Simple Ratio Analysis Research Articles

Evaluation of business strategies based on the financial performance of the corporation and investors' behavior using D-CRITIC and fuzzy MULTI-MOORA techniques: A real case study

Revealing the complex relationship between corporation-chosen strategies and financial performance is crucial in navigating today's dynamic business landscape. This research delves into the profound impact of business strategies on both corporation performance and investors' behavior, seeking to identify the strategy that yields the highest returns and efficiency for the corporation and its beneficiaries (investors, loan-holders, and stock-holders). This study proposes a multi-criteria decision-making (MCDM) model that integrates the distance correlation-based criteria importance through inter-criteria correlation (D-CRITIC) weighting method with multi-objective optimization on the basis of ratio analysis plus full multiplicative form (MULTI-MOORA) technique to evaluate the business strategies of corporations (i.e., defenders, analyzers, reactors, and prospectors). Notably, fuzzy MULTI-MOORA methodology is employed to account for uncertainty, addressing vague, imprecise, and ambiguous input and knowledge. This study is implemented on a real case of the Tehran Stock Exchange on a sample of eighty companies from 2017 to 2021. The study's results underscore the medium analyzers strategy as the optimal alternative among various business strategies, assuming the maximization of benefits for all beneficiaries, and simultaneously specify a prevalent preference for analyzer strategies within Iran's economic landscape. To validate the proposed method, the findings are compared to the other MCDM techniques such as technique for order performance by similarity to ideal solution (TOPSIS), multi-objective optimization on the basis of simple ratio analysis (MOOSRA), simple additive weighting (SAW), viekriterijumsko kompromisno rangiranje (VIKOR), and Taxonomy. Additionally, applying principal component analysis (PCA) as a visualization approach demonstrates the satisfactory performance of the proposed framework. Beyond its scientific contributions, this research holds practical managerial applicability, providing actionable insights for informed decision-making across various fields.

An integrated group decision making framework for business performance evaluation of performing arts enterprises with probabilistic hesitant fuzzy IDOCRIW-MOOSRA method

There is a lack of domestic and foreign research on the evaluation and improvement strategies of business performance of performing arts enterprises, especially in the context of the “restructuring” of cultural groups in China. Most of the existing studies are distributed in bulk, not only lacking in theoretical depth, but also lacking in systematization to a certain extent, which shows that the existing studies have not fully formed a mature and valuable theoretical system. The business performance evaluation of performing arts enterprises is a multiple attributes group decision making (MAGDM). This paper constructs a novel probabilistic hesitant fuzzy Multi-Objective Optimization Simple Ratio Analysis (PHF-MOOSRA) model based on the integrated determination of objective criteria weights (IDOCRIW) under the probabilistic hesitant fuzzy sets (PHFSs) for this issue. The PHFSs provides an evaluation circumstance containing more information which make the final decision-making results more accurately. Additionally, the IDOCRIW method separately and the MOOSRA method based on the MOORA method is proposed in PHFSs circumstance in this model. In the end, this model is then applied in a numerical case study for business performance evaluation of performing arts enterprises and compare this model with other existing methods.

Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk

Flood risk assessment is a key step in flood management and mitigation, and flood risk maps provide a quantitative measure of flood risk. Therefore, integration of deep learning – an updated version of machine learning techniques – and multi-criteria decision making (MCDM) models can generate high-resolution flood risk maps. In this study, a novel integrated approach has been developed based on multiplicative long short-term memory (mLSTM) deep learning models and an MCDM ensemble model to map flood risk in the Minab-Shamil plain, southern Iran. A flood hazard map generated by the mLSTM model is based on nine critical features selected by GrootCV (distance to the river, vegetation cover, variables extracted from DEM (digital elevation model) and river density) and a flood inventory map (70% and 30% data were randomly selected as training and test datasets, respectively). The values of all criteria used to assess model accuracy performance (except Cohens kappa for train dataset = 86, and for test dataset = 84) achieved values greater than 90, which indicates that the mLSTM model performed very well for the generation of a spatial flood hazard map. According to the spatial flood hazard map produced by mLSTM, the very low, low, moderate, high and very high classes cover 26%, 35.3%, 20.5%, 11.2% and 7% of the total area, respectively. Flood vulnerability maps were produced by the combinative distance-based assessment (CODAS), the evaluation based on distance from average solution (EDAS), and the multi-objective optimization on the basis of simple ratio analysis (MOOSRA), and then validated by Spearman's rank correlation coefficients (SRC). Based on the SRC, the three models CODAS, EDAS, and MOOSRA showed high-ranking correlations with each other, and all three models were then used in the ensemble process. According to the CODAS-EDAS-MOOSRA ensemble model, 21.5%, 34.2%, 23.7%, 13%, and 7.6% of the total area were classified as having a very low to very high flood vulnerability, respectively. Finally, a flood risk map was generated by the combination of flood hazard and vulnerability maps produced by the mLSTM and MCDM ensemble model. According to the flood risk map, 27.4%, 34.3%, 14.8%, 15.7%, and 7.8% of the total area were classified as having a very low, low, moderate, high, and very high flood risk, respectively. Overall, the integration of mLSTM and the MCDM ensemble is a promising tool for generating precise flood risk maps and provides a useful reference for flood risk management.

The Effect of Forensic Audit Services on Tax Fraud in South-South, Nigeria

The need to address tax fraud has increasingly been attracted to the state authorities’ administrators, decision-makers, scholars, and investigators in Nigeria. Although various efforts have been made to alleviate it for effective revenue generation, mobilization through taxation is still low. Consequently, this study examined Forensic Audit Services and Tax Fraud in South-South Nigeria. To this end, a cross-sectional research design with a survey research strategy was used. Copies of questionnaire, reflecting the research questions, were distributed to a sample size of 228 staff in the Nigerian Federal Inland Revenue Service in the South-South States with a target population (of 530) and a sampling error of 5% at a 95% confidence interval. To assess the study's hypotheses, the Robust Least Squares Estimation technique was used. Findings revealed that Forensic Audit Investigation Services disaggregated into Background Investigation, Investigative Interview, and Analytical Procedures exert a negative and significant effect on Tax Fraud. It was also revealed that the explanatory power of Litigation Support Services in the form of Pre-trial Support and Expert Witnessing negatively and significantly affected Tax Fraud. The implications of these findings suggest that forensic audit services mitigate the occurrence of tax fraud in Nigeria, thereby improving compliance and tax revenue generation. On the premise of the foregoing, we recommend that tax investigating agencies, such as the federal Inland Revenue Service and its counterparts in the states (all in Nigeria) should employ background investigation techniques including surveillance, undercover operations and database searches as a routine procedure to proactively search for indicators of fraud. Besides, tax officials should be well trained on the usefulness and application of analytical procedures, ranging from simple ratio analysis, data mining techniques, Bedford’s Law, and Beneish model during an investigation, to help in their audit efficiency.

Penerapan Metode MOOSRA Dalam Seleksi Pengantaran Pemesanan Kue Online Menggunakan Pembobotan ROC

Each cake shop certainly has its own website that can be used to order cakes and of course has its own advantages and disadvantages which can be used as an assessment of the quality of the application, such as the types of cakes provided vary at more affordable prices and even the existence of discounted prices makes consumers more interested in ordering cakes online. The large number of these applications makes service users confused about choosing a better application among the good ones, therefore a study was made that discussed techniques for solving these problems using a decision support system. In this study, there are two methods used in the SPK method, including the ROC method which is used as an arrangement of weights of importance on five features that are used as rules which include application usability, Rating Reviews, Promos (Discounts), Cake Menu and Handling Fees. The second method is the Multiobjective Optimization on the Basis of Simple Ratio Analysis (MOOSRA) method which will be used to select 8 online cake ordering applications by ranking each alternative based on predetermined criteria. The final result after applying the two SPK methods is that Akul cake delivery is declared the best online cake delivery application with a preference value of 38.012.

Penerapan Metode MOOSRA dan ROC dalam Penentuan Guru Terbaik

The best teacher is a teacher who is given appreciation because he has a high achievement value. There are several specific criteria in selecting the best teacher, namely in terms of achievement, skills possessed, morals/manners, and discipline. To improve quality in the world of education, of course, a teacher is needed who will interact directly with students in the learning process which can indirectly improve the quality of education. With the interaction in the learning process, it is hoped that it will become a resource in improving the quality of education. There is also something the government is doing that is in a way to advance education, namely by empowering teachers and giving an award, especially for those who are entitled to get the title of the best teacher with achievements. Based on the results of interviews and research related to SMK Negeri 2 Medan, in determining the best teacher the assessment is still in a subjective way. This is based only on the opinion of each teacher and does not start with certain criteria, this will affect social events because the method used is not in an objective way and ends in an unfair feeling caused by subjective methods among fellow teachers who teach at the school. Therefore, it is recommended that schools must determine and agree on certain criteria in determining outstanding teachers, even though the assessment in the final process is still carried out by voting from those who have voting rights in determining the outstanding/best teachers, which of course is also carried out by teachers. The system for determining the best teacher is always a problem every year because it can create injustice among fellow teachers. Therefore, to facilitate and eliminate this unfair attitude, a decision support system (SPK) is needed which can facilitate the determination of the best teacher at SMK Negeri 2 Medan. A decision support system is an interactive computer system that can assist decision making by utilizing existing data to solve unstructured and semi-structured problems. In this SPK there are several methods, one of which is the Moosra Method (Multiobjective Optimization on the Basis of Simple Ratio Analysis) and the ROC (Rank Order Centroid) Method. The result of ROC and MOOSRA method calculation showed that Halimah (A10) obtained the highest final preference value of 14.599, making her selected as the best teacher.

Penerapan Metode Multi-Objective Optimization on the Basis of Simple Ratio Analysis Dalam Seleksi Pengangkatan Karyawan Tetap

Improvement of status as a permanent employee is a form of career advancement for a contract employee. because permanent employees are the main asset for a company. In selecting permanent employees for a company, there are some employees who volunteer to take part in the selection to become permanent employees. permanent employee selection is carried out in stages such as file selection, oral or written tests and interviews. Usually the selected employees are sometimes far from what is expected, so it is necessary to use a decision-making system as the final result, so a decision support system is needed using the Multi-Objective Optimization on the Basis of Simple Ratio Analysis (MOOSRA) method. The application of the Multi-Objective Optimization on the Basis of Simple Ratio Analysis (MOOSRA) method used in the selection of permanent employees is to find the best employees in a company. Decision support systems are used to assist decision making to determine permanent employees. The results of this study can assist decision making in determining which employees are selected to become permanent employees. From the results of the calculation of the Multi-Objective Optimization on the Basis of Simple Ratio Analysis (MOOSRA) method, the selection of permanent employees is based on predetermined criteria, an employee named "Eben" alternative A7 with a preference value of 7.086 as an employee who is eligible to become a permanent employee.

Distinguishing Drones from Birds in a UAV Searching Laser Scanner Based on Echo Depolarization Measurement.

Widespread availability of drones is associated with many new fascinating possibilities, which were reserved in the past for few. Unfortunately, this technology also has many negative consequences related to illegal activities (surveillance, smuggling). For this reason, particularly sensitive areas should be equipped with sensors capable of detecting the presence of even miniature drones from as far away as possible. A few techniques currently exist in this field; however, all have significant drawbacks. This study addresses a novel approach for small (<5 kg) drones detection technique based on a laser scanning and a method to discriminate UAVs from birds. The latter challenge is fundamental in minimizing the false alarm rate in each drone monitoring equipment. The paper describes the developed sensor and its performance in terms of drone vs. bird discrimination. The idea is based on simple cross-polarization ratio analysis of the optical echo received as a result of laser backscattering on the detected object. The obtained experimental results show that the proposed method does not always guarantee 100 percent discrimination efficiency, but provides certain confidence level distribution. Nevertheless, due to the hardware simplicity, this approach seems to be a valuable addition to the developed anti-drone laser scanner.

A novel aggregated multiple criteria decision‐making approach to evaluate the physical workload of workers

AbstractPhysical workload (PWL) is a measure of the physical resources consumed by a worker while performing a task. Analyzing the PWL of workers can aid experts in identifying workers whose working conditions should be improved. Identifying workers who are exposed to the highest PWL is essential for task continuity and occupational health. In this study, a new approach is proposed to prioritize workers according to their PWL levels. In the proposed approach, multiple criteria decision‐making techniques are used because different factors can affect PWL levels. Eight criteria, that is, standard time, frequency of working posture, heart rate, the risk level of posture, noise level, lighting intensity, rest period, and perceived workload level are considered. In the evaluation process, criteria weights are computed using entropy considering measurements of the eight criteria for each worker. Using these weights in multiobjective optimization by simple ratio analysis, multiobjective optimization by ratio analysis, and multiattributive ideal‐real comparative analysis methods, workers are ranked according to their PWL levels. The final order of workers is determined by aggregating different rankings obtained from these three methods, using the technique of precise order preference method. This study presents original research that can potentially contribute to the literature by considering different perspectives offered by various methods for PWL evaluation. It focuses on several different criteria by performing real measurements and subsequently ranking workers based on the aggregation of these measurements.

Evaluation of innovative management success criteria in hotel establishments: Case study in Giresun-Turkey

The purpose of this study was to determine the success criteria for innovative management in hotel establishments that have a corporate identity in the Turkish city of Giresun as well as to find out the best hotel by ranking them based on the determined criteria. For the study, “Multi-objective Optimization by Simple Ratio Analysis (MOOSRA)” and “Evaluation Based on Distance from Average Solution (EDAS)” methods were used. The results revealed that the success criteria for innovative management are beneficial in increasing operational efficiency, performance, and customer satisfaction. The results also show that managers are well aware of the success criteria for innovative management that should be prioritized, as well as those that are likely to lead to the success of their establishments and keep them ahead of the competition. While “Presenting an Innovative Vision” was found as the most important success criteria, “Use of In-Hotel Information Sources” came out as the least important one. The hotels considered were then ranked following the identified criteria.

FUCOM-MOORA and FUCOM-MOOSRA: new MCDM-based knowledge-driven procedures for mineral potential mapping in greenfields

In this study, we present the application of two novel hybrid multiple-criteria decision-making (MCDM) techniques in the mineral potential mapping (MPM), namely FUCOM-MOORA and FUCOM-MOOSRA, as robust computational frameworks for MPM. These were applied to a set of exploration targeting criteria of skarn. The multi-objective optimization method on the basis of ratio analysis (MOORA) and the multi-objective optimization on the basis of simple ratio analysis (MOOSRA) approaches are used to prioritize and rank individual cells. What makes MOORA and MOOSRA more reliable compared to many other methods is the fact that the optimizations procedure is applied to calculate the prospectivity score of individual unit cells. This reduces the uncertainty stemming from erroneous mathematical calculations. The full consistency method (FUCOM), on the other hand, is useful for assigning weights to the spatial proxies. The FUCOM method, as a pairwise comparison method, reduces a large number of pairwise comparisons of similar and popular approaches such as analytic hierarchy process (AHP) with nleft( {n - 1} right)/2 and the best–worst method (BWM) with 2n - 3 number of pairwise comparisons with n - 1 which leads to a less time-consuming and more consistent performance compared with AHP and BWM. These were applied to a set of exploration targeting criteria of skarn iron deposits from Central Iran. Two potential maps were retrieved from the procedures applied, the comparison of which using correct classification rates and field checks revealed the superiority of FUCOM-MOOSRA over the FUCOM-MOORA.

ASVTrN-number approach of multi-objective optimisation on the basis of simple ratio analysis based on MCDM method

ASVTrN-number approach of multi-objective optimisation on the basis of simple ratio analysis based on MCDM method

A novel assessment of bio-medical waste disposal methods using integrating weighting approach and hesitant fuzzy MOOSRA

Bio-medical waste (BMW) management is highly important precaution for human health and environmental concern. There are several disposal treatment followed by medical practitioners in medical waste management. Here, a few disposal treatment is considered to be an alternatives. When assessing, it is necessary to evaluate and assume that all disposal treatment methods are safe and hygienic. In this way, every alternative assessment is evaluated based on the social acceptance, technology and operation, environmental protection, cost, noise and health risk. Finally the best alternative is chosen. When BMW is disposed and we select the best treatment method in BMW management, it can lead to multi-criteria decision making (MCDM) processes related to uncertain critical assessments. When making a decision, the decision makers having some hesitation to give their suggestions. Therefore, here we use hesitant MCDM method. In today’s practice we have choose five methods of BMW disposal methods used in the medical world and we have its alternatives. One of these alternative is sorted by six criteria weights for selecting the best method. The main aim of this research paper is propose a new methodology of hesitant fuzzy weight finding technique, it is named as Hesitant Fuzzy Subjective and Objective Weight Integrated Approach (HF-SOWIA) and also propose a new hesitant fuzzy rank finding methodology, it is named as Hesitant Fuzzy Multi-Objective Optimization on the basis of Simple Ratio Analysis (HF-MOOSRA). After evaluation, the result shows that autoclaving is the best alternative for BMW disposal treatment methods. Furthermore, sensitivity analysis is make in order to observe the difference of alternative ranking when the importance of subjective and objective weights changes.

A new approach for ergonomic risk assessment integrating KEMIRA, best–worst and MCDM methods

In this study, a new three-phase ergonomic risk assessment approach was proposed for manual lifting tasks to determine which worker has the highest ergonomic risk level considering two criteria sets as lifting-related criteria and human-related criteria. In the first phase, Modified Kemeny Median Indicator Ranks Accordance (KEMIRA-M) and a novel two-dimensional best–worst method (BWM) integration were proposed for weighting ergonomic risk criteria in two sets. In this way, weighting procedure of KEMIRA-M was advanced by the proposed two-dimensional BWM in a consistent manner and subjectivity in determining the best and the worst criteria in traditional BMW was prevented by using KEMIRA-M. Thus, the weaknesses of both methods have been developed. In the second phase, the rankings of workers were determined via utilizing multi-objective optimization on the basis of simple ratio analysis, multi-objective optimization by ratio analysis (MOORA) ratio, MOORA reference point and complex proportional assessment to see how worker rankings differ despite using the same advanced weighting approach based on KEMIRA-M and two-dimensional BWM integration. Finally, to aggregate these different ranking results, technique of precise order preference was applied. In this way, different viewpoints of each ranking approach can be reflected on a single worker’s priority. The applicability of the proposed ergonomic risk assessment approach was demonstrated with a real application in tube manufacturing.

A Bipolar Neutrosophic Multi Criteria Decision Making Framework for Professional Selection

Professional selection is a significant task for any organization that aims to select the most appropriate candidates to fill well-defined vacancies up. In the recruitment process, various individual characteristics are involved, such as leadership, analytical skills, independent thinking, innovation, stamina and personality, ambiguity and imprecision. It outlines staff contribution and therefore plays a significant part in human resources administration. Additionally, in the era of the Internet of Things and Big Data (IoTBD), professional selection would face several challenges not only to the safe selection and security but also to make wise and prompt decisions especially in the large-scale candidates and criteria from the Cloud. However, the process of professional selection is often led by experience, which contains vague, ambiguous and uncertain decisions. It is therefore necessary to design an efficient decision-making algorithm, which could be further escalated to IoTBD. In this paper, we propose a new hybrid neutrosophic multi criteria decision making (MCDM) framework that employs a collection of neutrosophic analytical network process (ANP), and order preference by similarity to ideal solution (TOPSIS) under bipolar neutrosophic numbers. The MCDM framework is applied for chief executive officer (CEO) selection in a case study at the Elsewedy Electric Group, Egypt. The proposed approach allows us to assemble individual evaluations of the decision makers and therefore perform accurate personnel selection. The outcomes of the proposed method are compared with those of the related works such as weight sum model (WSM), weight product model (WPM), analytical hierarchy process (AHP), multi-objective optimization based on simple ratio analysis (MOORA) and ANP methods to prove and validate the results.

A SVTrN-number approach of multi-objective optimisation on the basis of simple ratio analysis based on MCDM method

An essential process in the study of human work behaviour or huma n resource management is the personnel selection/recruitment and a major challenge of an organization is to determine the most potential personnel. From this perspective, this paper tries to formulate MOOSRA approach in the neutrosophic environment to select potential personnel in organizations. The method proves to reduce complexity in computation and is comprehensible. An illustrative example is shown for better understanding of the proposed method.

Low-k polymer gate dielectric selection for organic thin-film transistors (OTFTs) using material selection methodologies

Performance parameters of an organic thin-film transistor (OTFT) and their relation to the material parameters of the gate dielectric are analyzed. Based on the analysis, the surface energy, dielectric constant, glass-transition temperature, and breakdown field are identified as key parameters. Various multicriteria decision-making approaches, viz. multiobjective optimization using simple ratio analysis (MOOSRA), technique for order preference by similarity to ideal solution (TOPSIS) and Vlsekriterijumska Optimizacija I KOmpromisno Resenje (VIKOR), are used to solve the material selection problem. Various low-k polymers are analyzed, and it is observed that CYTOP is a promising gate dielectric material for OTFTs, with good agreement between the MOOSRA, VIKOR, and TOPSIS regarding this choice.

Knowledge based decision support system for appraisement of sustainable partner under fuzzy cum non-fuzzy information

PurposeThe purpose of this study is to design a DSS for construction sectors, which can determine the status of the related supplier alternatives, accompanying G-T, SC measures and their interrelated metrics. In today’s era, a supplier is observed as significant among entire agents of green supply chain (SC) management. Presently, it is determined that appraising worth of the supplier under green-traditional (G-T), SCs concerns still require the support of novel algorithmic/decision support systems (DSSs), which could embrace potential decision-making.Design/methodology/approachThe authors have proposed a DSS (consisting of the implementation of multi-level multi-criterion decision-making [ML-MCDM], reference point approach [RPA] and multi-objective optimization on the basis of simple ratio analysis [MOOSRA] methods on constructed MCDM supplier evaluation appraisement module) for measuring the performance score of clay-brick suppliers coming under G-T SCs corresponding to fuzzy and non-fuzzy information. A comparative analysis is conducted among the performance scores against alternatives, obtained by the three methods, i.e. ML-MCDM, RPA and MOOSRA, for robustly making a potential decision.FindingsThe presented research offers a DSS toward managers of construction sectors for benchmarking the performance scores against supplier alternatives under G-T SC measures and their interrelated metrics, modeled by fuzzy cum non-fuzzy information.Originality/valuePresented research work exhibited a DSS that can be used by construction sectors for benchmarking the supplier alternatives in accordance with their performance scores under G-T SCs. The MCDM G-T supplier evaluation appraisement module is constructed pertaining to small-scale clay-brick production units, located in the northern part of India to check the effectiveness of the proposed DSS.

Solving Critical Path Problem in Project Network by a New Enhanced Multi-objective Optimization of Simple Ratio Analysis Approach with Interval Type-2 Fuzzy Sets

Decision making is an important issue in business and project management that assists finding the optimal alternative from a number of feasible alternatives. Decision making requires adequate consideration of uncertainty in projects. In this paper, in order to address uncertainty of project environments, interval type-2 fuzzy sets (IT2FSs) are used. In other words, the rating of each alternative and weight of each criterion are expressed by IT2FSs. Moreover, for obtaining weights of criteria, interval type-2 fuzzy analytic hierarchy process (AHP) method is employed. In addition, a new enhanced model of multi-objective optimization on the basis of simple ratio analysis (MOOSRA) method is developed with IT2F-relative preference relation. Finally, to illustrate applicability of the introduced approach, an existing application from literature is adopted and solved.

The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop selection problem

A decision making process requires the values of conflicting objectives for alternatives and the selection of the best alternative according to the needs of decision makers. Multi-objective optimization methods may provide solution for this selection. In this paper it is aimed to present the laptop selection problem based on MOORA plus full multiplicative form (MULTIMOORA) and multi-objective optimization on the basis of simple ratio analysis (MOOSRA) which are relatively new multi-objective optimization methods. The novelty of this paper is solving this problem with the MULTIMOORA and MOOSRA methods for the first time.