achieved by GRA was also confirmed by the TOPSIS method, which indicates that both methods can be used with high reliability to optimize the tribological characteristics. The analysis of worn surfaces using scanning electron microscopy revealed adhesive and delamination wear as dominant mechanisms.

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

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

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

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

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

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

Optimization of thermal deformation process parameters of 2219 Al matrix composites

To investigate the deformation behavior and optimize the hot processing parameters for 2219 aluminum matrix composite, the constitutive equation and hot processing maps were established. Initially, hot compression experiments on 2219 aluminum alloy (2219A) were conducted using a Gleeble-3500 thermal simulation tester to obtain high-temperature rheological data. The deformation temperatures tested were 573, 623, 673, 723, and 773 K, with strain rates of 0.01, 0.1, 1, and 10 s−1, and a maximum deformation of 60 %. Subsequently, material parameters such as the activation energy, Zener–Hollomon parameter, power dissipation efficiency, and instability coefficient for 2219A were calculated. Analytical expressions for these material and deformation parameters were formulated, and a hot processing map for 2219A was constructed. The hot processing map, along with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and the Entropy Weight Method (EWM), were used to optimize the thermal deformation process parameters. The stability processing area and the optimal processing area identified by both methods were largely consistent. According to the hot processing map, the stability processing areas were identified in the temperature ranges of 580–660 K and 690–773 K with strain rates of 0.01 s−1 and 0.01–0.6 s−1, respectively. Using the TOPSIS and EWM methods, the stability processing areas were defined between 573 and 640 K and 0.01 s−1, 640–690 K and 0.01–0.1 s−1, and 690–773 K and 0.01–1 s−1. The consistency and accuracy of these optimization results were confirmed through microstructure analysis.

Safety resilience evaluation of hydrogen refueling stations based on improved TOPSIS approach

The traditional use of TOPSIS evaluation for assessing the resilience of hydrogen refueling stations results in overly one-sided outcomes and fails to identify the indicators that have a significant impact on the resilience of the stations. Therefore, this paper proposes an improved method for evaluating the safety resilience of hydrogen refueling stations based on CRITIC-DEMATEL and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The CRITIC-DEMATEL combination weighting method is employed to determine weights, thus improving the TOPSIS method. This method is then applied to assess the resilience level of hydrogen refueling stations. Then, introducing a Conditional Logistic Regression model identifies indicators with significant impact on resilience. The improved TOPSIS method enhances the reliability and accuracy of evaluation results. The Conditional Logistic Regression model provides a more accurate delineation of the impact size of indicators on the safety resilience of hydrogen refueling stations compared to traditional methods.

Wavelet denoising based on comprehensive index optimization and improved L2 regularization for load identification

Load identification, as an inverse problem, is still one of the challenges in the field of vibration engineering. In this paper, a comprehensive load identification method combining entropy weight Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) noise reduction evaluation algorithm and improved L2 regularization method is proposed to improve the load identification accuracy from the following two perspectives. Firstly, the entropy weight TOPSIS method is used to evaluate the wavelet denoising scheme comprehensively, and the optimal denoising scheme is found to reduce the noise interference in the process of inverse problem solving. Secondly, we introduce a new regularization filter function, and search for the regularization parameters by Generalized Cross-Validation (GCV) criterion and one-dimensional optimization algorithm to improve the ill-posedness of the inverse problem. Finally, the simulation and experimental verification of single-source load identification are carried out with the scaled crankcase model, while the accuracy is compared with the Tikhonov regularization method. The results show that the integrated algorithm proposed in this paper can improve the accuracy of load identification while overcoming the inadequacy of inverse problem. Unlike previous studies, the comprehensive evaluation method of denoising effect introduced in this paper provides a way to judge the denoising effect in practical engineering, and the experimental model is closer to practical engineering structures, which shows potential engineering application value.

Characteristics of Urban Flood Resilience Evolution and Analysis of Influencing Factors: A Case Study of Yingtan City, China

Intense climate change and rapid urbanization have increased the risk of urban flooding, seriously affecting urban economic and social stability. Enhancing urban flood resilience (UFR) has required a new solution to cope with urban flood disasters. In this study, taking Yingtan city as an example, a system of indicators for evaluating UFR was constructed, with 17 representative indicators, comprising three subsystems: socio-economic, ecological, and infrastructural. A hybrid model combining Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight Method (EWM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied, to develop an index-based measurement to compare and evaluate UFR, and Gray Relational Analysis (GRA) was used to discover the main factors affecting UFR. In addition, the natural discontinuous method was innovatively used to divide the UFR grade interval into levels, and the grade change was evaluated based on the TOPSIS method. The results showed that (1) From 2010 to 2022, the UFR in Yingtan City increased by 80.69%, and the factors affecting UFR were highly correlated with urban infrastructure development; however, the ecological resilience in the subsystem showed a fluctuating downward trend because of the influence of the surface area of lakes and rivers; (2) The grades of UFR for Yingtan City increased from Level III (2010 and 2016) to Level IV (2022), with local financial expenditures and the age structure of the population being the main factors currently limiting the development of UFR. The study provides a theoretical basis for the construction of an indicator system for assessing the UFR of Yingtan and proposes practical improvement directions for UFR.

Decision Support System For New Employee Selection Using AHP And TOPSIS

There are so many prospective workers with the same educational background, but not necessarily in accordance with the required company position and not necessarily they have the same expertise. To minimize the occurrence of errors, it can be done by making a decision-making system (DSS) to provide these recommendations. In this study, the Analytical Hierarchy Process (AHP) and the Technique For Order Preference by Similarity to Ideal Solution (TOPSIS) method were used to provide recommendations for prospective new employees. The steps taken are to compare the importance of each criterion weight with the AHP method. Then the ranking stage is carried out using the TOPSIS method to get recommendations for selected employees. The data used in this study is primary data in the form of 70 data on prospective employees from PT Hutama Jaya Perkasa. From the 70 data then selected to be 36 prospective employees based on the order of the highest ranking. Software testing is done by comparing the results of system calculations and the results of company calculations. Based on the test results obtained an accuracy value of 94.4%.

Comparison of TOPSIS Method and AHP Method in Determining Poor Households in Koto Baru Simalanggang

Every year, some social assistance is distributed by the Ministry of Social Affairs to each village/Nagari aimed at low-income families. The problem of poverty data almost occurs in all village/Nagari governments. In Koto Baru Simalanggang, several issues arose, including social assistance being mistargeted, poverty assessment being very complex, no system helped in DTKS, and no empowerment/system was carried out in reducing social problems; thus, all were put into DTKS without being able to see the details. Thus far, for the examination of the data of the proposer of assistance, the Nagari government still uses manual methods; therefore, it takes a long time and is subjective to the settlement of low-income families. Posdaya Bundo Saiyo is assisting in Koto Baru Simalanggang for the poor beneficiaries. There needs to be a decision support system to determine people with low incomes in this Nagari; using the method to determine people with low incomes is more objective. The results of this study are in the form of a decision support system for the determination of people with low incomes, which is used as a reference for the Bundo Saiyo Posdaya to make recommendations for the determination of people with low incomes to the Mayor of Koto Baru Simalanggang using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchy Process (AHP) methods. Based on the comparisons made in this research, the AHP and TOPSIS methods have the same ranking results

Resilience level assessment of regional water resources system based on multi-link

As environmental and human changes combine, water shortage and ecological fragility are becoming increasingly serious issues in the basin. The national “14th Five-Year Plan” proposes to build resilient basins to enhance disaster resilience. On the basis of clarifying the multi-link characteristics of water resources system resilience under the influence of meteorology-hydrology-ecology-society-economy, this paper reveals the interrelationship and chain reaction among the factors affecting the resilience of water resources system by clarifying the multi-link characteristics of water resources system resilience. Based on the interconnections among meteorology-hydrology-engineering-economic society-ecology in regional water resources systems, this study constructs a resilience evaluation index for the water resources system. The index considers different objectives, dimensions, and scales to provide a comprehensive assessment of the system’s resilience. Based on the multi-dimensional relationship of the evaluation index system, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method based on entropy weight method is employed to depict the resilience level of the water resources system at varying spatial and temporal scales. By focusing on Shanxi Province as the research subject, a multi-link evaluation framework for assessing the resilience of the water resources system is established, and a quantitative approach to evaluate its resilience is investigated. The results show that the level of water resources resilience in Shanxi Province has improved from 2017 to 2021, and there are certain spatial and temporal differences. In 2021, only Lvliang City and Yangquan City have low resilience level, and the resilience level of other cities has been improved. Among them, Taiyuan City has the highest resilience level of 0.56, which belongs to the high resilience level. The research results can improve the cognitive level of multi-link evolution mechanism of resilience to a certain extent, and provide scientific basis for multi-dimensional targeted regulation of water resources system resilience.

Optimizing irrigation schedules of greenhouse tomato based on a comprehensive evaluation model

In the context of climate change, water availability has emerged as a pressing concern in agriculture, indicating the urgency to enhance irrigation management. Nevertheless, accurately quantifying irrigation schedules in diverse climates and soil textures remains a complex challenge. Here, a three-year irrigation experiment (2020–2022) of greenhouse tomato was conducted at three typical locations (Yulin, Yangling and Hanzhong) in northwest China. The irrigation schedules were determined by the cumulative evaporation (CE) measured from a 20 cm pan. A total nine treatments were set by combining three irrigation frequencies (IF) with three single doses (ID). Twenty indices related to tomato growth, photosynthesis, yield and water use, fruit quality, and environmental benefit were evaluated comprehensively by using TOPSIS (technique for order preference by similarity to ideal solution) and GRA (grey relational analysis) methods of combining weights based on game theory. The results showed that the indicator with highest weight was yield, followed by crop water productivity and vitamin C. The optimal ranges of irrigation frequency (irrigation dose) were determined as follows: 10.00–11.08 mm (0.81–0.90 CE) in Yulin, 13.12–15.68 mm (0.77–0.89 CE) in Yangling, and 11.44–16.40 mm (0.67–0.77 CE) in Hanzhong, respectively. This study introduces a method applied to prioritize the key indicators for irrigation scheduling, providing a reference for optimizing irrigation practices in greenhouse tomato production and improving water management in agriculture.

Assessment of Service Quality in Aviation Using the CRITIC-Supported TOPSIS Method: A Case Study of Central Airports in Asia

In this study, the service quality performance of 14 central airports that are members of SKYTRAX in the Asian continent are analyzed. The data of 2022 are taken as basis in the analysis process. The aim of the study is to evaluate the service quality of the airports included in the analysis. In order to compare the service quality performances of the selected airports, The Criteria Importance Trough Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods from Multi-Criteria Decision-Making Methods (MCDM) are used. The data of the airports included in the analysis are obtained through SKYTRAX. While the importance degrees of the criteria of the alternatives are calculated by the CRITIC method, the ranking of the alternatives is obtained by the TOPSIS method. The criteria used in the analysis are security screening, arrival, departure, terminal comfort, terminal facilities, food and beverage services. According to the findings of the CRITIC method; it is concluded that the most important criterion is departure and the least important criterion is terminal comfort. According to the results of the TOPSIS method; Singapore Changi Airport ranks first in terms of service quality performance. Seoul Incheon and Tokyo Haneda airports follow this airport in the ranking. Mumbai Chhatrapati Airport takes the last place.

Towards explainable TOPSIS: Visual insights into the effects of weights and aggregations on rankings

Multi-Criteria Decision Analysis (MCDA) is extensively used across diverse industries to assess and rank alternatives. Among numerous MCDA methods developed to solve real-world ranking problems, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) remains one of the most popular choices in many application areas. TOPSIS calculates distances between the considered alternatives and two predefined ones, namely the ideal and the anti-ideal, and creates a ranking of the alternatives according to a chosen aggregation of these distances. However, interpreting the inner workings of TOPSIS is difficult, especially when the number of criteria is large. To this end, recent research has shown that TOPSIS aggregations can be expressed using the means (M) and standard deviations (SD) of alternatives, creating MSD-space, a tool for visualizing and explaining aggregations. Even though MSD-space is highly useful, it assumes equally weighted criteria, making it less applicable to real-world ranking problems. In this paper, we generalize MSD-space to arbitrary weighted criteria by introducing the concept of WMSD-space defined by what is referred to as weight-scaled means and standard deviations. We demonstrate that TOPSIS and similar distance-based aggregation methods can be successfully illustrated in a plane and interpreted even when the criteria are weighted, regardless of their number. The proposed WMSD-space offers thus a practical method for explaining TOPSIS rankings in real-world decision problems.

Comparison of TOPSIS and SMARTER Methods in Selecting Delivery Services

The rapid growth of the e-commerce world has propelled the demand for freight forwarding services, a pivotal component in maintaining the smooth flow of this business. Major companies like JNE, TIKI, Kantor Pos Indonesia, SiCepat, and J&T Express are involved. However, despite this convenience, various challenges often accompany the shipping process. Some of these include delayed deliveries, lost or damaged items, or even misdeliveries to the wrong customers. This presents a dilemma for leading e-commerce companies in selecting the most suitable delivery service partner. Hence, a decision support model is necessary in choosing a freight forwarding service. This study will outline comparison methods based on both Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Multi Attribute Rating Technique Exploiting Ranks (SMARTER). The proposed methods have strong relevance to the rapid growth of the e-commerce world. This research emphasizes the importance of selecting freight forwarding services in maintaining the smooth operation of e-commerce businesses. The results of this study have obtained ranking results from the final value of each method. The first place in the TOPSIS method with a value of 0.7033 at sensitivity 3 and the lowest in the SMARTER method with a value of 0.1303 at the first sensitivity, and at the second sensitivity, all methods have the same value of 0.2. The conclusion is that TOPSIS is the best method compared to the SMARTER method as a decision support for the selection of freight forwarding services.

Information Fusion for the Training of Public Administrators: Design of a Composite Indicator through the Integration of AHP and TOPSIS Methods

The article addresses the importance of capacity development among public administrators to ensure the efficient functioning of the state and the provision of quality services to the citizenry. This entails acquiring legal knowledge, ethical principles, and technical skills in areas such as strategic planning, financial management, and policy evaluation. In addressing the evaluation of public administrators' training programs, the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods are employed. The methodology delineates the fundamental concepts and key equations to understand the application of these methods in evaluating public administrators' training programs. The findings underscore the significance of specific competencies in public administrators' training. These competencies, combined with comprehensive training encompassing cognitive, attitudinal, and instrumental dimensions, can equip professionals to confront current governmental challenges effectively. The article emphasizes the relevance of capacity development among public administrators and introduces methods such as AHP and TOPSIS to assess and enhance training programs in this field, aiming to bolster the state's capacity to respond efficiently to societal needs.

IDEAL LOCATION SELECTION FOR CONTACTLESS PARCEL PICK-UP POINTS

The post-Covid era has witnessed the adoption of various new habits in our daily lives, particularly in relation to the ubiquitous e-commerce platforms that have become essential for urban populations. The surge in e-commerce activities and the intensified volume in delivery of packages during the pandemic sparked innovative ideas. This study explores one such creative concept: parcel pick-up points. We conducted a pioneering research endeavor to determine the optimal locations for these pick-up points in Istanbul, Turkey. Our methodology employed a novel hybrid approach, combining the Spherical Fuzzy Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The evaluation of criteria importance was facilitated by a literature review and experienced high-level managers in the cargo industry, who determined the criteria weights using the Spherical Fuzzy AHP method. Subsequently, the TOPSIS method was employed to identify the most ideal locations, leading to the selection of Kadikoy, Umraniye, and Atasehir. This study provides valuable insights into the selection of the optimal locations for parcel pick-up points in Istanbul, Turkey, which can inform policymakers, e-commerce companies, and logistics stakeholders. The proposed hybrid approach demonstrates the integration of modern smart technologies with fuzzy decision-making techniques, offering a robust framework for decision support in the field of e-commerce logistics. Future research can further explore the implementation and effectiveness of these pick-up points to enhance the efficiency and convenience of last-mile deliveries in urban areas.

Evaluation of PPP-ABS investment environment based on combined weighting of level difference maximization and TOPSIS method.

Asset-backed securitization (ABS) is currently used to refinance public-private partnership (PPP) projects in the infrastructure field. To stimulate the investors' enthusiasm, this study evaluated the investment environment of PPP projects asset-backed securitization (PPP-ABS). Firstly, we established a PPP-ABS investment environment evaluation indicator system based on the literature review and the practice of PPP-ABS. Then, the optimal weights of each indicator were determined by the combined weighting of level difference maximization method, where the subjective weights were determined by the AHP method, and the objective weights were determined by the entropy method. Finally, we evaluated the PPP-ABS investment environment from 2015 to 2022 with the technique for order preference by similarity to ideal solution (TOPSIS) method. The final valuation results are consistent with the actual situation. The results showed that the PPP-ABS investment environment exhibits a stable and upward trend. Under the overall guidance of the government, the approval process, information disclosure and supervisory systems have continued to improve, the number of ABS products issued has continued to grow, and the overall market risk is controllable. However, some problems still need to be solved and improved, including inadequate accounting and tax systems, insufficient liquidity in the secondary market, and the recovery of economic development in the post-COVID-19 era. This study fills the research gap in PPP-ABS. It proves the rationality and feasibility of PPP-ABS and is expected to provide a reference for investors' decision-making and promote the sustainable and healthy development of PPP-ABS.

Streamlining apartment provider evaluation: A spherical fuzzy multi-criteria decision-making model

In the context of the thriving real estate market in developing countries like Vietnam, understanding consumer preferences and effectively addressing them through a comprehensive multi-criteria decision-making (MCDM) framework is paramount for real estate providers. This study presents a two-stage MCDM model that integrates the Delphi technique and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on Spherical Fuzzy Sets (SFSs). Initially, the SF-Delphi technique validates critical criteria influencing customers' apartment selection in Vietnam. Secondly, the SF-TOPSIS method evaluates the top ten apartment providers. To ensure robustness and validity, a comparative analysis compares the results with those from the Intuitionistic Fuzzy TOPSIS (IF-TOPSIS) and Fuzzy TOPSIS (F-TOPSIS) methods. Subsequently, five rank correlation coefficients (Spearman, Kendall, Goodman-Kruskal, Weighted rank measure of correlation, Weighted Similarity) are used to assess the relationships between various TOPSIS techniques applied to apartment suppliers in Vietnam. The correlation coefficients demonstrate strong agreement among the TOPSIS methods, with the smallest coefficient being 0.7778, surpassing the threshold of 0.7. This high level of consistency confirms the efficacy of the proposed TOPSIS approach with different Fuzzy Sets in reliably evaluating customers' preferences for apartment suppliers. Notably, the legal aspect's prominence underscores its critical role in shaping customer choices, emphasizing the significance of considering legal factors in the context of apartment supply and demand in Vietnam. Furthermore, using SFSs makes this approach particularly suited to capture consumer perceptions within the dynamic and uncertain business environment characterized by volatility, uncertainty, complexity, and ambiguity (VUCA).

Sistem Pendukung Keputusan Pemilihan Jurusan Menggunakan Metode TOPSIS Berbasis Web Studi Kasus: SMK Negeri 38 Jakarta Pusat

The selection of majors at SMK Negeri 38 Jakarta currently relies on a single opportunity during registration, without considering various factors that might influence students' choices. This underscores the necessity for developing a decision support system that can aid prospective students in selecting majors aligned with their interests, potentials, and characteristics. The aim of this research is to design and implement a decision support system using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method as an alternative to facilitate a more informed decision-making process. The research methodology involves data collection from prospective students, including personal interests, the average grades from the first to the fifth semester of junior high school, average school examination scores, registration pathways, and percentiles of grades. This data serves as input for the TOPSIS method to generate recommendations for study focus options that suit the characteristics of each prospective student. The research findings indicate that the implementation of a TOPSIS-based decision support system offers a more accurate possibility for major selection, aligning with the potentials and interests of prospective students. This system allows for data filtering and processing to produce recommendations guiding prospective students in choosing a suitable study focus among three available options: Hospitality, Culinary Arts, and Fashion Design. Conclusively, the decision support system using the TOPSIS method proves to be an effective solution in aiding new prospective students at SMK Negeri 38 Jakarta in selecting majors aligned with their interests and potentials, thereby reducing the tendency for errors in major selection based on a single registration opportunity.