Modeling Of Operations Research Articles

Load Balancing for Multiedge Collaboration in Wireless Metropolitan Area Networks: A Two-Stage Decision-Making Approach

Mobile edge computing (MEC) relieves the latency and energy consumption of mobile applications by offloading computation-intensive tasks to nearby edges. In wireless metropolitan area networks (WMANs), edges can better provide computing services via advanced communication technologies. For improving the Quality-of-Service (QoS), edges need to be collaborated rather than working alone. However, the existing solutions of multi-edge collaboration solely adopt a centralized or decentralized decision-making way of load balancing, making it hard to achieve the optimal result because the local and global conditions are not jointly considered. To solve this problem, we propose a novel Two-stage Decision-making method of load Balancing for multi-Edge Collaboration (TDB-EC). First, the centralized decision-making is executed with global information, where a deep neural networks (DNN) based prediction model is designed to evaluate the range of task scheduling between adjacent edges. Next, considering the global condition of load balancing, the decentralized decision-making is executed with local information, where a deep Q-networks (DQN) based Q-value prediction model of adjustment operations is developed to evaluate the load balancing plan among edges. Finally, the objective load balancing plan is obtained via feedback-control. Extensive simulation experiments demonstrate the adaptability of the TDB-EC to various scenarios of multi-edge load balancing, which approximates the optimal result and outperforms three classic methods.

Моделирование и управление параметрами поточных технологий с локальным гидротранспортом и обезвоживанием угля на основе анализа структуры проходческого цикла

Introduction. The article discusses the issues of modeling various variants of flow line tunneling technologies based on an analysis of the structure of the tunneling cycle. Materials and methods. As research methods, the authors used: analysis of the structures of the tunneling cycle of continuous mining technologies, mathematical modeling of processes and operations, and industrial testing of options for implementing the proposed technological tunneling systems. Results. The results obtained during the implementation of the economic and mathematical model for the variant of the flow technology presented above showed that the following parameters of the technological scheme are optimal: technical productivity of the combine – 1.1 m3/min; process water consumption – 200 m3/h; number of drilling rigs – 2; the number of miner units is 6. The speed of development with these parameters of the flow technological scheme will be 880 m3/month, the labor productivity of miners is 14.8 m3/person during the work shift. Conclusion. The article summarizes the results of research on the development, justification of parameters and implementation of various options for in-line technologies for carrying out inclined workings with a hydraulic slope for various purposes for a wide range of conditions of their application. Resume. Implementation of the model proposed by the authors made it possible to determine the primary area of application of the considered version of the flow technology for carrying out preparatory workings in relation to the mining and geological conditions of the Kuzbass mines. Variants of the considered technological schemes for carrying out workings were implemented during the mining of reserves of the Anzherskaya-Yuzhnaya-3 mine, during the underground mining of reserves of the Listvyansky and Kedrovsky open-pit mines located in the Kemerovo region of the Russian Federation and the implementation of a project for the combined mining of a graphite deposit in middle Vietnam.Suggestions for practical application and direction for future research. The materials of the article may be useful for scientific and design organizations, coal mining enterprises in modeling and implementing precision mining technologies.

From the History of Mathematical Modeling Military Operations in Russia (1900-1917)

The article is devoted to the original mathematical models of combat operations developed in Russia at the beginning of the XX century. One of the first works in which approaches to mathematical modeling of military operations were outlined can be considered an article by Y. Karpov «Tactics of fortress artillery», published in 1906. It considered the task of defending the fortress from attacking enemy infantry chains. Based on the idea of the attackers overcoming the line of defense, mathematical relations were obtained linking the parameters of the shot of the shrapnel charge with the movements of the infantryman. Similarly, the task of using a machine gun for the defense of the fortress was considered. After analyzing the obtained ratios, Y. Karpov came to the conclusion that all means of defense of the fortress can be correlated through the length of the area defended by this means. P. Nikitin developed Y. Karpov's ideas. He considered a wide range of means of destruction. Based on the results of the research, the author made recommendations on the distribution of forces and means in the defense of fortresses. M. Osipov in 1915 published vivid and original models of two-way combat operations, a year earlier than the well-known Lanchester theory. Summing up the numbers of the fighting sides at infinitesimal intervals of time, and then moving to the limits, he obtains linear and quadratic laws of the influence of the ratio of the number of the fighting sides on their losses, and explores heterogeneous means of destruction. All this is verified by the practice of various battles. M. Osipov showed that the coefficients in the laws of losses depend on the training of personnel, terrain, the presence of fortifications, the moral and psychological state of the troops, etc. Based on the results of mathematical modeling, M. Osipov for the first time substantiated a number of provisions of the art of war. He showed that neither linear nor quadratic laws of losses in general do not correspond to the practice of the battles conducted. For ease of use at that level of computer technology development and to obtain a more reliable result, M. Osipov proposed using the degree of "three second" in the laws of losses, although he himself understood its approximate nature. Much attention is paid to the problem of authorship, the search for a prototype of the creator of the first two-sided model of combat operations, and the application of theory to solve modern applied problems.

Feasibility assessment of self-deicing concrete pavements with recycled CFRP reinforcement: Environmental and mechanical performance

Electrically conductive concrete (ECC) with carbonaceous conductive elements has been studied for self-deicing pavements; however, carbon fiber production is costly and resource-intensive. Therefore, mechanically recycled carbon fiber reinforced polymer (CFRP) scraps from plane manufacturing were evaluated as reinforcing and conductive elements for ECC.Evaluation of mechanical properties showed that 5%Vol rCFRP-ECC had approximately 20% higher flexural strength and twofold greater absorbed fracture energy than the reference plain concrete. In a -15 °C environment, this ECC melted ice at a heating rate of 0.05 °C/min and a 3.8 kWh/m2 power consumption.A life cycle assessment modeling of winter maintenance operations for one snowstorm in the Sierra Nevada Mountain regions in California showed that rCFRP-ECC and two other ECCs from the literature have a significantly higher global warming potential from energy consumption than snowplowing, sanding, and salting. Nevertheless, important advantages of ECCs in reducing chloride contamination in water and soil, corrosion in vehicles and infrastructure, and stronger and more durable fiber-reinforced infrastructure materials should be considered in decision-making.

Culture of Collaboration: How Madison County Government Drew from its Roots and used Rural Appalachian Values to Increase Government Participation through COVID Service Delivery

This study employs a storytelling approach to describe how a small, rural county in southern Appalachia drew from its roots to combat a pandemic, and in the process increased its resilience. In Madison County, leaders supported their communities by developing cross-sector collaborations that dramatically increased public participation on issues that most affected the county: education access, community outreach, food security and COVID vaccination delivery. Leaders built upon cultural norms and values–honest intent, community spirit, kindness, helping neighbors, hard work, and self-reliance–to increase participation. The results are modeled as an intervention, a flowchart that includes prior county operational norms, cultural norms and values, new models for government operations, and impacts on service delivery. There is a particular focus on mechanisms, the often-unspoken psychological processes that supported the transition in government operations and community and individual participation. Employing storytelling techniques amplifies the explanation of how the processes increased the county’s resilience, and outlines a way of working that they will use to address existing and new complex challenges as they arise.

The Community Development Operation Model of Ban Nam Sai Community, Thung Benja Sub-district, Tha Mai District, Chanthaburi Province

Community development operations are important because they help strengthen communities. The objectives of this research were to analyze and examine the consistency of the community development operations (CDO) model with empirical data, and to study the weight of direct, indirect, and total effect of factors influencing on CDO of Ban Nam Sai Community, Thung Benja Sub-district, Tha Mai District, Chanthaburi Province. The population in this study was 173 people who are representing households living in Nam Sai Village. The samples were 121 people, calculating by Taro Yamane’s formula. The research tool in this study was the questionnaire-based survey. Analyzed and examined the consistency of the community development model of Ban Nam Sai community with empirical data based on the model goodness of fit indices used a Structural Equation Model. Analyzed weight of direct, indirect and total effect of causal factors on community development operation of Ban Nam Sai community by used a Structural Equation Model with statistic Path analysis. The results were found that The CDO model of Ban Nam Sai community developed consistent with the empirical data. It was also found that Leadership, public participation and community communication had direct effects on community development operations of Ban Nam Sai community. Besides, community communication was found a direct effect on public participation. Among these studied variables, leadership was found to have an indirect effect on community operations of Ban Nam Sai Community through public participation and community communication. Community communication also had an indirect effect on the community development operation of the Ban Nam Sai community through public participation. The total effect on community development operations of Ban Nam Sai community. The highest total influential factor was public participation, followed by leadership and community communication respectively. Suggestion from the research results, the community development operation model of Ban Nam Sai Community is suitable for use. The model should be developed and applied to use to the similar context of communities/villages for further community development.

Synergies between Lean and Industry 4.0 for Enhanced Maintenance Management in Sustainable Operations: A Model Proposal

Companies actively seek innovative tools and methodologies to enhance operations and meet customer demands. Maintenance plays a crucial role in achieving such objectives. This study identifies existing models that combine Lean Philosophy and Industry 4.0 principles to enhance decision-making and activities related to maintenance management. A comprehensive literature review on key concepts of Lean Philosophy and Industry 4.0, as well as an in-depth analysis of existing models that integrate these principles, is performed. An innovative model based on the synergies between Lean Philosophy and Industry 4.0, named the Maintenance Management in Sustainable Operations (MMSO) model, is proposed. A pilot test of the application of the MMSO model on a conveyor belt led to an operational time increase from 82.3% to 87.7%, indicating a notable 6.6% improvement. The MMSO model significantly enhanced maintenance management, facilitating the collection, processing, and visualization of data via internet-connected devices. Through this integration, various benefits are achieved, including improved flexibility, efficiency, and effectiveness in addressing market needs. This study highlights the value of integrating Lean Philosophy and Industry 4.0 principles to improve maintenance management practices. The proposed MMSO model effectively leverages these principles, fostering agility, optimized resource utilization, heightened productivity and quality, and reduced energy consumption. The model not only serves as a tool for operational optimization and customer demand enhancement but also aligns with sustainability principles within the energy transition. Its successful application in the pilot test phase further reinforces its potential as a reliable approach for maintenance management and sustainable operations in both production and decision-making processes.

A Hybridized- Logistic Regression and Deep Learning-based Approaches for Precise Anomaly Detection in Cloud

Anomaly Detection plays a pivot role in determining the abnormal behaviour in the cloud domain. The objective of the manuscript is to present two approaches for Precise Anomaly Detection Approaches by hybridizing RBM with LR and SVM models. The various phases in the present approach are (a) Data collection (b) Pre-processing and normalization; OneHot Encoder for converting categorical values to numerical values followed by encoding the binary features through normalization (c) training the data (d) Building the Feedforward Deep Belief Network (EDBN) using hybridizing Restricted Boltzmann Machine (RBM) with Logistic Regression (LR) and Support Vector Machine (SVM); In the first approach, RBM model is trained through unsupervised pre-training followed by fine-tuning using LR model. In the later approach, RBM model is trained through unsupervised pre-training followed by fine-tuning using SVM model; both the approaches adopt unsupervised pre-training followed by supervised-fine-tuning operations (e) Model Evaluation using the significant parameters such as Precision, Recall, Accuracy, F1-score and Confusion Matrix. The experimental evaluations concluded the effective anomaly detection techniques by integrating the RBM with LR and SVM for capturing the intricate patterns and complex relationships among the data. The proposed approaches paves a path to improved anomaly detection technique, thereby enhancing the security features and anomaly monitoring systems across distinct domains.

Operations-informed incident response playbooks

Cyber security incident response playbooks are critical for establishing an effective incident response capability within organizations. We identify a significant conceptual gap in the current research and practice of cyber security playbook design: the lack of ability to communicate the operational impact of an incident and of incident response on an organization. In this paper, we present a mechanism to address the gap by introducing the operational context into an incident response playbook. This conceptual contribution calls for a shift from playbooks that consist only of process models to playbooks that consist of process models closely linked with a model of operations. We describe a novel approach to embed a model of operations into the incident response playbook and link it with the playbook's incident response activities. This allows to reflect, in an accurate and systematic way, the interdependencies and mutual influences of incident response activities on operations and vice versa. The approach includes the use of a new metric for evaluating the change in operations in coordination with critical thresholds, supporting decision-making during cyber security incident response. We demonstrate the application of the proposed approach to playbook design in the context of a ransomware attack incident response, using a newly developed open-source tool.

(Invited) An Industrial Perspective on Electrochemistry and the Energy Transition

As wind and solar become an increasingly larger share of primary energy, the use of electrochemical storage and conversions is growing. Traditionally, carbon-based fuels such as oil, coal and natural gas have been globally traded and served as energy storage media for the power and transportation sectors. With the growth of lower-carbon energy technologies, particularly wind and solar power, effective use of renewable electrons requires high-performing, cost-effective solutions for storage and distribution services that are analogous to traditional fuels. Electrochemical energy storage technologies are increasingly used for a range of applications from kW to GW scales, with this deployment momentum built from improved performance and declining costs.This talk will address where multiple types of storage exist in the historical & current energy system, their scales, and the roles of storage in the emerging energy transition from the perspective of an international energy company. Our energy storage and conversion research & development over the past decade will also be discussed, with a focus on current and emerging electrochemical systems designed for use in mobility, the power sector, and industry. Systems of active research include metal anodes used with high voltage cathodes and high energy density stationary batteries. This work has been driven by business challenges, with a range of modeling approaches used to generate insights that complement theory & experiment. Techniques include large-scale energy system modeling, modeling of power market operations, technoeconomic analyses to guide cost & performance targets of new technologies, and new ways to capture and predict physical phenomena that underpin device life and performance. Finally, commercial operation examples of energy storage operation will be provided, along with challenges still to be resolved through improved system understanding across scales.

An Extended Type-2 Fuzzy Relational Database Model for Aggregate and Grouping Operations

This paper introduces an extended type-2 fuzzy relational database model (ET-2FRDB) for aggregate and grouping operations that can represent and query uncertain and imprecise information in the real world applications. In ET-2FRDB, each fuzzy relation is represented by a type-2 fuzzy set whose membership degree of each tuple is a fuzzy number on [0, 1], the fuzzy aggregate functions, the fuzzy relational algebraic, aggregate and grouping operations are defined as extensions of those in the classical relational database model thereby the membership degree of tuples associated by using the minimum and maximum of fuzzy numbers. Also, some properties of the fuzzy relational algebraic, aggregate and grouping operations in ET-2FRDB are formulated and proven.

Conceptual modeling of big data SPJ operations with Twitter social medium

Conceptual modeling of big data SPJ operations with Twitter social medium

An Intelligent Risk Forewarning Method for Operation of Power System Considering Multi-Region Extreme Weather Correlation

Extreme weather events pose significant risks to power systems, necessitating effective risk forewarning and management strategies. A few existing researches have concerned the correlation of the extreme weather in different regions of power system, and traditional operation risk assessment methods gradually cannot satisfy real-time requirements. This motivates us to present an intelligent risk forewarning method for the operation of power systems considering multi-region extreme weather correlation. Firstly, a novel multi-region extreme weather correlation model based on vine copula is developed. Then, a risk level classification method for power system operations is introduced. Further, an intelligent risk forewarning model for power system operations is proposed. This model effectively integrates the multi-region extreme weather correlation and the risk level classification of the system. By employing the summation wavelet extreme learning machine, real-time monitoring and risk forewarning of the system’s operational status are achieved. Simulation results show that the proposed method can rapidly identify potential risks and provides timely risk forewarning information, helping enhance the resilience of power system operations.

Continuum Logic of Control Signals in Analog Cyber–Physical Nets

The use of embedded processors is the most promising direction in the development of automatic control systems. The article is devoted to analog models and technical solutions that allow continuous analysis of information in a technical system in order to synthesize control signals. Technical solutions are obtained on the basis of continuum logic methods, which aim to increase the speed of embedded computing networks, reduce power consumption, and unify the element base of analog processors. The effect of high speed is achieved due to the transition from sequential digital calculations to parallel synthesis of analog control signals. Examples of the implementation of schemes for the synthesis of control commands using the developed models of logical operations are given.

The power of computational thermochemistry in high-temperature process design and optimization: Part 1 — Unit operations

Industrial process modeling is increasingly accessible through computational chemistry packages. Computational Thermochemistry (CT) is particularly convenient for exploring the behavior of high-temperature processes (e.g., pyrometallurgical unit operations such as calciners, roasters, smelters, converters, and electric arc furnaces) since their operating conditions are mostly dictated by local/global thermodynamic phase equilibria. Under these high-temperature conditions, energy barriers are small and do not limit the kinetics of many chemical reactions. In this context, engineers-in-training must take full advantage of CT to explore and understand current unit operations in high-temperature manufacturing technologies. This work illustrates the strength of computational thermochemistry for high-temperature modeling through four case studies, i.e., 1. a carbo-reduction process, 2. a glass production/recycling furnace, 3. an aluminothermic reactor for the production of a ferroniobium alloy, and 4. a titanium purification unit. Moreover, the relevance of key fundamental thermodynamic concepts is discussed through the modeling of these unit operations. All the thermodynamic simulations presented in this work were performed using FactSage, a metallurgy-specialized thermochemical package widely employed in both academia and industry.

MODELING TRAFFIC OPERATIONS AT A TOLL PLAZA USING DISCRETE-EVENT SIMULATION

The objective of this study is to develop a discrete-event simulation model of traffic operations at a toll plaza to compare the performance measures between the manual and electronic toll collection (ETC) systems. This study also attempts to simulate the scenarios where the number of servers in these systems is reallocated to improve the overall operational performance. The field data were collected at a toll plaza on an expressway on-ramp during a PM peak period via video recording. The collected data were summarized to determine the characteristics of parameters as inputs for the simulation model. The simulation model was developed in EZStrobe, while the results were carried out via STROBOSCOPE. The simulation model was calibrated with the field data prior to making modifications and simulating various scenarios. The results show that, under the existing conditions, switching an electronic tollgate to a manual tollgate reduces the average overall queue delay and queue length by 71% and 78%, respectively. After reallocation, the average queue delays in these systems equalize as the proportion of ETC users reaches 72%. The additional throughput time of 0.4 s/veh in the ETC system is small compensation for the 6.8-second deduction in the average overall throughput time. In conclusion, shortening the queue lengths at the toll plaza by efficiently utilizing the existing tollgates is possible for improving the overall operational performance of the toll plaza without the additional tollgate installation.

Ergonomic Evaluation and Modelling of Sawmill operations: Theoretical Framework and Research Directions

Ergonomic Evaluation and Modelling of Sawmill operations: Theoretical Framework and Research Directions

A comprehensive study of agent-based airport terminal operations using surrogate modeling and simulation

Airport terminals are complex sociotechnical systems, in which humans interact with diverse technical systems. A natural way to represent them is through agent-based modeling. However, this method has two drawbacks: it entails a heavy computational burden and the emergent properties are often difficult to analyze. The purpose of our research is therefore to accurately abstract and explain the dynamics of airport terminal operations by means of computationally efficient and interpretable surrogate models, based on an existing detailed agent-based simulation model. We propose a methodology consisting of two stages. Stage I involves the development of faithful surrogates. A sample is collected according to an active learning strategy, upon which Gaussian process regression, higher-order polynomials, gradient boosting, and random forests are fitted. Stage II then applies state-of-the-art techniques from the emerging field of explainable artificial intelligence to interpret and understand these models. Both model-agnostic and model-specific methods are considered, and their results are synthesized in order to explain the emergent properties. We prove the efficacy of this approach by conducting two case studies on AATOM, an existing Agent-based Airport Terminal Operations Model. The first case study examines the total expenditure on discretionary activities, such as shopping and dining. A combination of poor staffing strategies and high occupancy rates on certain flights was found to disrupt the terminal journey of passengers on subsequent flights. As a result of these knock-on phenomena, less free time is left for discretionary activities, which has a negative effect on the total expenditure. The second case study examines the throughput of security checkpoints. While throughput increases with passenger numbers, a clear point was observed where the checkpoint reaches its maximum capacity. This leads to longer queues and therefore higher waiting times. It even goes so far as to put passengers at risk of missing their flight, especially with poor staffing strategies. Altogether, we clearly observed the preservation of emergent phenomena in surrogate models, and conclude that their combination with interpretable machine learning is an effective way to explain the dynamics of complex sociotechnical systems.

Evaluating draft Environmental Protection Agency emissions models for broiler operations

Evaluating draft Environmental Protection Agency emissions models for broiler operations

Application of a generative adversarial network for multi-featured fermentation data synthesis and artificial neural network (ANN) modeling of bitter gourd–grape beverage production

Artificial neural networks (ANNs) have in recent times found increasing application in predictive modelling of various food processing operations including fermentation, as they have the ability to learn nonlinear complex relationships in high dimensional datasets, which might otherwise be outside the scope of conventional regression models. Nonetheless, a major limiting factor of ANNs is that they require quite a large amount of training data for better performance. Obtaining such an amount of data from biological processes is usually difficult for many reasons. To resolve this problem, methods are proposed to inflate existing data by artificially synthesizing additional valid data samples. In this paper, we present a generative adversarial network (GAN) able to synthesize an infinite amount of realistic multi-dimensional regression data from limited experimental data (n = 20). Rigorous testing showed that the synthesized data (n = 200) significantly conserved the variances and distribution patterns of the real data. Further, the synthetic data was used to generalize a deep neural network. The model trained on the artificial data showed a lower loss (2.029 ± 0.124) and converged to a solution faster than its counterpart trained on real data (2.1614 ± 0.117).