Quality Measurement Model Research Articles

QUALIMETRIC MODEL OF A HIGH-TECH INFORMATION AND MEASURING SYSTEM FOR THE PREPARATION AND CONDUCT OF TESTS

The issue of the report concerns the selection of information and testing equipment or a system that will be used for planning and conducting tests with minimal error and high efficiency. The complexity of consideration as a whole, when choosing the components of the information and measurement system and determining the qualitative measurement model determines the need to divide it into several stages and consider them separately. The stages of building a qualitative model of a high-tech information and measurement system for preparing and conducting tests are divided into: modeling stage; design stage; stage of implementation; interpretation stage. At the stage of determining the limitations, it should be taken into account in which division the tests are carried out, namely: by purpose; by the level of testing; by stages of product development; by testing finished products; according to the conditions and venue; by the duration of the tests; by type of action; by the result of the action; according to the characteristics of the object. The relevance of the work is the consideration of the stages of building a qualitative model of the information and measurement system in the difficult conditions of the military and political situation in Ukraine. The purpose of the work is to develop the technological aspects of qualitative modeling, as well as the stages of qualitative model examination. Additional results of the work can be – to obtain initial data (parameters); – to break down important indicators (parameters) according to their importance using an expert method; – to assess state and foreign requirements for the system for further work; – to assess and choose a direction based on the price indicator (factor) and duration of work or other indicators; – provide recommendations for making a decision regarding the construction of a qualitative model of the properties of the information and measurement system;–allow to improve (apply) the process of conducting tests of high-tech weapons and military equipment; – allow to use qualitative model databases to reduce preparation time to conducting tests of high-tech weapons and military equipment;–will allow to expand the directions for finding new features and providing recommendations in the further process of tests. The article builds a qualitative model (hereafter KM) of the formation (creation) of KM stages of the information and measurement system (hereafter IMS) at the preparatory stage of OVT tests. Stages of KM examination have been developed based on the current purpose of the tests, the required quantity and quality of recorded and measured indicators, the complexity of measurements and their analysis. Formulated KM efficiency indicators are as follows: minimal error; cost of equipment; equipment resource; others. The stages of selection of IBS components have been developed and the KM of assessment at the preparatory stage of tests has been determined. A model of quantitative quality measurement has been developed, taking into account possible errors and the provided, structural, technological aspects and stages of qualitative modeling. The most expedient tool for a systematic approach to decision-making during the construction of the IBS CM has been determined. The results of a similar review of literary sources of domestic and foreign scientists are presented. However, the insufficient development of the scientific basis for creating KM efficiency indicators is noted. The normative and real vectors of the creation of the CM are determined, which are laid down as important indicators of the effectiveness of the CM, by changing which it is possible to change the direction of the selection of the creation of the CM of the IAS.

Examining Service Quality in Ghanaian Higher Education: A Comparative Analysis of Private and Public Universities

Little is known about how students perceive the quality of service provided by Ghanaian tertiary institutions and how this perception influences their enrolment choices. Using the Higher Education Quality (HiEdQUAL) model for service quality measurement, this study examined service quality across five key dimensions; teaching and course content, administrative services, academic facilities, campus infrastructure, and support services in Ghanaian private and public universities. A structured questionnaire based on HiEdQUAL model consisting of 27 items with five dimensions, measured on a five-point Likert-Scale was used to gather data. Out of the initial 2,266 sampled respondents surveyed from private and public universities, a total of 1,758 correctly completed questionnaires were returned. This gave a high response rate of 76.43%. The paired t-test results that examined the equality of means between students’ perceptions of service quality at private and public universities across five dimensions, helped to determine the presence of statistically significant differences in some areas. The results suggested that public universities may be making better use of their resources or benefitting from their large numbers of enrollment and state financial support. Given that the quality of service provided by both private and public universities in Ghana falls short of students’ expectations, the paper provides administrators with practical insights to improve service quality in Ghanaian universities and suggests the need for continuous quality improvement in institutions of higher learning in Ghana. Keywords: HiEdQUAL, Service Quality, Higher Education, Public Universities, Private Universities.

Digital Service Quality Measurement Model Proposal and Prototype Development

Traditional service quality models, which are survey-based methods, have been noted by researchers to contain operational errors in their application. Researchers criticize service quality models such as SERVQUAL and SERVPERF for containing operational errors, high implementation costs, and the issue of response recall. Additionally, these models face difficulties when applied to different sectors, as they were developed for the retail industry. The adaptation of the model, data collection, and processing have become outdated in comparison to current information processing technologies. With the rise in the use of social media, new communication paradigms have emerged. In this new paradigm, direct communication is established between people and institutions through social media. Institutions analyze social media data using text mining and sentiment analysis methods to keep up with this change. There are studies in the literature proposing new methods for measuring service quality by separately using text mining and sentiment analysis techniques. In this study, these two techniques have been combined. It is believed that combining these two techniques will result in a more robust service quality measurement model. Additionally, an application has been developed to demonstrate the functionality of the model. A municipality was specifically chosen as the application area because social media allows for fast, efficient, and inclusive participation between citizens and the municipality. The proposed model will enable the better identification of service quality deficiencies, leading to a more efficient use of municipal resources and fostering a more sustainable understanding of the municipality. With the implementation of the model, 463,886 tweets sent to the @ankarabbld and @mavimasa accounts were analyzed to identify 10 service quality dimensions and 106 keywords representing these dimensions, which would reveal the municipality’s service quality. The sentiment analysis technique was applied to 187,084 tweets containing the identified keywords. Thus, an attempt was made to uncover the municipality’s service quality.

The Impact of Training Service Quality on Postgraduate Student Satisfaction: a Case Study of a Public University from Vietnam

Purpose: This study examines important characteristics that affect student satisfaction with the graduate training services of the University of Commerce. Theoretical framework: Research models are developed based on service quality measurement models based on customers' desires and perceptions, such as models using the SERVQUAL scale. Research method: the author uses qualitative methods combined with quantitative methods using a non-probability stratified sampling survey method to analyze 216 postgraduate students of the University of Commerce. Results and discussion: According to research results, while most components of the University of Commerce's graduate training services are highly appreciated by students, other elements need to be maintained and improved. more good. Research results have identified a key factor (Trust & Quality of the training program) that affects the satisfaction of graduate students at the University of Commerce. Research Implications: The research results serve as a foundation for making a variety of proposals and recommendations to support the long-term development of postgraduate training services in emerging countries. Originality/value: Research results have identified a key factor: Trust & Quality of the training program that has the greatest impact on the satisfaction of graduate students at the University of Commerce.

K-MEANS AND AGGLOMERATIVE HIERARCHY CLUSTERING ANALYSIS ON THE STAINLESS STEEL CORROSION PROBLEM

Stainless Steel (SS) is a material that is widely used in various fields because it is resistant to corrosion. However, if SS is exposed to heat at high temperatures for a long period of time, a sigma phase, namely the Fe-Cr compound, will form, which indicates that corrosion has begun. The appearance of this corrosion can be detected through color changes on the SS surface, ranging from light brown to dark blue. Corrosion events will be observed through the distribution of color on the sample surface at the location selected through the SS microstructure image. Cluster analysis will be used to group the colors on the surface of the SS sample through the images used. The results of cluster analysis can be used to identify SS color which indicates the appearance of corrosion in the sample. In this research, we will examine the determination of many clusters for K-Means and Agglomerative Hierarchy with Ward's Criterion, Single, Average, and Complete Linkages. In addition, the model quality measure was tested with Silhouette Coeficient. Single linkage gives the worst results because it gives the impression that only one dominant color appears so it can be said that it is unable to distribute each color to the specified cluster. Likewise with Average because the number of clusters cannot be determined with certainty. On the other hand, the K-Means results are similar to Ward's results, this is reasonable because the basic idea of both is to find the minimum distance between each object and its center, in this case the average is used as the measure of the center, while the results that are most similar to the original image are clustering uses complete linkage. These results can be used as recommendations for academics and practitioners in the fields of Statistics, Mathematics and Materials Engineering in the subsequent analysis process to solve SS corrosion problems.

Robust ambiguity datum for real-time GNSS satellite integer clock estimation

With the capability for single-receiver AR (Ambiguity Resolution), real-time GNSS (Global Navigation Satellite System) satellite integer clock has been the research hotspot of GNSS community in the past decade. Robust ambiguity datum is one prerequisite for high-quality integer clock. In this paper, with undifferenced AR model derived, a robust ambiguity datum definition method is proposed to isolate the signal biases from the integer ambiguity. Firstly, based on the ambiguity graph, the ambiguity datum is selected with the minimum spanning tree algorithm. Secondly, the ambiguity datum is transferred to the subsequent epochs through signal bias connection. Moreover, additional datum ambiguities are introduced to accommodate the arising of receiver and satellite in real-time circumstance. With the combination of ambiguity graph and signal bias, the efficient definition and smooth transition of ambiguity datum are assured for the real-time processing. To validate the approach, simulated real-time integer clock estimation for GPS/Galileo/BDS-3 satellites are conducted with one-month (January 2022) data from about 100 global IGS (International GNSS Service) network stations. Experiments show that the proposed robust ambiguity datum can produce accurate real-time integer clock. The average WL (Wide-Lane) and NL (Narrow-Lane) fixed rates exceed 99 % and 95 %, respectively, for most of the GPS/Galileo satellites. While comparable WL fixed rate is achieved for the BDS-3 satellites, the NL fixed rate is about 5 % lower as a result of poor orbit quality and imperfect measurement model. With eclipsing satellites excluded, the standard deviations between real-time integer clock and post-processed reference clock reach 0.018 ns and 0.016 ns for the GPS and Galileo constellations, respectively, which are 28 % and 24 % smaller than those of corresponding float clocks. In contrast, the standard deviations of float and integer clocks are similar for the BDS-3 constellation. Further improvements on the background models, including unconservative force modeling, satellite attitude modeling and antenna calibration are required to achieve ultimate accuracy, especially for the BDS-3 satellites.

Strengthening Quality Measurement to Predict Success for Total Knee Arthroplasty: Results from a Nationally Representative Total Knee Arthroplasty Cohort.

When performed well on appropriate patients, total knee arthroplasty (TKA) can dramatically improve quality of life. Patient-reported outcome measures (PROMs) are increasingly used to measure outcome following TKA. Accurate prediction of improvement in PROMs after TKA potentially plays an important role in judging the surgical quality of the health-care institutions as well as informing preoperative shared decision-making. Starting in 2027, the U.S. Centers for Medicare & Medicaid Services (CMS) will begin mandating PROM reporting to assess the quality of TKAs. Using data from a national cohort of patients undergoing primary unilateral TKA, we developed an original model that closely followed a CMS-proposed measure to predict success, defined as achieving substantial clinical benefit, specifically at least a 20-point improvement on the Knee injury and Osteoarthritis Outcome Score, Joint Arthroplasty (KOOS, JR) at 1 year, and an enhanced model with just 1 additional predictor: the baseline KOOS, JR. We evaluated each model's performance using the area under the receiver operator characteristic curve (AUC) and the ratio of observed to expected (model-predicted) outcomes (O:E ratio). We studied 5,958 patients with a mean age of 67 years; 63% were women, 93% were White, and 87% were overweight or obese. Adding the baseline KOOS, JR improved the AUC from 0.58 to 0.73. Ninety-four percent of those in the top decile of predicted probability of success under the enhanced model achieved success, compared with 34% in its bottom decile. Analogous numbers for the original model were less discriminating: 77% compared with 57%. Only the enhanced model predicted success accurately across the spectrum of baseline scores. The findings were virtually identical when we replicated these analyses on only patients ≥65 years of age. Adding a baseline knee-specific PROM score to a quality measurement model in a nationally representative cohort dramatically improved its predictive power, eliminating ceiling and floor effects and mispredictions for readily identifiable patient subgroups. The enhanced model neither favors nor discourages care for those with greater knee dysfunction and requires no new data collection. Prognostic Level II . See Instructions for Authors for a complete description of levels of evidence.

RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality.

RNA is a complex macromolecule that plays central roles in the cell. While it is well known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

Exploring accessibility and service quality perceptions on local public transportation in Thailand

This study aimed to analyze the relationship between perceived accessibility of service quality and intention to use public transportation systems. The sample population comprised 400 public transport users, aged 20–60 years. The study was divided into two parts. The first parttested the quality indicator of public transportation services by modifying the SERVQUAL method. Confirmatory factor analysis (CFA) was then conducted and subsequently analyzed for the correlation between perceived accessibility of service quality and intention to use the public transportation system using the structural equation model (SEM). The results showed that the service quality measurement model consisted of 10 components, comprising 40 indicators. The values met the criteria and were suitable for use as indicators of local public transport service quality in Nakhon Ratchasima Province, Thailand. It was found that attitude was the factor that most affected the intention in using public transport, followed by perceived behavioral control and accessibility. Perceived service quality was considered an important variable for perceived accessibility. The study results indicate that service quality has an effect on perceived accessibility in public transportation systems, especially in terms of responsiveness, convenience, and safety. Enhancing the quality of service in these specific areas will heighten user awareness of easily using public transportation services. Furthermore, it is a pivotal factor in promoting better accessibility to public transport services in rural areas.The last part measured the heterogeneity in perceived accessibility and intention between the groups and found significant differences. The obtained results may be beneficial to the government and agencies involved in formulating policies to promote the use of public transportation and its accessibility. Additionally, the service quality indicators should be considered in improving the service quality of public transportation suitable for the area.

Quantitative Evaluation Model of the Quality of Remanufactured Product

Remanufacturing is a significant process for achieving carbon neutrality. However, the existing literature shows that consumer concerns regarding the quality of remanufactured products restrict the large-scale development of the remanufacturing industry. Studies on the quantitative evaluation of the quality of remanufactured products are limited. Therefore, we propose a quantitative evaluation model of the quality of remanufactured products. Extended from the formation process of the quality of remanufactured products, the quality loss function of remanufactured parts and assemblies was constructed based on Taguchi's quality concept. Subsequently, the relationship among social loss, functional limit, and quality loss of remanufactured products was assessed. Accordingly, a quality measurement model of remanufactured products was established. Moreover, the parameter estimation algorithm based on a neural network was evaluated to deal with the dynamic changes and uncertainties of remanufacturing technology and the remanufacturing market. Finally, the application of the proposed model was demonstrated through a case study in which the quality of remanufactured engines was quantitatively evaluated. The relevance of the model was verified through market feedback. This article can provide practitioners and decision-makers with new and effective tools and insights to improve the quality of remanufactured products and reduce the cognitive gaps of customers. It is expected to accelerate the high-quality development of the remanufacturing industry.

BIGOWL4DQ: Ontology-driven approach for Big Data quality meta-modelling, selection and reasoning

Context:Data quality should be at the core of many Artificial Intelligence initiatives from the very first moment in which data is required for a successful analysis. Measurement and evaluation of the level of quality are crucial to determining whether data can be used for the tasks at hand. Conscientious of this importance, industry and academia have proposed several data quality measurements and assessment frameworks over the last two decades. Unfortunately, there is no common and shared vocabulary for data quality terms. Thus, it is difficult and time-consuming to integrate data quality analysis within a (Big) Data workflow for performing Artificial Intelligence tasks. One of the main reasons is that, except for a reduced number of proposals, the presented vocabularies are neither machine-readable nor processable, needing human processing to be incorporated. Objective:This paper proposes a unified data quality measurement and assessment information model. This model can be used in different environments and contexts to describe data quality measurement and evaluation concerns. Method:The model has been developed as an ontology to make it interoperable and machine-readable. For better interoperability and applicability, this ontology, BIGOWL4DQ, has been developed as an extension of a previously developed ontology for describing knowledge management in Big Data analytics. Conclusions:This extended ontology provides a data quality measurement and assessment framework required when designing Artificial Intelligence workflows and integrated reasoning capacities. Thus, BIGOWL4DQ can be used to describe Big Data analysis and assess the data quality before the analysis. Result:Our proposal has been validated with two use cases. First, the semantic proposal has been assessed using an academic use case. And second, a real-world case study within an Artificial Intelligence workflow has been conducted to endorse our work.

KANO Model for experience quality measurement of wine tourism events

Abstract Visiting wine tourism events is an attractive part of a trip for holidaymakers and day visitors. Thus, wine tourism is an important niche market for Germany with its 13 wine regions. Experiences play an increasingly important role in the consumption of tourism services. Therefore, it is also important to organize wine tourism events in an experience-oriented way. The present research design uses the KANO model to determine the requirements for experience staging at wine events from the customer’s point of view. The aim of the study is to provide wineries and other wine tourism providers with recommendations for experience staging in order to increase the attractiveness of the events and to improve the economic success.

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

SQMetrics: An Educational Software Quality Assessment Tool for Java

Over the years, various software quality measurement models have been proposed and used in academia and the software industry to assess the quality of produced code and to obtain guidelines for its improvement. In this article, we describe the design and functionality of SQMetrics, a tool for calculating object-oriented quality metrics for projects written in Java. SQMetrics provides the convenience of measuring small code, mainly covering academic or research needs. In this context, the application can be used by students of software engineering courses to make measurements and comparisons in their projects and gradually increase their quality by improving the calculated metrics. Teachers, on the other hand, can use SQMetrics to evaluate students’ Java projects and grade them in proportion to their quality. The contribution of the proposed tool is three-fold, as it has been: (a) tested for its completeness and functionality by comparing it with widely known similar tools, (b) evaluated for its usability and value as a learning aid by students, and (c) statistically tested for its value as a teachers’ aid assisting in the evaluation of student projects. Our findings verify SQMetrics’ effectiveness in helping software engineering students learn critical concepts and improve the quality of their code, as well as in helping teachers assess the quality of students’ Java projects and make more informed grading decisions.

Duygu Analizi ve Metin Madenciliği ile Hizmet Kalitesi Ölçüm Modeli Önerisi: Su ve Kanalizasyon Hizmetleri Örneği

This study proposes a new model for service quality measurement using sentiment analysis and text mining techniques. This model aims to overcome traditional methods' time, cost and implementation difficulties and provide a more dynamic and efficient approach to service quality measurement. In addition, in this model, instead of the dimensions used in service quality measurements, such as SERVQUAL or SERVPERF, it is shown how to determine new categories and keywords specific to the service sector in which the model is used by text mining. Thus, it is aimed at something other than reaching more accurate results in service quality measurement. To achieve the model’s purpose, it aims to develop a service quality measurement model using social media data processed by text mining and sentiment analysis. To find an answer to this question, the keywords "flood", "meter", "rain", "irrigation", "infrastructure", "sewerage", "sewage", "maintenance hole ", "aski", "waterless", "water" were extracted from 109.844 tweets sent to the Twitter account of a municipality between 2016 and 2022 by text mining method. Service quality was measured by subjecting 5766 tweets containing the keywords extracted to sentiment analysis. As a result of the service quality measurement, 1922 negative, 973 positive and 2871 neutral tweets were identified. The average negative score was 0.51, the average positive score was 0.11, and the average neutral score was 0.38.

Perceived service quality and student satisfaction in higher learning institutions in Tanzania

Despite policy efforts to promote higher learning in Tanzania, reports show persistent student dissatisfaction, revealing the extant inadequate quality measurement models. The study examined the fundamental elements causing dissatisfaction using an extended SERVQUAL model with additional variables, perceived transparency mediated by trust. Researchers collected quantitative data from 398 third-year higher learning students. The structural equations modelling result shows that reliability, perceived transparency, and trust in an institution significantly predict satisfaction. Further, trust partially mediates the influence of perceived transparency on student satisfaction. Evidence from this study suggests that education policy geared to promote the expertise of service providers and punctuality of service offering, transparency in service offering, and social responsibility of service provision is adequate for student satisfaction. Future research can look into a cross-level of economic development, groups of students—analysis of satisfaction determinants, and test the transparency—trust-based SERVIQUAL Model in quality struggling sectors in Tanzania and other developing countries. Also, studies can test how satisfaction mediates the effect of quality on academic performance.

Evaluating Gastronomic Destination Competitiveness through Upscale Gastronomy

Food has evolved from a basic necessity to a primary motivation for travel. While providing a high-quality gastronomic offer is still a restaurant’s primary function, quality is no longer enough; today’s demanding guests seek unique and memorable dining experiences. A restaurant’s competences play a significant role in shaping the guests’ overall perception of the quality and derived perceived value, both of which ultimately impact the competitiveness of both the restaurant and the gastronomic destination. The main contribution of this article is the development of a novel theoretical model—GADECOMP—which is designed to measure a gastronomic destination’s competitiveness by centering it around the destination’s gastronomic offer. Exploratory factor analyses confirmed that the instrument is reliable and valid. The model was tested on upscale restaurant guests in Slovenia, Italy, and Croatia. Additionally, the SERVQUAL model of quality measurement was transferred from the entrepreneurial level to the destination level. Our method uses a unique competence approach and expands and diversifies the existing quality concepts by using elements of innovativeness, creativity, sustainability, and local features. The GADECOMP model measures the perceived quality of a gastronomic destination through its competences, which are reflected in the gastronomic destination offer. A regression analysis was used to identify the strength of the effect that perceived value has on the gastronomic destination offer and gastronomic destination competences.

FACTORS AFFECTING CUSTOMER SATISFACTION ON SERVICE QUALITY AT SAN HA RETAIL GROUP

This study proposes a research model of the factors affecting customer satisfaction with the service quality of San Ha Retail Group. The study proposes a service quality measurement model with five factors and 20 observed variables. The study used the method of testing the reliability of the model scale by Cronbach’s Alpha coefficient, the exploratory factor analysis, and the multivariate regression analysis to explore the results of interviews with 130 customers who have used products and services of San Ha Retail Group. The results show that customer satisfaction with the service quality of San Ha Retail Group is influenced by five factors, including empathy, responsiveness, tangibles, reliability, and service capacity. The findings from the research suggest solutions to increase customer satisfaction when using products and services for San Ha Retail Group.

Evaluating mixed-integer programming models over multiple right-hand sides

A critical measure of model quality for a mixed-integer program (MIP) is the difference, or gap, between its optimal objective value and that of its linear programming relaxation. In some cases, the right-hand side is not known exactly; however, there is no consensus metric for evaluating a MIP model when considering multiple right-hand sides. In this paper, we provide model formulations for the expectation and extrema of absolute and relative MIP gap functions over finite discrete sets.

RILS-ROLS: robust symbolic regression via iterated local search and ordinary least squares

In this paper, we solve the well-known symbolic regression problem that has been intensively studied and has a wide range of applications. To solve it, we propose an efficient metaheuristic-based approach, called RILS-ROLS. RILS-ROLS is based on the following two elements: (i) iterated local search, which is the method backbone, mainly solving combinatorial and some continuous aspects of the problem; (ii) ordinary least squares method, which focuses on the continuous aspect of the search space—it efficiently determines the best—fitting coefficients of linear combinations within solution equations. In addition, we introduce a novel fitness function that combines important model quality measures: R^2 score, RMSE score, size of the model (or model complexity), and carefully designed local search, which allows systematic search in proximity to candidate solution. Experiments are conducted on the two well-known ground-truth benchmark sets from literature: Feynman and Strogatz. RILS-ROLS was compared to 14 other competitors from the literature. Our method outperformed all 14 competitors with respect to the symbolic solution rate under varying levels of noise. We observed the robustness of the method with respect to noise, as the symbolic solution rate decreases relatively slowly with increasing noise. Statistical analysis of the obtained experimental results confirmed that RILS-ROLS is a new state-of-the-art method for solving the problem of symbolic regression on datasets whose target variable is modelled as a closed-form equation with allowed operators. In addition to evaluation on known ground-truth datasets, we introduced a new randomly generated set of problem instances. The goal of this set of instances was to test the sensitivity of our method with respect to incremental equation sizes under different levels of noise. We have also proposed a parallelized extension of RILS-ROLS that has proven adequate in solving several very large instances with 1 million records and up to 15 input variables.