Operations Research Methods Research Articles

Time autonomy in personnel planning: Requirements and solution approaches in the context of intelligent scheduling from a holistic organizational perspective

Traditional work models often need more flexibility and time autonomy for employees, especially in manufacturing. Quantitative approaches and Artificial Intelligence (AI) applications offer the potential to improve work design. However, current research does not entirely focus on human-centric criteria that enable time autonomy. This paper addresses this gap by developing a set of criteria to evaluate intelligent personnel planning approaches based on their ability to enhance time autonomy for employees. Existing quantitative approaches are not sufficient to fully integrate the developed criteria.Consequently, a novel model approach is proposed in an attempt to bridge the gap between current practices and the newly developed criteria. This two-stage planning approach fosters democratization of time autonomy on the shopfloor, moving beyond traditional top-down scheduling. The paper concludes by outlining the implementation process and discusses future developments with respect to AI for this model approach.Practical Relevance: In order to make working conditions on the shopfloor in high-wage countries more attractive, an alternative organization of shift work is needed. Intelligent planning approaches that combine traditional operations research methods with artificial intelligence approaches can democratize shift organization regarding time autonomy. Planning that takes both employee and employer preferences into account in a balanced way will strengthen the long-term competitiveness of manufacturing companies in high-wage countries and counteract the shortage of skilled labor.

Regression Machine Learning Models for the Short-Time Prediction of Genetic Algorithm Results in a Vehicle Routing Problem

Machine learning techniques have advanced rapidly, leading to better prediction accuracy within a short computational time. Such advancement encourages various novel applications, including in the field of operations research. This study introduces a novel way to utilize regression machine learning models to predict the objectives of vehicle routing problems that are solved using a genetic algorithm. Previous studies have generally discussed how (1) operations research methods are used independently to generate optimized solutions and (2) machine learning techniques are used independently to predict values from a given dataset. Some studies have discussed the collaborations between operations research and machine learning fields as follows: (1) using machine learning techniques to generate input data for operations research problems, (2) using operations research techniques to optimize the hyper-parameters of machine learning models, and (3) using machine learning to improve the quality of operations research algorithms. This study differs from the types of collaborative studies listed above. This study focuses on the prediction of the objective of the vehicle routing problem directly given the input and output data, without optimizing the problem using operations research algorithms. This study introduces a straightforward framework that captures the input data characteristics for the vehicle routing problem. The proposed framework is applied by generating the input and output data using the genetic algorithm and then using regression machine learning models to predict the obtained objective values. The numerical experiments show that the best models are random forest regression, a generalized linear model with a Poisson distribution, and ridge regression with cross-validation.

Методи обґрунтування запасів запасних частин у системі управління транспортним процесом

The article formulates directions and analyzes the possibilities of substantiating and choosing the most optimal variant of applying mathematical models for forecasting the number and range of spare parts. The provided recommendations allow to take into account the multi-brand fleets of motor transport enterprises and trends for extrapolation in the short term, identified by the results of observations in real operating conditions. The main reasons for the occurrence of prolonged vehicle downtime due to technical malfunctions are considered. Traditional methods of planning spare parts inventories based on the use of reliability theory and results of mileage forecasting, standard time to failure; inventory management theory and statistical methods for forecasting random processes; methods of operations research; economic and mathematical methods; stochastic methods of transformation of random variables are analyzed. The article formalizes the grouping of factors that affect the size of stocks and the range of spare parts in the operation of rolling stock of enterprises, depending on the state of the system of organization of work of services for maintenance and repair of vehicles; structure of the fleet and technical characteristics of vehicles; level of development of the production base; availability, interest and qualification of personnel; operating conditions of automotive equipment. It is proposed to improve the logistics of spare parts inventory management and to formulate measures to improve the performance of carriers based on an assessment of the total costs incurred by the enterprise to maintain the reliability of rolling stock in operation and taking into account the amount of lost profit due to vehicle downtime in the technical service units. At the same time, it is recommended to use the mathematical apparatus of fuzzy set theory for decision-making, which allows formalizing the parameters of vaguely defined expectations of consumers of motor transport services and reducing the subjectivity of decision-making when solving problems of optimizing spare parts stocks in the transport process management system based on the analysis of the structure of consumer properties and service quality indicators.

SIMULATION MODEL FOR UNMANNED AERIAL COMPLEXES AT LAUNCHING POSITIONS SURVIVABILITY ASSESSEMENT

The relevance of issues related to the justification of the rational approaches to the combat operations planning using unmanned aircraft systems, which are now assigned the main role in attacking the enemy by fire, necessitates the development of methods for evaluating such actions. The issue of increasing the survivability of unmanned aircraft systems under combat conditions is of important applied importance. The subject of the study is the unmanned aircraft systems application under the conditions of enemy action planning process. The article uses operations research methods. The purpose of the work is to develop a simulation model in the form of an algorithm, that allows conducting process research based on quantitative estimates and substantiating decisions aimed at increasing the survivability of unmanned aircraft systems under conditions of enemy action. By varying the parameters of the simulation model, it becomes possible to develop the most rational solutions for the unmanned aircraft systems use, ensuring the maximum level of their survivability under the specific conditions of combat operations. A simulation model is proposed, with the help of which it is possible to quantitatively evaluate decisions regarding the tactics of using UAV and finding ways to increase the level of their survivability under the conditions of enemy fire. During the implementation of the proposed model, statistics of the results of the simulated fire impact are accumulated, that allows to calculate estimates of the probability of UAV, deployed at the launching positions, to get various degrees of damage. By varying the specified parameters, it becomes possible to develop the most rational solutions aimed at increasing the survivability of UAV under the specific conditions of combat activities performing. The proposed simulation model for the survivability of unmanned aircraft systems assessment may be used in the decision support system for the appropriate level commander.

ADAPTATION OF AN AGRICULTURAL PROJECT TO THE REGULATION OF GREENHOUSE GAS EMISSIONS

Using an original methodological approach, a model analysis of the adaptation of an agricultural project to the regulation of greenhouse gas emissions from agricultural land was carried out for the farms in the south of Russia. Emissions from both technological and ecosystem processes were taken into account. Methods of operations research and simulation modeling are applied to the study of two options for regulating greenhouse gas emissions, i.e. through market mechanisms focusing on the cost of carbon units in the EU and through the administrative control, which sets the emission limits. For each of the options, the optimal intensity of use was estimated for each land plot involved in the project.The proposed methodology supplements the scientific and methodological support for the analysis of the largescale agricultural projects under the state regulation of the impact of economic activities on the Earth’s climate. It is applicable to projects with a significant spatial dispersion of plots. As a result of the study, the scientific visions of the climate policy impact on the technological configuration of agricultural production were specified. A significant margin of sustainability of agriculture in the south of Russia to carbon market methods has been revealed; It is shown that as the restrictive administrative approaches are tightened, farmers prefer to stop the intensive use of areas with the highest productivity potential while maintaining the maximum production intensity within the rest. The totality of the results obtained is recommended for practical application for developing and analyzing investment agricultural projects, including the analysis of project risks, as well as for improving the state environmental and climate policy as a tool for analyzing its consequences.

Modified projection generalized two-point two-stage extragradient quasinewton method for saddle point problems

Abstract. The purpose of this work is to investigate a new method mentioned in the article’s name. This method is designed for solving saddle problems with convexo-concave differentiable function that is defined on a convex closed subset of some finite-dimensional euclidean space and has «ravine» level hypersurfaces. The paper contains a brief survey of native publications devoted to new projection gradient methods for solving saddle problems. A mathematical statement of a saddle problem, information about solution method, some auxiliary inequalities, and method’s convergence are discussed in the article as well. Moreover, iterative formulas are exemplified for another perspective saddle method for convexo-concave differentiable saddle functions, which may be validated as well as formulas proved in this work. New auxiliary inequalities complete mathematical apparatus of convex analysis for justification of convergence and rate of convergence and have value also for justification of another methods of operations research. By using obtained inequalities, convex analysis and numerical mathematics, convergence of the saddle method for convexo-concave smooth saddle functions with Lipschitz partial gradients is proved. Under supplementary conditions, for twice continuously differentiable saddle functions, superlinear and quadratic rate of convergence of saddle method are proved, too.

Analysing Customer Satisfaction Towards Personal Loans: Evidence from Banking Industry

With an emphasis on banking industry, this study intends to examine customer satisfaction levels of those who have taken out personal loans from banking institutions. Customer satisfaction is a crucial indicator of success and longevity in the banking industry. This study explores the specifics of customer satisfaction among recipients of personal loans; wherein, the primary goal is to use operational research methods to analyse the intricate topography of customer satisfaction in the personal loan sector. The study’s secondary objective is to demonstrate how factor analysis, a well-known dimensionality reduction method, may combine these disparate traits into a condensed set of essential components. The work presents practical insights to guide strategic choices intended to improve customer satisfaction and foster long-term growth in the banking industry, focusing on Banking sector.

The Needs of Researchers Implementing Supply Chain Digitalisation

Abstract The digitalisation of the supply chain has presented substantial opportunities for companies to enhance resource allocation, pricing strategies, and overall operational efficiency. In this context, researchers play a crucial role in developing novel methodologies and algorithms that can confer a competitive edge to stakeholders. This study employed operational research methods to investigate the specific requirements of researchers in this domain. The results highlight the challenges, motivations, and practical needs experienced by researchers. Additionally, data-related issues and databases were examined to identify areas of improvement. The findings indicate that enhancing researchers' engagement in data-driven solutions for supply chain problems primarily hinges on addressing issues related to data quality, data accessibility, and regular dataset updates. By addressing all the aspects defined in this study, organisations can enhance the practical implementation of findings and drive advancements in supply chain management.

Coupling life cycle audition and operation research methods to achieve sustainable rapeseed production system

Coupling life cycle audition and operation research methods to achieve sustainable rapeseed production system

Power consumption profiles of the main fans at coal mines

Purpose. To determine the main indicators of the power consumption profiles of the main ventilation fans of coal mines under the condition of regulating their performance using controlled electric drive systems based on semiconductor elements. The methods. The methods of operations research were used to analyse technological processes in terms of compliance of the actual power consumption profiles of the main fans with their expected operating modes. The methods of mathematical statistics were used to determine the statistical parameters of the electrical loading diagrams of the main ventilation fans of coal mines. Findings. A statistical analysis of typical power consumption profiles of the main fans of coal mines with different electric drive systems has been carried out: a synchronous motor with a high-voltage frequency converter, an induction motor based on the asynchronous-thyristor cascade system, and a synchronous motor with ‘classical’ rotor excitation control without semiconductor elements. The feasibility of using adjustable electric drive systems to control the performance of main ventilation fans in terms of reducing active power consumption has been confirmed. However, at the same time, the consumption of a significant amount of reactive power is shown, which is inherent for systems based on semiconductor elements, regardless of the motor type (synchronous or asynchronous). Originality. The statistical analysis of the actual daily power consumption profiles of the main fans of coal mines with different controlled electric drive systems has shown that the standard deviation of the active load does not exceed 1% of the rated power of the drive motor and does not depend on its type (synchronous or asynchronous). Practical implementation. The practical value of the work is in determining the actual consumption of reactive power by the systems of controlled electric drive based on semiconductor elements. It shows its significant level, which should be provided by appropriate filter-compensating devices on the power supply buses of the motors of the main ventilation fans.

Food Waste Management Strategies suitable for households as sustainable food

Lack of knowledge, attitude, and behavior in managing leftover foods in households impacts the natural ecosystem and food chain, particularly in developing countries. This research aims to analyze appropriate methods for reducing and processing food waste produced in household areas. This research method uses qualitative research with operational research methods carried out for 6 months on 25 housewives in Pondok Labu Village in South Jakarta, Indonesia. The research was carried out in 3 stages, the first stage before the intervention, the second stage providing the intervention, and the third stage after the intervention. Results showed that before the intervention, on average each respondent produced 351 g of food waste each day. This amount decreased to 8.43 g/day after respondents participated in socialization to reduce food waste and training to manage food waste. The concluded that a combination of education and training improves knowledge, attitude, and behavior in household food waste management and helps moderate food waste generation.

SỬ DỤNG TÁC PHẨM TRUYỆN TRONG TÍCH HỢP GIÁO DỤC DINH DƯỠNG - SỨC KHOẺ CHO TRẺ Ở TRƯỜNG MẦM NON

Nutrition and health education are the main content in physical development education for preschool children. To help children develop some good habits and behaviors in eating and hygiene to prevent disease, teachers can use many teaching resources. This study analyzes the advantages of the story works and directions for using them to integrate nutrition and health education for preschool children. Content analysis method, direct interview method, and operational product research method have been used. The results show that stories that can be used to integrate nutrition education for children in preschool are relatively rich and diverse. Teachers can use these stories to educate children about the habit of maintaining physical hygiene and educating children about hygienic and nutritious eating, thereby improving the quality of care, nurturing and education of children at preschool educational establishments.

A Study of Systems of Neutrosophic Linear Equations

Operations research methods are among the modern scientific methods that have occupied a prominent place among the mathematical methods used in planning and managing various economic and military activities. They have been able to help specialists in developing ideal plans in terms of costs, production, storage, or investment of human energies. One of its most important methods is the method Linear programming, which was built based on the sets of linear equations that represent the constraints for any linear model. Based on the methods for solving the systems of linear equations, researchers were able to prepare algorithms for solving linear models, such as the direct Simplex algorithm and its modifications. After the emergence of neutrosophic science, we found that research methods had to be reformulated. Operations using the concepts of this science, and as a basis and foundation for neutrosophic linear programming. In this research, we will reformulate the systems of linear equations and some methods for solving them using the concepts of neutrosophic to be a basis for any study presented in the field of neutrosophic linear programming.

Neutrosophic Treatment of the Modified Simplex Algorithm to find the Optimal Solution for Linear Models

Science is the basis for managing the affairs of life and human activities, and living without knowledge is a form of wandering and a kind of loss. Using scientific methods helps us understand the foundations of choice, decision-making, and adopting the right solutions when solutions abound and options are numerous. Operational research is considered the best that scientific development has provided because its methods depend on the application of scientific methods in solving complex issues and the optimal use of available resources in various fields, private and governmental work in peace and war, in politics and economics, in planning and implementation, and in various aspects of life. Its basic essence is to use the data provided for the issue under study to build a mathematical model that is the optimal solution. It is the basis on which decision makers rely in managing institutions and companies, and when operations research methods meet with the neutrosophic teacher, we get ideal solutions that take into account all the circumstances and fluctuations that may occur in the work environment over time. One of the most important operations research methods is the linear programming method. Which prompted us to reformulate the linear models, the graphical method, and the simplex method, which are used to obtain the optimal solution for linear models using the concepts of neutrosophic science. In this research, and as a continuation of what we presented previously, we will reformulate the modified simplex algorithm that was presented to address the difficulty that we were facing when applying the direct simplex algorithm. It is the large number of calculations required to be performed in each step of the solution, which requires a lot of time and effort.

Research on the “Trinity” Teaching Mode of Management Operations Research

Operations research has been widely applied in various fields such as production planning, marketing, and engineering technology, provided the scientific basis for management decision-making, and promoted modernization of management. Operations research, as a discipline based on data decision optimization, has made significant development and progress. However, the teaching mode of operations research has not undergone reform and innovation with the arrival of the big data era. In the traditional teaching process of operations research, teachers often overemphasize mathematical theoretical derivation and problem-solving algorithms, while neglecting students’ ability to solve practical problems. To further improve the teaching quality of operations research and the training quality of management professionals, this paper analyzes the main problems existing in the teaching of management operations research courses and proposes a ‘Trinity’ teaching mode of management operations research. Based on the above, establish an integrated teaching method that integrates mathematical modeling ideas, computer experiments, and subject competition training. Through the “Trinity” teaching mode, we can deepen the understanding and application of theoretical knowledge, establish students’ modeling thinking ability, and cultivate students’ ability to use relevant software and solve practical problems. Through the study of operations research, students can use the knowledge and methods of operations research to analyze, model, and solve some typical problems in economics and management with the help of computers.

The Box and Muller Technique for Generating Neutrosophic Random Variables Follow a Normal Distribution

The focus of operations research is the existence of a problem that requires making an appropriate decision that helps reduce risk and achieves a good level of performance. Operations research methods depend on formulating realistic issues through mathematical models consisting of a goal function and constraints, and the optimal solution is the ideal decision, despite the multiplicity of these methods. However, we encounter many complex issues that cannot be represented mathematically, or many issues that cannot be studied directly. Here comes the importance of the simulation process in all branches of science, as it depends on applying the study to systems similar to real systems and then projecting this. The results if they fit on the real system. So simulation is the process of building, testing, and running models that simulate complex phenomena or systems using specific mathematical models. The simulation process depends on generating a series of random numbers subject to a regular probability distribution in the field [0, 1], and then converting these random numbers into random variables subject to the distribution law. Probability, according to which the system to be simulated operates, using appropriate techniques for both the probability density function and the cumulative distribution function. Classical studies have provided many techniques that are used during the simulation process, and to keep pace with the great scientific development witnessed by our contemporary world, we found that a new vision must be presented for this. Techniques A vision based on the concepts of neutrosophics, the science founded by the American mathematical philosopher Florentin Smarandache. The year 1995, in which new concepts of probabilities and probability distributions are used, as we presented in previous research some techniques from a neutrosophic perspective, and as an extension of what we presented previously, we present in this research a neutrosophic vision of the Box and Muller technique used to generate random variables that follow a normal distribution.

Integrating machine learning and operations research methods for scheduling problems: a bibliometric analysis and literature review

Operations research (OR) techniques have been widely used for optimizing problems, such as manufacturing scheduling, supply chain optimization, and resource allocation. Despite its effectiveness, traditional OR, especially exact methods, often struggle with scalability, computational efficiency, and adaptability to the dynamic and uncertain environments of Industry 4.0. While machine learning (ML) advancements provide novel approaches for addressing these challenges, they also present limitations, such as the lack of guaranteeing exact solutions and the need of relevant data. Therefore, the integration of OR and ML offers a balanced solution, leveraging ML’s capability to extract patterns from large datasets and making predictive decisions and OR’s precision to enhance decision-making processes, especially in scheduling tasks withing the context of Industry 4.0. This combination not only improves solution robustness and efficiency but also mitigates individual limitations of both fields. and make predictive decisions under uncertainty complements the decision-making process of OR. This paper aims to conduct a bibliometric analysis and a brief literature review on the integration of ML and OR, focusing on their application in scheduling problems.

“Known Unknowns”: Reducing Digital Inequalities in the Silver Economy

Because of the world's aging population, Europe's over-50 segment has grown significantly. This demographic trend has created a major economic opportunity known as the silver economy. Technology plays an important role in the development of products and services targeting this older market segment, yet technological innovation has compromised these individuals’ quality of life by aggravating already existing social disparities. Limited access to and use of technology is known as digital inequality, which is a matter of growing concern that is pushing experts to develop initiatives that mitigate the impact of this disparity. However, decision makers first need to be fully aware of the challenges associated with this endeavor, which arise out of the inherent complexity of digital inequality and the uncertainty produced by technological disruptions. This study addresses this decision problem by adopting a constructivist, socio-technical approach that combines cognitive mapping and decision-making trial and evaluation laboratory techniques in a neutrosophic context. Data were collected from an expert panel with experience in real-world cases. The members analyzed different strategies and their effectiveness in terms of reducing digital inequalities in the silver economy. The results were analyzed and validated by the Head of Data and Analytics at Nippon Telegraph and Telephone Data Europe and Latin America. No prior research was found that has used the same combination of operational research methods to address this decision problem. The findings include recommendations for how to facilitate the development of initiatives that increase digital literacy in a silver economy context.

Distributed Data Proceeding Systems Architectures Resource Efficiency Improvement on the Basis of Apriory Data about the Jobs Late Completion Times

Purpose of research. The purpose of this study is to develop a method for improving the efficiency of distributed architectures of data processing systems operating in the fog and edge layers of the network. In conditions of high dynamics of both the network infrastructure and the load, the task of forming the architecture of data processing systems is solved regularly (migration of virtual machines, horizontal scalingб etc.) At the same time, the issue of the consumption of the residual resource of computing nodes is practically not considered, while the often used devices have relatively low capacity, and their high workload leads to a reduction in the service life. Therefore, the creation of methods for forming an architecture of a computing device system that is effective in terms of saving a computing resource is an urgent task.Methods. The main scientific methods used in this study are domain analysis, operations research methods, optimization methods and computer modeling, confirming the feasibility of the main aspects of the developed method. To improve the efficiency of placing computational tasks on the nodes of a network fragment, this paper formulated a multicriteria optimization problem, where each element of the vector objective function corresponds to an individual value of the probability of failure-free operation of a computing device. To obtain estimated values of the cost function, a priori estimates of the late completion of the solution of computational problems by nodes are used, since the resource allocated for solving depends on the allocated time, and the time for solving the problem, respectively, on the allocated computing resource. The value of the cost function is calculated on the basis of approximate a priori estimates, which leads to a positive effect in terms of the consumption of computing resources of devices.Results. The result of the study is a developed method for improving the efficiency of distributed architectures of data processing systems operating in the fog and edge layers of the network.Conclusion. The method proposed in this work allows to choose such a load distribution in order to reduce the workload of devices and thus reduce the consumption of computing resources of the devices.

Разработка математической модели оптимизации процесса обучения курсантов силовых структур в высших учебных заведениях как сложной системы

This article discusses the problem of developing a mathematical model to describe the process of training cadets of law enforcement agencies in higher educational institutions, which are a complex system. Currently, reality places high demands on the qualifications and personal qualities of law enforcement officers, which in turn requires the relevant educational organizations to carry out advanced professional training of specialists. The functioning of higher educational institutions of law enforcement agencies is influenced by objective and subjective factors. To this end, in order to increase the efficiency of managerial decision-making in the process of training cadets of law enforcement agencies in higher educational institutions, it is proposed to use a mathematical model of training and education of a group of cadets, developed on the basis of the method of operations research, specifically the theory of Markov random processes that occur during continuously changing time and use discrete states of the system. The mathematical model of training and education of a group of cadets in its functioning uses streams of events that have the properties of stationarity and ordinariness. The process of transition from one state to another is described in the form of a marked graph of states with a transition probability density. Setting the intensity of the event flows will allow you to find the limiting probabilities and predict the states in which the study group is located. This approach will reduce the level of subjectivity of university staff and will allow structuring and optimizing the process of assessing the level of knowledge of cadets.