A Multi-Criteria Neutrosophic Group Decision Making Method Based TOPSIS for Supplier Selection

Purpose - The purpose of this study, Borsa Istanbul 100 (BIST100) of the financial performance of energy companies traded multi-criteria decision making methods, TOPSIS (Technique for order preference by Similarity to ideal solution) method is measured. Methodology - For this purpose, 5 energy companies traded in BIST in 2021 and whose data are regularly accessed have been included in the scope of the study. The annual balance sheet and income statements of the companies were used in the study. The balance sheets and income statements of the companies within the scope of the research for the year 2021 were taken from the official website of the Public Lighting Platform (KAP). During the period when the application part of this study was conducted, the data of the year 2022 could not be used due to the fact that the data of the year 2022 has not yet been published. The obtained data were measured by TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, which is one of the multi-criteria Decisionmaking methods, and the performance of the companies were analyzed and compared among themselves. Findings - Ipek Natural Energy Resources Research and Production A. One of the companies included in the study as a result of the analyzes performed by the TOPSIS method.Sh. ((IPEK) was found to be the company with the best data in terms of financial performance in the analyzes carried out in accordance with the criteria included in the evaluation. After this company, the second best company in terms of financial performance is Koza Anadolu Metal Madencilik Işletmeleri A.Sh. He appeared as a (COCOON). Conclusion - In the light of the analyzes carried out in the study; IPEKE company ranked first as the best company in terms of financial performance. Ardın KOZAA of IPEKE company ranked second, ASELS company ranked third, AKSEN company ranked fourth and VESTL company ranked last in terms of financial performance among the 5 companies included in the study. Dec. Apart from these data, it can be said that changing the December period to be based on different studies, using different financial ratios, the degree of importance given to criteria, using other multi-criteria decision-making methods (MSCS) such as MOORA other than TOPSIS may give results that may change the performance ranking. Keywords: BIST-100, TOPSIS, financial performance, energy, MCDMM JEL Codes: C61, M41, M49

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

Initial water rights allocation is a complex decision problem, and it is difficult for traditional methods to determine weights and solve the decision problem. In this study, an improved TOPSIS (Technique for order preference by similarity to ideal solution) method is proposed using vertical distance instead of Euclidean distance to calculate the closeness degree of schemes to the ideal solution, which overcomes the shortage of traditional TOPSIS method. In order to determine the weights of evaluation indexes reasonably, the improved method adopts game theory to harmonize and conjugate the subjective weight and the objective weight. On this basis, a comprehensive weight which eliminates the unilateral result can be obtained, and a model of initial water rights allocation in the watershed is established by the improved TOPSIS method. Finally, the proposed model is applied in the study of initial water rights allocation in Fuhuan River in Hubei Province, which shows rationality and validity when comparing with other models like traditional TOPSIS method, analytic hierarchy process and projection pursuit model.

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.

Every year LLDIKTI XI Kalimantan provides scholarships to universities under its auspices. Which Uniska has received scholarships since 2015-2018 as many as 152 bidik misi students for new students. Which is usually selected using manual steps with the help of human power. From the selection process there are problems, namely human factors. Therefore we need a computational process that supports the selection process. Then the SVM (Support Vector Machine) method is used for the classification process and the TOPSIS (Technique For Order Preference By Similarity To Ideal Solution) method is used to give a priority ranking of scholarship. The average speed of the entire process in the selection system and recommendations for the acceptance of the UNISKA Bidik Misi scholarship with the implementation of the SVM and TOPSIS methods using testing from a comparison ratio of 19.12 seconds, the fastest time is 14.40 and the longest time is 23.58. The accuracy of the selection and recommendation of acceptance of the UNISKA Bidik Misi scholarship using the training data comparison ratio and 90%: 10% data testing has an average accuracy of 85.53% and testing based on the best parameters of the SVM sequential training process is λ (Lambda) = 0.1 , constant γ (gamma) = 0.05, e = 0.0001, Maximum Iteration = 1000, ratio of 90%: 10% and value of d = 2, C (Complexity) = 1. So that the best accuracy is 100% and the average accuracy the best is 93.63%. Keywords: Selection, UNISKA MAB, SVM, TOPSIS, MCDM.

The Purpose research is to develop a new interval value fuzzy by integrating Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for multiple criteria group decision-making model (FMCGDM). The Interval Value Analytic Hierarchy Process (IVFAHP) approach is capable of elaborating complex multi-criteria problems into a hierarchical structure and determining the optimal weight in multi-criteria decision-making. In determining the best alternative, the Interval Value Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IVFTOPSIS) approach is well-suited for being able to make multi-decisions based on the alternative that has the shortest distance from the ideal solution positive and furthest from the ideal negative solution. The results study is a new framework Interval Value Fuzzy (AIVF) decision-making model with flexible linguistic preferences and a high degree of accuracy. The method is implemented in mapping strategy selection problem for measurement e-learning.

Integration of fuzzy set theory and TOPSIS into HFMEA to improve outpatient service for elderly patients in Taiwan

The selection of thesis supervisors is a crucial process in higher education but is often conducted manually and subjectively, leading to mismatches in expertise, unequal workload distribution, and inefficiencies. This study aims to develop a decision support system (DSS) to assign thesis supervisors objectively and fairly. The approach combines the Entropy and TOPSIS methods. The Entropy method is used to calculate objective weights for five criteria: academic rank, field of expertise, student preference, supervision quota, and student graduation speed. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is then applied to rank supervisor candidates based on their proximity to ideal solutions. The results demonstrate that the system effectively recommends the most suitable supervisor, with Lecturer G identified as the top choice. This system is expected to enhance the efficiency, transparency, and equity of the thesis supervisor assignment process within the Informatics Engineering Program at Malikussaleh University.

Comprehensive evaluation of wild Cordyceps cicadae from different geographical origins by TOPSIS method based on the macroscopic infrared spectroscopy (IR) fingerprint.

"Coal-to-electricity" project can effectively reduce the air pollution in the northern China, and their benefits evaluation can reasonably reflect the effect of the projects. According to the present situation of the "coal-to-electricity" projects in a northern city, the evaluation index system is put forward to estimate the benefits of the projects with the aspects of economic benefits, social benefits and operation status of the distribution networks. A statistic method - TOPSIS (technique for order preference by similarity to ideal solution) is used to evaluation and analysis. In this method, the index system is weighted by the combination of analytic hierarchy process (AHP) and entropy weighting method. In the evaluation system of "coal-to-electricity" project, combination weighting - TOPSIS method can sort the different projects based on the distance between their comprehensive benefits and the positive and negative ideal solution. After the analysis of "coal-to-electricity" region with poor benefits, some recommendations can be given. Three actual project examples are applied for the evaluation system, and then the evaluation result of the examples is compared. Finally, the result is analyzed, while some improvement measures are put forward in these examples, which can give the reference and the guidance for the further implementation of the projects.

PurposeThis paper aims to optimize the process parameters of the fused deposition modelling (FDM) process using the Grey-based Taguchi method and the results to be verified based on a technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) calculation.Design/methodology/approachThe optimization of process parameters is gaining a potential role to develop robust products. In this context, this paper presents the parametric optimization of the FDM process using Grey-based Taguchi, TOPSIS and AHP method. The effect of slice height (SH), part fill style (PFS) and build orientation (BO) are investigated with the response parameters machining time, surface roughness and hardness (HD). Multiple objective optimizations were performed with weights of w1 = 60%, w2 = 20% and w3 = 20%. The significance of the process parameters over response parameters is identified through analysis of variance (ANOVA). Comparisons are made in terms of rank order with respect to grey relation grade (GRG), relative closeness and AHP index values. Response table, percentage contributions of process parameters for both GRG and TOPSIS evaluation are done.FindingsThe optimum factor levels are identified using GRG via the Grey Taguchi method and TOPSIS via relative closeness values. The optimized factor levels are SH (0.013 in), PFS (solid) and BO (45°) using GRG and SH (0.013 in), PFS (sparse-low density) and BO (45°) using TOPSIS relative closeness value. SH has higher significance in both Grey relational analysis and TOPSIS which were analysed using ANOVA.Research limitations/implicationsIn this research, the multiple objective optimizations were done on an automotive component using GRG, TOPSIS and AHP which showed a 27% similarity in their ranking order among the experiments. In the future, other advanced optimization techniques will be applied to further improve the similarity in ranking order.Practical implicationsThe study presents the case of an automotive component, which illustrates practical relevance.Originality/valueIn several research studies, optimization was done on the standard test specimens but not on a real-time component. Here, the multiple objective optimizations were applied to a case automotive component using Grey-based Taguchi and verified with TOPSIS. Hence, an effort has been taken to find optimum process parameters on FDM, for achieving smooth, hardened automotive components with enhanced printing time. The component can be explored as a replacement for the existing product.

One of the most significant competitive strategies for organizations is sustainable supply chain management (SSCM). The vital part in the administration of a sustainable supply chain is the sustainable supplier selection, which is a multi-criteria decision-making issue, including many conflicting criteria. The valuation and selection of sustainable suppliers are difficult problems due to vague, inconsistent and imprecise knowledge of decision makers. In the literature on supply chain management for measuring green performance, the requirement for methodological analysis of how sustainable variables affect each other, and how to consider vague, imprecise and inconsistent knowledge, is still unresolved. This research provides an incorporated multi-criteria decision-making procedure for sustainable supplier selection problems (SSSPs). An integrated framework is presented via interval-valued neutrosophic sets to deal with vague, imprecise and inconsistent information that exists usually in real world. The analytic network process (ANP) is employed to calculate weights of selected criteria by considering their interdependencies. For ranking alternatives and avoiding additional comparisons of analytic network processes, the technique for order preference by similarity to ideal solution (TOPSIS) is used. The proposed framework is turned to account for analyzing and selecting the optimal supplier. An actual case study of a dairy company in Egypt is examined within the proposed framework. Comparison with other existing methods is implemented to confirm the effectiveness and efficiency of the proposed approach.

The distribution of scholarships is carried out to assist in the determination of recommending someone who deserves to receive a scholarship, a Decision Support System is needed because the system for selecting scholarship candidates is still manual, and has many weaknesses. The large number of scholarship participant applicants makes schools having difficulty handling manual data processing so that software is needed to simplify the data processing. There for not all students who apply to receive scholarships can be granted, because the number of students who apply is very large, it is very necessary to build an SPK with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method which can help provide recommendation for scholarship recipients. Based on the analysis of the DSS with the TOPSIS method, it was carried out by means of a questionnaire, interview observation and system implementation. In the assessment of scholarship acceptance, it can be used as a basis for facilitating decisions on scholarship recipients because the system will process data and provide information quickly, precisely and consistently to the principal of students to receive the best scholarships to be awarded. Can form a decision that is right, effective and efficient in managing data on student recipients who are truly entitled to receive the scholarship. The TOPSIS method can be used to determine scholarship recipients, SPK in the assessment of scholarship acceptance can facilitate decisions in grade 7 students of SMP Negeri 17 Padang proportionally based on the results of student data processing including family cards, parents 'jobs, parents' income, number of dependents of parents and age parents accurately and accurately because the system can minimize errors in the process of calculating data normalization

This paper proposed a concept of the Group Decision Support System (GDSS) to evaluate the performance of Information and Communications Technology (ICT) Projects in Indonesian regional government agencies to overcome any possible inconsistencies which may occur in a decision-making process. By considering the aspect of the applicable legislation, decision makers involved to provide an assessment and evaluation of the ICT project implementation in regional government agencies consisted of Executing Parties of Government Institutions, ICT Management Work Units, Business Process Owner Units, and Society, represented by DPRD (Regional People’s Representative Assembly). The contributions of those decision makers in the said model were in the form of preferences to evaluate the ICT project-related alternatives based on the predetermined criteria for the method of Multiple Criteria Decision Making (MCDM). This research presented a GDSS framework integrating the Methods of Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Copeland Score. The AHP method was used to generate values for the criteria used as input in the calculation process of the TOPSIS method. Results of the TOPSIS calculation generated a project rank indicated by each decision maker, and to combine the different preferences of these decision makers, the Copeland Score method was used as one of the voting methods to determine the best project rank of all the ranks indicated by the desicion makers.

This research aims to develop a Decision Support System (DSS) using the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assist the Ministry of Health in selecting the most suitable medical equipment distributor. The AHP method is employed to determine the weight of multiple evaluation criteria, including Quality Management System, Human Resources Management, Infrastructure, Inventory Handling, Traceability, Complaint Handling, FSCA, Returns, Disposal, Illegal Access, Internal Audit, Management Review, and Third-Party Activities. Once the weights are established, the TOPSIS method is applied to evaluate and rank the distributor alternatives based on their relative proximity to the ideal and anti-ideal solutions. The integration of AHP and TOPSIS ensures a more structured, objective, and data-driven decision-making process. The results show that the distributor labeled D4 has the highest preference value (0.64632), indicating the best performance among all alternatives evaluated. This combined method enhances decision-making accuracy, reduces subjectivity, and aligns selection outcomes with operational and regulatory standards. The study concludes that implementing a DSS using AHP and TOPSIS can significantly improve the efficiency, transparency, and effectiveness of medical equipment distributor selection within the healthcare logistics system.