Optimization parameters for the cotton swab process using hybrid MCDM methods based on response surface methodology

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 ...

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.

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.

This research aims to evaluate the performance of contract employees using the Weighted Aggregated Sum Product Assessment and Borda methods. The Weighted Aggregated Sum Product Assessment approach uses mathematical calculations, thereby producing precise and accurate results. The Borda technique is useful because it efficiently aggregates diverse rankings of alternatives through voting groups that combine many preferences. The choice of these two methodologies has the ability to produce more efficient solutions to complex problems by guiding or making decisions more quickly in resolving problems. The determining criteria for assessing the performance of contract employees are punctuality or attendance, skills, knowledge, communication, decision making, discipline, adaptation to the work environment, work safety and initiative. The results of the calculation process from the ranking combination of the weighted aggregate sum product assessment and the borda method were obtained by placing alternative 5 as an employee who is worthy of being a permanent employee and getting the highest score of 0.288. All calculations using this method can be a guide in making decisions at PT SMART Tbk Surabaya, because the accuracy of contract employee performance evaluations has a significant influence on achieving company goals in the permanent employee recruitment process.

A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design

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.

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

Circular supplier selection in the construction industry: A sustainability perspective for the emerging economies

The paper illustrates the application of three Multiple-Criteria Decision-Making (MCDM) methods to the problem of the selection of a new hub airport for a hypothetical European Union (EU) airline assumed to operate within the EU liberalised air transport market. The three MCDM methods used are SAW (Simple Additive Weighting), TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) and AHP (Analytic Hierarchy Process), and they are applied to a preselected set of alternative airports. The attributes (criteria) are defined to express the performance of particular alternatives (airports) relevant for a Decision-Maker (DM), in this case the EU airline in question. In addition to illustrating the three methods, this application of three different MCDM methods is intended to lead to a preliminary judgment about their usefulness as supplementary decision-making tools for eventual practical use. The example in which seven preselected European airports are ranked according to nine performance criteria, indicates that all three methods, if applied to the same problem and using the same method for determining the importance of the different criteria, produce the same results

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.

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.

In this paper, we cluster a unlabeled human wrist pulse signal data set via a multiple criteria decision making (MCDM) framework to mine useful information for further study. First, a preprocessing scheme is performed and spatial features are extracted to represent a pulse signal. Then, a list of clustering algorithms are initialized to generate a number of clustering alternatives. The goodness of these clustering alternatives are sequentially comprehensively evaluated by 11 criteria, including ten internal cluster validation indices and an ad-hoc index, the robustness to noise, which is proposed for assessing the clustering alternatives of the pulse data set with spatial features. Taking the evaluation results as inputs, the technique for order preference by similarity to ideal solution (TOPSIS) method is employed to solve the resulting MCDM model. According to the TOPSIS rank, clustering the data set into thirteen clusters via k-means is optimal. Samples drawn from each cluster have similar patterns, corresponding to specific pulse type in traditional Chinese pulse diagnosis. The thirteen clusters are segregated into two groups, namely the healthy and the unhealthy, which can be further applied for unhealthy pulse detection.

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

The technique for order preference by similarity to ideal solution (TOPSIS) has become a popular multi-criteria decision making (MCDM) technique, since it has a comprehensible theoretical structure and is able to provide an exact model for decision making. For the use of TOPSIS in group decisions, the common approaches in aggregating individual decision makers’ judgments are the geometric and the arithmetic mean methods, although these are too intuitive and do not consider either preference levels or preference priorities among alternatives for individual decision makers. In this paper, a TOPSIS group decision aggregation model is proposed in which the construction consists of three stages: (1) The weight differences are calculated first as the degrees of preferences among different alternatives for each decision maker; (2) The alternative priorities are then derived, and the highest one can be denoted as the degree to which a decision maker wants his most favorite alternative to be chosen; (3) The group ideal solutions approach in TOPSIS is used for the aggregation of similarities obtained from different decision makers. A comparative analysis is performed, and the proposed aggregation model seems to be more satisfactory than the traditional aggregation model for solving compromise-oriented decision problems.

Flooding is one of the major natural catastrophic disasters that causes massive environmental and socioeconomic destruction. The magnitude of losses due to floods has prompted researchers to focus more on robust and comprehensive modeling approachesfor alleviating flood damages. Recently developed multi-criteria decision making (MCDM) methods are being widely used to construct decision-making process more participatory, rational, and efficient. In this study, two statistical MCDM approaches, namely the analytical hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS), have been employed to generate flood risk maps together with hazard and vulnerability maps in a GIS framework for Navsari city in Gujarat, India, to identify the vulnerable areas that are more susceptible to inundation during floods. The study area was divided into 10 sub areas (i.e., NC1 to NC10) to appraise the degree of flood hazard, vulnerability and risk intensities in terms of areal coverage and categorized under 5 intensity classes, viz., very low, low, moderate, high, and very high. A total of 14 flood indicators, seven each for hazard (i.e., elevation, slope, drainage density, distance to river, rainfall, soil, and flow accumulation) and vulnerability (i.e., population density, female population, land use, road network density, household, distance to hospital, and literacy rate) were considered for evaluating the flood risk. Flood risk coverage evaluated from the two approaches were compared with the flood extent computed from the actual flood data collected at 36 random locations. Results revealed that the TOPSIS approach estimated more precise flood risk coverage than the AHP approach, yielding high R2 values, i.e., 0.78 to 0.95 and low RMSE values, i.e., 0.95 to 0.43, for all the 5 risk intensity classes. The sub areas identified under "very high" and "high" risk intensity classes (i.e., NC1, NC4, NC6, NC7, NC8, and NC10) call for immediate flood control measures with a view to palliate the extent of flood risk and consequential damages. The study demonstrates the potential of AHP and TOPSIS integrated with GIS towards precise identification of flood-prone areas for devising effective flood management strategies.