A Study on Aggregation of TOPSIS Ideal Solutions for Group Decision-Making

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.

The Analytical Hierarchy Process (AHP) approach and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method are two methods that are commonly used to handle multicriteria problems. The AHP technique uses a matrix of pairwise comparisons and a hierarchical approach to criteria in order to obtain the final criteria weights. In contrast, the TOPSIS approach incorporates the concepts of proximity to the ideal solution and preference. Next, TOPSIS uses the previously established criterion weights to compute each option's relative closeness score to the ideal answer. In order to determine how effectively the AHP and TOPSIS methodologies complement one another, this study aims to assess the protocols and underlying assumptions of the two approaches. When decision makers are aware of the benefits and drawbacks of each approach, they may choose the best course of action from a mix of the two. The results of the study indicate that the process may be streamlined, subjectivity can be reduced, and criterion weights can be stabilized by combining the AHP and TOPSIS approaches.The Analytical Hierarchy Process (AHP) approach and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method are two methods that are commonly used to handle multicriteria problems. The AHP technique uses a matrix of pairwise comparisons and a hierarchical approach to criteria in order to obtain the final criteria weights. In contrast, the TOPSIS approach incorporates the concepts of proximity to the ideal solution and preference. Next, TOPSIS uses the previously established criterion weights to compute each option's relative closeness score to the ideal answer. In order to determine how effectively the AHP and TOPSIS methodologies complement one another, this study aims to assess the protocols and underlying assumptions of the two approaches. When decision makers are aware of the benefits and drawbacks of each approach, they may choose the best course of action from a mix of the two. The results of the study indicate that the process may be streamlined, subjectivity can be reduced, and criterion weights can be stabilized by combining the AHP and TOPSIS approaches.

A famous multi-criteria decision-making(MCDM) method called technique for order preference by similarity to ideal solution(TOPSIS) was often fuzzy generalized into fuzzy multi-criteria decision-making(fuzzy MCDM). In generalizing TOPSIS, fuzzy preference relation on interval-valued fuzzy numbers(IVFNs) is to find the ideal solution and anti-ideal solution for reserving lots of information. Usually, the work is complicated on computation. Practically, IVFNs is more difficult than others. In order to resolve the above tie for the decision-making problem about airport performance evaluation, we utilize TOPSIS and relative preference relation(RPR) in fuzzy MCDM. By combining RPR with TOPSIS, the performance evaluating problem of airports could be easily solved.

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.

Although the cost-effectiveness of preventive maintenance (PM) treatments for pavement is important, literature addressing this issue is limited. Even under the well-controlled FHWA long-term pavement performance (LTPP) study, incomplete data and sections exist. Criteria for selecting PM treatments often conflict and have to be compromised. The multiple criteria decision-making (MCDM) method is one of numerous approaches available for resolving variations of results. The technique for order preference by similarity to ideal solution (TOPSIS), an MCDM method, was used to analyze successfully all 14 specific pavement study (SPS)-3 sites in Texas. The distress score (DS), international roughness index (IRI), and treatment costs were used as criteria to determine the cost-effectiveness of various PM treatments (thin overlay, slurry seal, crack seal, and chip seal). With TOPSIS, the cost-effectiveness of these treatments can be quantified, with variations caused by subjective judgment thus minimized. When all ...

The process of multiple criteria decision making (MCDM) is of determining the best choice among all of the probable alternatives. The problem of supplier selection on which decision maker has usually vague and imprecise knowledge is a typical example of multi criteria group decision-making problem. The conventional crisp techniques has not much effective for solving MCDM problems because of imprecise or fuzziness nature of the linguistic assessments. To find the exact values for MCDM problems is both difficult and impossible in more cases in real world. So, it is more reasonable to consider the values of alternatives according to the criteria as single valued neutrosophic sets (SVNS). This paper deal with the technique for order preference by similarity to ideal solution (TOPSIS) approach and extend the TOPSIS method to MCDM problem with single valued neutrosophic information. The value of each alternative and the weight of each criterion are characterized by single valued neutrosophic numbers. Here, the importance of criteria and alternatives is identified by aggregating individual opinions of decision makers (DMs) via single valued neutrosophic weighted averaging (IFWA) operator. The proposed method is, easy use, precise and practical for solving MCDM problem with single valued neutrosophic data. Finally, to show the applicability of the developed method, a numerical experiment for supplier choice is given as an application of single valued neutrosophic TOPSIS method at end of this paper.

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

<abstract><p>Many real-world decision-making issues frequently involve competing sets of criteria, uncertainty, and inaccurate information. Some of these require the involvement of a group of decision-makers, where it is necessary to reduce the various available individual preferences to a single collective preference. To enhance the effectiveness of multi-criteria decisions, multi-criteria decision-making is a popular decision-making technique that makes the procedure more precise, reasonable, and efficient. The "Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)" and "Elimination and Choice Transforming Reality (ELECTRE)" are prominent ranking methods and widely used in the multi-criteria decision-making to solve complicated decision-making problems. In this study, two $ m $-polar fuzzy set-based ranking methods are proposed by extending the ELECTRE-Ⅰ and TOPSIS approaches equipped with cubic $ m $-polar fuzzy (C$ m $PF) sets, where the experts provide assessment results on feasible alternatives through a C$ m $PF decision matrix. The first proposed method, C$ m $PF-TOPSIS, focuses on the alternative that is closest to a C$ m $PF positive ideal solution and farthest away from the C$ m $PF negative ideal solution. The Euclidean and normalized Euclidean distances are used to determine the proximity of an alternative to ideal solutions. In contrast, the second developed method is C$ m $PF-ELECTRE-Ⅰ which uses an outranking directed decision graph to determine the optimal alternative, which entirely depends on the C$ m $PF concordance and discordance sets. Furthermore, a practical case study is carried out in the diagnosis of impulse control disorders to illustrate the feasibility and applicability of the proposed methods. Finally, a comparative analysis is performed to demonstrate the veracity, superiority, and effectiveness of the proposed methods.</p></abstract>

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

"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.

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.

Traditionally, the planning and design of road infrastructure projects relied on conventional methods. However, with technological advancements, the InfraWorks tool, integrated into BIM, has become essential for planning these projects in a realistic environment. This process begins with the evaluation of multiple route alternatives, comparing them based on technical, environmental, and economic criteria, to select the optimal option. The primary objective of this research is to replace the most flood-prone road section among those connecting the city of Sidi Belattar to RN 90, located on the left bank of the Chellif River in Algeria. To achieve this, the multi-criteria decision-making (MCDM) methods VIKOR and TOPSIS are used to evaluate and rank the different options. The analysis is based on MCDM methods, combining two complementary approaches: VIKOR (VIsekriterijumsko KOmpromisno Rangiranje) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), to rank the alternatives. The results revealed a general consistency between the two methods, designating the first alternative as the optimal solution to replace the vulnerable section of the existing route. In conclusion, this approach represents a significant advancement thanks to the integration of InfraWorks and MCDM methods, offering an innovative approach to optimize the planning and design of road infrastructure from the early stages of development.

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.

TOPSIS for bi-level MODM problems

Multi-Criteria Decision Making (MCDM) method plays a key role to find out the best option from all possible alternatives. From different application areas Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one of the Multi-Criteria Decision Making (MCDM) method to find out the correct result. TOPSIS is a real-world and valuable procedure for ranking and selection of a number of externally determined substitutes through distance measures. In this paper, application of techniques for order preference by similarity of an ideal solution (TOPSIS) method is applied for solving multiple criteria (objective) optimization problem in mechanical and physical properties of jute/Al2O3 fiber based hybrid composites. The best performance value of composite is designed from ideal solution and as well as the worst performance value of composite is designed for negative-ideal solution. The results have demonstrated the model to be both robust and efficient.