Modeling Of Operations Research Articles

Towards Greener Coal Mining: A Life Cycle Assessment Model for Small-Scale Underground Operations

Towards Greener Coal Mining: A Life Cycle Assessment Model for Small-Scale Underground Operations

Research on Issues and Strategies of Financial Management Informationization in the Era of Big Data

In the context of rapidly evolving big data and cloud computing technologies, the field of corporate financial management is undergoing a profound transformation. Financial management informationization, as a core engine driving improvements in corporate efficiency and cost optimization, has become a new frontier in industry competition. This trend not only accelerates the intelligent and real-time nature of corporate decision-making processes but also profoundly alters traditional models of capital operations, cost control, and risk management. However, while the wave of big data offers unprecedented data treasures and powerful analytical tools for corporate financial management, it also brings a series of pressing challenges that need to be addressed. This paper aims to thoroughly analyze the main obstacles faced by financial management informationization in the era of big data and to propose a series of practical solutions.

Service center in electricity distribution sector with variable peak electricity time: A two-stage queuing model approach with nested simulation

Customer service is essential for any business, significantly impacting customer satisfaction, brand reputation, and reducing associated costs. In the electricity industry, this importance is heightened due to the critical nature of electricity in daily life and growing usage rate of new technologies. Effective customer service in electric distribution companies involves handling a wide range of inquiries, from billing issues to technical support, requiring both remote and on-site assistance. The variability in customer calls, influenced by factors such as peak electricity hours, and different service and customer types, presents unique challenges in designing efficient service centers. This research develops a tailored model for the service center operations of a power distribution company, focusing on optimal staffing levels within budget constraints. A two-stage queuing model is proposed to address the needs of residential and non-residential customers. The study employs nested simulation techniques to account for variability in customer arrival times and peak hours. Key performance indicators (KPIs) are used to evaluate the effectiveness of staffing policies, considering both quantitative and qualitative costs. The study integrates stochastic optimization and ranking and selection methods to identify the best staffing configurations. The findings offer actionable insights for improving customer service efficiency and operational effectiveness in electricity customer service centers.

A conceptual model for sustainable cementing operations in offshore wells

This review paper introduces a conceptual model to enhance the sustainability of cementing operations in offshore wells. Traditional cementing practices, characterized by significant greenhouse gas emissions and compliance challenges, necessitate the development of more environmentally friendly approaches. The proposed model integrates low-carbon cement alternatives, such as geopolymer cements and supplementary cementitious materials (SCMs), with advanced slurry designs that incorporate nanomaterials, biodegradable additives, and recycled materials. This integrated approach reduces the environmental impact and enhances the mechanical properties and durability of cementing operations. The paper provides operational guidelines for implementing this model and discusses future prospects, emphasizing the importance of continued research and collaboration among industry stakeholders. By adopting these innovative practices, the oil and gas industry can achieve significant environmental benefits and ensure compliance with stringent regulatory standards, paving the way for a more sustainable future in offshore drilling.

Team-centered IDAC: Modeling and simulation of operating crew in complex systems - Part 2: Simulation aspects and application

Complex systems operations, such as Nuclear Power Plants (NPPs), generally require professional operating teams. Factors associated with teamwork, such as inappropriate communication and coordination, are important contributing factors to accidents and unsafe behavior. The impact of crew interactions on team effectiveness and, consequently, on the entire system, has yet to be fully and quantitatively explored in high-risk environments such as NPPs. Since a team is an interactive social system, team-specific issues must be studied and evaluated from a “team perspective”—based on team dynamics and processes. This paper is a part of a two-papers series that presents a simulation-based Team Model for NPP control room operations. Part 1 describes the theoretical fundaments of the model and details its elements. The current paper, Part 2, describes the simulation aspects and a full application of the method to an NPP four-steam generator feedwater system pipe break. It presents how to set up the simulation elements, such as hardware and humans within a team, using MATLAB Simulink. The method is demonstrated through a case study of an NPP four-steam generator feed water system pipe break. The results are discussed and assessed against theoretical and experimental findings.

AIOps in Action: Automating AI Deployment and Management of Large Language Models for Scalable and Ethical Operations

This comprehensive article explores the transformative role of Artificial Intelligence for IT Operations (AIOps) in the deployment and management of Large Language Models (LLMs). It delves into the automation strategies that streamline LLM deployment, including data preparation, model training optimization, and continuous integration and deployment practices. The article addresses the unique challenges in LLM management, such as resource allocation complexities and latency issues, presenting AIOps-driven solutions that leverage predictive analytics and dynamic scaling techniques. A significant focus is placed on the synergies between AIOps and MLOps, highlighting how their integration enhances model versioning, governance, and performance monitoring. The article also examines the critical aspects of real-time monitoring and incident management, showcasing how AIOps enables sophisticated anomaly detection and automated incident response. Ethical considerations in AIOps-driven LLM deployment are thoroughly discussed, emphasizing the importance of bias mitigation, transparency, and accountability. Looking ahead, the article explores future trends in AIOps for LLM management, including advancements in automation technologies and their implications for operational scalability and efficiency. Through a combination of theoretical analysis and practical case studies, this article provides a comprehensive overview of how AIOps is revolutionizing the landscape of AI operations, offering insights into both the current state and future potential of automated, scalable, and ethically responsible LLM management.

Simulation modeling of artillery operations in computer games: approach based on Markov processes

The object of this research is an approach to simulating the combat operation of artillery in computer games based on Markov processes. In modern computer games, an important role is played by the realism and plausibility of combat simulation. One of the most difficult and at the same time most interesting tasks is the modeling of artillery operations, where it is necessary to take into account numerous factors affecting the effectiveness of combat work. The research was aimed at improving the methods and models of controlling the combat work of artillery under the conditions for the firing position change and the presence of external disturbances. The use of stochastic models allows more accurate modeling of the behavior of artillery units, taking into account the random nature of many parameters, such as projectile speed, reload time, and the probability of detection by enemy forces. The proposed approach includes the development of a simulation model that allows determining optimal strategies to achieve maximum effectiveness of combat work. The model is based on Markov processes, which allows taking into account possible system states and probable transitions between them. This allows not only to simulate combat operations, but also to predict the results depending on different scenarios. The results of the study show that the use of Markov processes in the simulation of combat operations can significantly increase the realism and efficiency of artillery operations in computer games. This opens up new opportunities for game developers to create more immersive and authentic gaming experiences. The proposed model can be used as a basis for further research and improvement of combat simulation methods in computer games. It can also be used in military simulators and simulators where realistic combat conditions must be taken into account.

Multi-physics modeling of phase change memory operations in Ge-rich Ge2Sb2Te5 alloys

One of the most widely used active materials for phase-change memories (PCM), the ternary stoichiometric compound Ge2Sb2Te5 (GST), has a low crystallization temperature of around 150°C. One solution to achieve higher operating temperatures is to enrich GST with additional germanium. This alloy crystallizes into a polycrystalline mixture of two phases, GST and almost pure germanium. In a previous work [R. Bayle et al., J. Appl. Phys. 128, 185 101 (2020)], this crystallization process was studied using a multi-phase field model (MPFM) with a simplified thermal field calculated by a separate solver. Here, we combine the MPFM and a phase-aware electrothermal solver to achieve a consistent multi-physics model for device operations in PCM. Simulations of memory operations are performed to demonstrate its ability to reproduce experimental observations and the most important calibration curves that are used to assess the performance of a PCM cell.

Climb Performance Prediction in High Drag Configuration Middle-Class Transportation Aircraft: An Ensemble Learning Approach

This study addresses the application of machine learning and artificial neural network models for predicting the climb speed of the C-130H military transport aircraft. Random Forest, Neural Network, and Ensemble models were developed to overcome limitations of traditional chart reading and interpolation methods. Models were trained on flight manual data, considering factors such as gross weight, pressure altitude, drag index, temperature deviation, and engine efficiency. Comparative analysis revealed the Ensemble approach, combining Random Forest and Neural Network techniques, provided the highest accuracy (R² ≈ 0.4532), followed by Random Forest (R² ≈ 0.4303) and Neural Network (R² ≈ 0.3765) models. All significantly outperformed the traditional Young Method (R² = -1.2673). Feature importance analysis identified pressure altitude, gross weight, and engine efficiency as critical factors influencing climb speed. The ensemble approach demonstrated more reliable and accurate results in predicting C-130H climb rates, reducing risks associated with single-model reliance. This research highlights the potential of machine learning in aircraft performance prediction, offering possibilities for improving pre-flight preparation, reducing workload, and enhancing flight safety. Implications for the aviation industry and future research directions are discussed, emphasizing the role of advanced predictive models in shaping future flight operations and aircraft performance management.

Analysing cross-border logistics operations for performance improvement: development and validation of a reference model

AbstractThe critical role played by cross-border logistics (CBL) systems in sustaining fast, efficient and responsive global supply chains is widely recognised. However, scholarly research focusing on CBL systems is limited, which means that theoretical frameworks and methodological approaches available for analysing and improving CBL operations are underdeveloped. As such, coordination among diverse stakeholders, meeting regulatory requirements, adapting to a rapidly evolving technological environment, and limited capacity for investing in high-value assets, all remain ongoing challenges hindering the smooth and swift flow of cargo across borders. With a view to addressing these challenges and research gaps, this paper develops a reference model for analysing and improving CBL-specific processes. Seven underlying process dimensions have been identified following a rigorous methodological approach for the modelling, analysis and improvement of CBL operations. The efficacy of the reference model is demonstrated using two purposive case applications. The reference model also helps identify potential technological interventions to improve operations and benchmark CBL systems, operations and processes.

Coherent Radio Emission from “Main-sequence Radio Pulse Emitters”: A New Stellar Diagnostic to Probe 3D Magnetospheric Structures

Main-sequence radio pulse emitters (MRPs) are magnetic early-type stars that produce coherent radio emission observed in the form of periodic radio pulses. The emission mechanism behind this is the electron-cyclotron maser emission (ECME). Among all kinds of magnetospheric emission, ECME is unique due to its high directivity and intrinsically narrow bandwidth. The emission is also highly circularly polarized and the sign of polarization is opposite for the two magnetic hemispheres. This combination of properties makes ECME highly sensitive to the three-dimensional structures in the stellar magnetospheres. This is especially significant for late-B and A-type magnetic stars that do not emit other types of magnetospheric emission such as Hα, the key probe used to trace magnetospheric densities. In this paper, we use an ultra-wideband observation (0.4–2 GHz) of a late B-type MRP HD 133880 to demonstrate how we can extract information on plasma distribution from ECME. We achieve this by examining the differences in pulse arrival times (“lags”) as a function of frequencies and qualitatively comparing those with lags obtained by simulating ECME ray paths in hot stars’ magnetospheres. This reveals that the stellar magnetosphere has a disk-like overdensity inclined to the magnetic equator with a centrally concentrated density that primarily affects the intermediate frequencies (400–800 MHz). This result, which is consistent with the recent density model proposed for hotter centrifugally supported magnetospheres, lends support to the idea of a unifying model for magnetospheric operations in early-type stars, and also provides further motivation to fully characterize the ECME phenomenon in large-scale stellar magnetospheres.

Team-centered IDAC: Modeling and simulation of operating crew in complex systems – Part 1: Team model and fundamentals

Operation of highly complex systems such as Nuclear Power Plants (NPPs) generally require highly trained professional operating teams. Factors associated with teamwork, such as ineffective communication and coordination, can be important contributing factors to accidents and unsafe behavior. The impact of crew interactions on team effectiveness and, consequently, on the entire system, has not been fully and quantitatively explored in high-risk environments such as NPPs. Since a team is an interactive social system, team-specific issues must be studied and evaluated from a “team perspective”—based on team dynamics and processes. This paper is part of a two-papers series that presents a simulation-based Team Model for NPP control room operations. The current paper, Part 1, describes the theoretical fundaments of the model and details its elements. The accompanying paper describes the simulation aspects, and a full application of the method to a pipe break accident in a four- four-loop steam generator feedwater system. The proposed model is based on the IDAC (Information, Decision, and Action in Crew context) cognitive model framework. The resulting model, Team-centered IDAC (Tc-IDAC), examines the team activities “Collaborative information collection,” “Shared decision making,” and “Distributed action execution” through specific modules for Team Error Management. These modules include error detection, error indication and error correction, and team performance shaping factors.

FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

Сombat operations model of a single self-propelled artillery system for the computer game ARMA 3

Military computer games are an important segment of the world media culture and media business. In addition to the entertainment aspect, military simulators play an important role in the training of future military specialists. However, quite often the scenario component of military gaming lags behind the rapid development of real military equipment. World experience of military conflicts of the 21st century shows that the most intensively developing segment of ground forces weapons is its artillery component. In this paper, a model of combat use of a new generation artillery system is developed. The model is intended for modification of the military game ARMA 3. The new generation artillery system is a large-caliber gun with a high level of automation. It has a high rate of fire, maneuverability, and shooting accuracy. Due to these qualities, the new generation artillery system is comparable in combat power to a unit of traditional guns and can carry out combat operations in single mode. A technique has been developed for dynamically assessing the current combat capability of an artillery system, taking into account the resource costs during fire activity, including indirect hits by the enemy. It is shown that in addition to traditional tactical counter-battery tasks, an artillery system can be used to destroy a suddenly emerging high-risk target. A high-risk target is a non-artillery system capable of causing very large damage in a short period of time. Based on the method of dynamically assessing the current combat capability, a tree of artillery system states is constructed. It includes the most probable states of the artillery system and the corresponding design parameters. A ratio is obtained that allows, for a known state of the artillery system, to estimate the number of shots needed to hit the target with the required guaranteed probability. Calculated examples show that a new-generation artillery system is capable of destroying a high-risk target, sometimes even at the cost of its own loss. The developed model is implemented and is being tested as a mod for the war game ARMA 3.

Model of activity of Mayor Aleksey Sokolov and agents stormtroopers under his leadership

In the public narrative on partisan warfare, an extremely important role is played by agents stormtroopers under Mayor Aleksey Sokolov leadership. Many have heard that this person was “the leader of all-Ukrainian agents stormtroopers”, who was sent to Lithuania to organize the activity of agents stormtroopers. Yet, the information known about this person has been scarce so far. By this article it is sought to provide some facts on the biography of A. Sokolov and highlight the principles of activity of special groups under his leadership. A. Sokolov was a specialist in the specific agency-operational area. He was a recipient of various insignia and other honorary awards, which demonstrated appreciation of his activity by the All-Russian Extraordinary Commission (Cheka) authorities. On the other hand, legality of methods used by A. Sokolov and stormtroopers under his leadership raised doubts. It is likely that namely this problem prevented him from awarding the title of the Hero of the Soviet Union. It took to A. Sokolov less than one year of working with the special group in order to create a model of stormtroopers usage adapted to fighting the Ukrainian partisans. In doing this, A. Sokolov consulted the stormtroopers themselves. While being on secondment in Lithuania, he reformed the special group of the Ministry of Internal Affairs (MVD) of Lithuanian Soviet Socialist Republic (LSSR) according to the Ukrainian model. The operation that took place in Grendavė village of Onuškis Rural District in Trakai County was in line with the model of operations carried out in Ukraine: on the basis of operational data, a legend had been created, a list of persons to be detained and interrogated compiled, the stormtroopers, dressed and equipped as the Red Army soldiers, prepared bunkers for future interrogations, wrote minutes containing evidence of the persons detained, etc. A. Sokolov’s expertise and methodical guidance laid the foundation for practical activity of special groups in Lithuania. Further on, the stormtroopers actually employed the tactics, created by A. Sokolov.

String-level compact modeling of erase operations in the body-floated vertical channel of 3D charge trapping flash memory

In this study, we examined the use of string-level compact modeling as an effective framework for circuit simulation focusing on erase operation in 3D charge trapping flash (CTF) memory devices. We analyzed the behaviors of the accumulated hole from p-type bulk (p-well) and the corresponding difference of channel electrostatic potential in the CTF cell string, which is attributed to the variation in hole barrier height in the channel of the ground select line (GSL) transistor during erase operation. We derived a formula for the hole current delivering positive potential from the p-well to the channel region and established a modeling procedure. Technology computer-aided design (TCAD) simulation results were used to extract model parameters and analyze channel electrostatic potential during the erase operation. Additionally, experimental data for erase speed were verified using simulation program with integrated circuit emphasis (SPICE) results. Because the erase efficiency is strongly related to hole behaviors based on the conditions of the GSL transistor, the proposed compact modeling is an effective tool for circuit designers and system architects to achieve better performance in erase execution and optimizing the design of 3D CTF memory devices.

Can applying a motivation theory of elderly volunteers help solve the management dilemma of rural libraries? A case study from China

The biggest management dilemma of rural libraries stems from the shortage of librarians. This study draws upon the case analysis of Qiluo Library in China to investigate the volunteering motivation of the elderly as well as the feasibility of the model of a rural library run entirely by elderly volunteers. Through questionnaires and semi-structured interviews, it was found that the elderly had four types of motivation for volunteering at rural libraries: endogenous egoism, exogenous egoism, endogenous altruism, and exogenous altruism. Connecting motivation with rural libraries’ dilemma of personnel shortages is of great significance. Despite some challenges, the Qiluo model of library operations provides an innovative and effective way of managing rural libraries in the context of librarian shortages and an aging population, especially in areas with low elderly mobility and less-developed economies. The construction of a motivational framework of elderly volunteers also provides new ideas for addressing the library dilemma.

Modeling and Analysis of BESS Operations in Electricity Markets: Prediction and Strategies for Day-Ahead and Continuous Intra-Day Markets

Modeling and Analysis of BESS Operations in Electricity Markets: Prediction and Strategies for Day-Ahead and Continuous Intra-Day Markets

Computer-Simulated Virtual Image Datasets to Train Machine Learning Models for Non-Invasive Fish Detection in Recirculating Aquaculture.

Artificial Intelligence (AI) and Machine Learning (ML) can assist producers to better manage recirculating aquaculture systems (RASs). ML is a data-intensive process, and model performance primarily depends on the quality of training data. Relatively higher fish density and water turbidity in intensive RAS culture produce major challenges in acquiring high-quality underwater image data. Additionally, the manual image annotation involved in model training can be subjective, time-consuming, and labor-intensive. Therefore, the presented study aimed to simulate fish schooling behavior for RAS conditions and investigate the feasibility of using computer-simulated virtual images to train a robust fish detection model. Additionally, to expedite the model training and automate the virtual image annotation, a process flow was developed. The 'virtual model' performances were compared with models trained on real-world images and combinations of real and virtual images. The results of the study indicate that the virtual model trained solely with computer-simulated images could not perform satisfactorily (mAP = 62.8%, F1 score = 0.61) to detect fish in a real RAS environment; however, replacing a small number of the virtual images with real images in the training dataset significantly improved the model's performance. The M6 mixed model trained with 630 virtual and 70 real images (virtual-to-real image ratio: 90:10) achieved mAP and F1 scores of 91.8% and 0.87, respectively. Furthermore, the training time cost for the M6 model was seven times shorter than that for the 'real model'. Overall, the virtual simulation approach exhibited great promise in rapidly training a reliable fish detection model for RAS operations.

Transit Dynamic Operation Optimization Using Combination of Stop-Skipping Strategy and Local Route Optimization

The efficient operation and intelligent upgrading of public transportation can effectively enhance the attractiveness of conventional public transportation. In order to improve the delicacy management level of bus operations, this study designed a new dynamic optimization model for single-line bus operations with the dual optimization objectives of the lowest passenger travel cost and lowest operation cost, using a combination of the strategy of stop-skipping control and local route optimization. Simulated annealing (SA) was introduced into the genetic algorithm (GA) to design a hybrid heuristic algorithm for model solving. The effectiveness of the optimization model and the hybrid algorithm were verified and evaluated by using the No. 115 bus line in Ganzhou City as an example. The results showed that the proposed optimization model had a good usability, which can effectively improve the average vehicle speed, shorten the overall waiting time of passengers, and enhance the operational efficiency of the line. The hybrid algorithm saw significant improvement in terms of the iteration speed and the quality of the optimal solution compared with the conventional genetic algorithm.