Optimal Management Method Research Articles

A novel analytical method for optimal management of network congestion caused by electric vehicle charging stations

Congestion management with the emergence of electric vehicle charging stations (EVCSs) can be considered as one of the challenges facing the network operator, whose incorrect management causes the price difference to increase and creates market power. In general, the effect of EVCSs on the congestion rent (CR) of grid lines has been studied in a few studies, which are considered a research gap. In this paper, the share of each EVCS to the variations in line CR and total congestion rent (TCR) is precisely determined. Also, the uncertainty regarding the capacity of the EVCSs is considered to make the problem more real. In addition, the share of other factors as well as marginal, expensive, and cheap units, grid loads, and congested lines are calculated on the CR of grid lines. The proposed method is divided into two parts, in which the share of the five factors to variations in the buses’ locational marginal price (LMP) and the flow of lines is determined in part 1. In part 2, the share of EVCSs to variations in the CR of lines and TCR is computed. The proposed method is implemented using the Kahn-Tucker condition method. This approach is implemented on an IEEE 24-bus system. The analysis of results indicate that the congestion of network lines in the best and worst cases of placing 3 EVCSs in the network increases by 0.0013 % (0.65 $/h) and 0.1 % (48.94 $/h), respectively. Therefore, determining the appropriate placement of the EVCS is of particular importance.

ГНОСЕОЛОГІЯ УПРАВЛІННЯ ТРУДОВИМ ПОТЕНЦІАЛОМ ПІДПРИЄМСТВ В УМОВАХ НЕВИЗНАЧЕНОСТІ

The existence of conditions of uncertainty in the process of managing the labor potential of enterprises gives rise to the need to find optimal management methods and tools for ensuring and combining the available human resources, their knowledge, experience and qualifications with the main components of the enterprise's potential, he strategy and goals of its development, ensuring the company's need for employees with the necessary qualifications and effective use of the labor potential of both individual employees and the team as a whole. A research aim is to learn the process of managing the labor potential, based on the current conditions of uncertainty in the development of enterprises. The object of the research is the process of managing the labor potential of enterprises in different economic conditions. The method of the research are modern methods and technologies of scientific knowledge, methods and techniques of conducting scientific research, tools of systemic, complex, economic analysis, principles of management of enterprises and their labor resources, which allow to review the existing approaches to the management of labor potential and identify directions stabilization of enterprises in modern economic conditions. The hypothesis of the research was an assumption about the possibility to determine the logic of learning the process of managing the labor potential of enterprises in modern conditions of uncertainty. The statement of basic materials. It is proved that epistemology is applied in the research in a purposeful way, as it is a tool of knowledge and a philosophy of managing the labor potential of enterprises in conditions of uncertainty. It has been confirmed that the management of labor potential is a component of the general process of managing the production potential of the enterprise, which means that it makes a direct contribution to the results of operations, the volume and output of products, the creation of certain benefits not only by the economic entity itself, but also contributes to the national income of the country. A certain philosophy of labor potential management is proposed, aimed at developing a special toolkit and acquiring new knowledge that can be used in labor potential management programs in wartime and postwar times. The originality and practical significance of the research is confirmed by the above philosophy of managing the labor potential of enterprises in modern conditions of uncertainty. Conclusions and perspectives of further research. The implemented epistemology of labor potential management made it possible to state that labor potential is a complex system with certain properties that requires the formation of additional requirements both for the assessment of labor potential and for its management. Therefore, the formation of an effective philosophy of labor potential management requires the constant development of personnel, the development of special programs for the management of labor potential based on the existing conditions of uncertainty of enterprises, including war and post-war times. Further research will be aimed at the development of special labor potential management programs, based on the sphere of activity of enterprises and their regional location

Блокада нервов нижней конечности: показания и преимущества

An inadequate approach to the choice of anesthetic care during surgery and in the postoperative period leads to numerous complications, chronic pain, decreased quality of life, as well as to the formation of a negative patient attitude and dissatisfaction with anesthetic care. Currently, there is a constant search for optimal methods of anesthetic management, especially in high-risk patients. Objective. To summarize and present current information on lower limb nerve blockade. Materials and methods. During the research work, an analysis of dissertations on this topic for 2014–2023 was carried out, and Internet resources Elibrary, Cyberleninka, PubMed for the last 9 years were used, since there are not enough literary sources on this topic from 2018 to 2023. Results. An analysis of Russian and foreign literature for 2014–2023 was given, devoted to such a type of anesthetic management as lower limb nerve blockade. Indications, anatomical features, methods, complications in femoral, obturator and sciatic nerve blockades are described. Conclusion. Due to the use of ultrasound navigation, neuromuscular stimulation, catheter technologies, it has become possible to perform peripheral blockades with a minimal risk of complications, as well as to carry out extended peripheral blockades, which allows to speed up the postoperative recovery of patients. Due to the lack of influence of peripheral blockades on hemodynamics, a smaller number of severe complications inherent in central blockades, anesthesiologists increasingly prefer peripheral blockades when choosing a method of anesthetic support for operations on the lower extremities. This type of anesthesia is offered as an alternative to neuraxial blockades, especially in elderly patients with a high class of physical condition.

Energy management optimization of hybrid electric vehicles based on deep learning model predictive control

In this paper, the hybrid electric vehicle (HEV) energy management optimization method is proposed based on deep learning (DL) model predictive control. Through empirical research combined with the questionnaire survey, this article not only provides a new perspective and practical basis but also improves the efficiency and accuracy of the model by improving the relevant algorithms. The study first analyzes the importance of HEV energy management and reviews the existing literature. Then, the optimization method of HEV energy management based on the deep learning model is introduced in detail, including the composition of energy management for hybrid electric vehicles, the structure and working principle of the deep learning model, especially the backpropagation neural network (BPNN) and the convolutional neural network (CNN), and the steps of application of deep learning in energy management. In the experimental part, questionnaire data from 1,500 consumers were used to design the HEV energy management optimization scheme, and consumers’ attitudes and preferences towards HEV energy optimization were discussed. The experimental results show that the proposed model can predict HEV energy consumption under different road conditions (urban roads, highways, mountain areas, suburban areas, and construction sites), and the difference between the average predicted energy consumption and the actual energy consumption is between 0.1KWH and 0.3KWH, showing high prediction accuracy. In addition, the deep learning-based energy management strategy outperforms traditional control strategies in terms of fuel consumption (6.2 L/100 km), battery charge and discharge times (814), battery life, and CO2 emissions, significantly improving the efficiency of HEV energy. These results demonstrate the great potential and practical application value of deep learning models in the optimization of energy management of HEVs, helping to drive the development of more sustainable and efficient transportation systems.

Financial Optimization Management and Equipment Procurement Risk Analysis of New Energy Photovoltaic Power Generation Project

As renewable energy gains momentum and energy structures evolve, photovoltaic power generation projects assume greater significance in the energy sector. This paper aims to explore financial optimization strategies and procurement risks in new energy PV projects, offering countermeasures. Firstly, an in-depth study of PV project finance reveals potential issues during operation. Cost control, income optimization, and capital management strategies are proposed to ensure economic viability and long-term profitability. Secondly, a systematic analysis of equipment procurement risks in PV projects considers factors like market volatility, technological advancements, and supply chain challenges. Comprehensive risk management measures are put forward to mitigate these risks. These measures cover supplier selection, contract management, technical monitoring and other aspects, aiming at minimizing the uncertainty in the procurement process and ensuring the equipment quality and reliability of the project. Finally, based on actual cases, this paper verifies the practicability and effectiveness of the proposed optimal management and risk analysis methods by comparing the effects of different financial management and equipment procurement strategies. Through these studies, this paper aims to provide useful reference for investors, managers and decision makers of new energy photovoltaic power generation projects, and promote them to make more scientific and reasonable financial management and equipment procurement decisions in the process of project implementation.

КЛАСИФІКАЦІЯ ВІДХОДІВ ЕЛЕКТРИЧНОГО ТА ЕЛЕКТРОННОГО ОБЛАДНАННЯ ЗА ОЗНАКОЮ ЕКОЛОГІЧНОЇ НЕБЕЗПЕКИ

The article discusses the problem of waste electrical and electronic equipment, which is constantly growing due to the mass consumption of equipment and its rapid obsolescence. Data on the volumes and forecasts of e-waste generation in the future, which belong to specific types of waste based on their composition and structure, are provided. The article emphasizes that due to the content of various components made of different materials, including rare, valuable, and hazardous compounds, waste electrical and electronic equipment poses a significant threat to human health and the environment. As a result, there is a need for specialized management of this type of waste. Based on the analysis of the characteristics and properties of the waste, aiming to ensure effective management of this waste and focusing on its ecological hazards, a classification of waste electrical and electronic equipment by ecological hazard is proposed. The proposed waste classification divides them into three categories, depending on the level of potential negative impact on human health and the environment as a result of improper handling, and determines optimal management methods for each category. It is noted that this classification can be used as an auxiliary tool in the 10R waste management hierarchy, which is the basis of the circular economy 3.0, and is described in detail in the article in the context of this type of waste. A separate part of the article is dedicated to comparing the proposed classification of waste with other known classifications of waste electrical and electronic equipment. Both common features and differences are highlighted, emphasizing the greater comprehensiveness of the proposed classification, including the determination of optimal methods for of e-waste management to reduce the negative impact on the environment and extend the life cycle of the equipment.

Revolutionizing energy management: A two-step optimal day-ahead scheduling approach for advanced distribution systems with enhanced transactional efficiency

This study makes a significant contribution to advancing smart energy management systems, crucial for effective control of energy supply and demand. Traditional methods for optimal energy management in power grids often encounter computational challenges due to the nonline ar and discrete nature of the optimization problem. In response, our research introduces a novel two-step Optimal Day-Ahead Scheduling approach for an Advanced Distribution System. This system incorporates Renewable Energy Sources, Distributed Generation units, Electric Vehicles, and Energy Storage Systems, while also facilitating surplus electricity sales to electricity markets. The proposed optimal day-ahead scheduling involves both central and local controllers collaborating to optimize a unified program. The Active Distribution System engages in electricity transactions with multiple descending energy hubs, including Combined Cooling, Heating, and Power plants. Each energy hub (EH) operator optimizes day-ahead scheduling problems, submitting bids to the distribution system operator. This unique approach integrates Demand Load Curtailment and Time-of-Use schemes as demand response programs in descending energy hubs. Addressing the inherent complexity of this optimization challenge, the study introduces a novel path-finding algorithm leveraging an improved local search operator. The application of this proposed method to a 33-bus system demonstrates significant advantages. Results showcase a notable increase of up to 193% in the benefit of selling electricity, coupled with an impressive 84% reduction in distribution system operating costs, considering base values. This contribution highlights the effectiveness of the proposed approach in optimizing energy transactions and enhancing the overall efficiency of distribution system operations.

Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods

The reverse osmosis (RO) process is a well-established desalination technology, wherein energy-efficient techniques and advanced process control methods significantly reduce production costs. This study proposes an optimal real-time management method to minimize the total daily operation cost of an RO desalination plant, integrating a storage tank system to meet varying daily freshwater demand. Utilizing the dynamic model of the RO process, a cascade structure with two reinforcement learning (RL) agents, namely the deep deterministic policy gradient (DDPG) and deep Q-Network (DQN), is developed to optimize the operation of the RO plant. The DDPG agent, manipulating the high-pressure pump, controls the permeate flow rate to track a reference setpoint value. Simultaneously, the DQN agent selects the optimal setpoint value and communicates it to the DDPG controller to minimize the plant’s operation cost. Monitoring storage tanks, permeate flow rates, and water demand enables the DQN agent to determine the required amount of permeate water, optimizing water quality and energy consumption. Additionally, the DQN agent monitors the storage tank’s water level to prevent overflow or underflow of permeate water. Simulation results demonstrate the effectiveness and practicality of the designed RL agents.

Теоретическое осмысление HRM в концепте инновационно-ориентированного менеджмента образовательной организации

The article discusses theoretical-conceptual approaches to the development of personnel management measures in innovation-oriented educational organizations. The purpose of the study is to identify the most relevant theoretical-conceptual approaches to improving the efficiency of management of higher educational institutions at this stage of social development, taking into account the needs of employers and development strategies of the Russian Federation. The study's methodology relied on analytical, comparative, and deductive theoretical approaches, drawing from recent research by both domestic and international scholars focused on examining management practices within different educational institutions. It is analyzed that universities use not only innovative methods, but also strict directive approaches, which provide for the application of sanctions to senior management in case of low performance indicators, up to their replacement with new personnel. It is shown that in an increasingly competitive environment, the concept of building human resource management capacity in an educational organization is to find and retain talented employees in the workplace, as well as to select optimal approaches and methods of human resource management.

Automatic Group Decision-Making for Algal Bloom Management Based on Information Self-Learning

The phenomenon of algal bloom seriously affects the function of the aquatic ecosystems, damages the landscape of urban river and lakes, and threatens the safety of water use. The introduction of a multi-attribute decision-making method avoids the shortcomings of traditional algal bloom management that relies on manual experience. However, the weight-calculation part of this method still receives the artificial influence of human factors, which reduces the accuracy and scientific rigor of the decision. This paper presents a group decision-making method based on information self-learning which makes decision weights automatically clustered and assigned. A general framework of decision-making management is constructed for the algal bloom management process. In the decision-making process, an improved density-based clustering algorithm is used to automatically cluster and rank the decision data in the form of the three-parameter interval number, and ultimately obtain the optimal management method that meets the management objectives. Finally, the method was applied at the monitoring station of Sanjiadian Reservoir in Beijing, China. Based on the treatment objectives and water quality monitoring data of the station, relevant experts were invited to evaluate the management solutions, and the information self-organizing algorithm of this paper was used to automatically rank the decision-making methods, and finally obtain the most suitable management method for the station. Comparison with the water quality data and treatment inputs after the previous man-made selection of treatment options, and discussion among experts, show that the decision-making method is feasible and effective, and contributes to the sustainable treatment of algal blooms.

Energy optimization management of microgrid using improved soft actor-critic algorithm

To tackle the challenges associated with variability and uncertainty in distributed power generation, as well as the complexities of solving high-dimensional energy management mathematical models in mi-crogrid energy optimization, a microgrid energy optimization management method is proposed based on an improved soft actor-critic algorithm. In the proposed method, the improved soft actor-critic algorithm employs an entropy-based objective function to encourage target exploration without assigning signifi-cantly higher probabilities to any part of the action space, which can simplify the analysis process of distributed power generation variability and uncertainty while effectively mitigating the convergence fragility issues in solving the high-dimensional mathematical model of microgrid energy management. The effectiveness of the proposed method is validated through a case study analysis of microgrid energy op-timization management. The results revealed an increase of 51.20%, 52.38%, 13.43%, 16.50%, 58.26%, and 36.33% in the total profits of a microgrid compared with the Deep Q-network algorithm, the state-action-reward-state-action algorithm, the proximal policy optimization algorithm, the ant-colony based algorithm, a microgrid energy optimization management strategy based on the genetic algorithm and the fuzzy inference system, and the theoretical retailer stragety, respectively. Additionally, com-pared with other methods and strategies, the proposed method can learn more optimal microgrid energy management behaviors and anticipate fluctuations in electricity prices and demand.

Integrated Management of Clubroot in Zhejiang Province, China

Clubroot, caused by Plasmodiophora brassicae, is a destructive soil-borne disease significantly harming global Brassica crop production. This study employed the Williams and European Clubroot Differential (ECD) and Williams systems to identify the pathotypes of P. brassicae collected from Hangzhou City, Yuhang District and Quzhou City, Kaihua County in Zhejiang Province. Greenhouse and field trials were conducted to evaluate the effects of plastic film covering and four chemical agents on the growth parameters and clubroot severity of the Chinese cabbage cultivar ‘Granaat’. Potential treatment mechanisms on clubroot were explored through a qPCR analysis of the resting spore density and pH measurement of the soil. Furthermore, treatment with 1-napthaleneacetic acid (NAA), a synthetic auxin, was also evaluated for its potential role in suppressing clubroot. The results indicate that the pathotypes of P. brassicae in the two districts were P1, ECD20/31/12, and P3, ECD20/15/4. While an individual application of plastic film covering could not effectively control clubroot, calcium cyanamid, dazomet and ammonium bicarbonate demonstrated significant efficacy in its management. These three agents significantly reduced the resting spore density in the soil, with calcium cyanamid and ammonium bicarbonate also increasing soil alkalinity. Additionally, ammonium bicarbonate promotes lateral root development in ‘Granaat,’ helping infected plants access adequate water and nutrients. However, NAA exhibited no efficacy in clubroot control. Therefore, sustained lateral root development is crucial for effectively resisting P. brassicae invasion. Considering application costs and environmental friendliness, we propose the field application of ammonium bicarbonate as the optimal method for clubroot disease management in Zhejiang Province.

An applied study of industrial enterprise engineering and management based on human factors engineering

Abstract This paper proposes an innovative management optimization method for the challenges facing the management of industrial enterprises in the context of the current rapid development of the industrial economy. The article combines human factors engineering and lean production theory to optimize the management efficiency and effectiveness of industrial enterprises. Considering the complexity of industrial environments, a personnel identification system and a tracking system are constructed in the study to achieve more effective staff management. To comprehensively assess the management effectiveness, the article combines the fuzzy synthesis method to build an evaluation model for the management effectiveness of industrial enterprises. The empirical study shows that the optimization method significantly improves the productivity: the working hours are reduced from 210 hours to 150 hours, the torso rotational and bending forces are reduced by about 54% and 45%, respectively, and the troops on each joint are reduced by about 59% on average. The overall optimization of the model has an affiliation value of 0.53, indicating that the management effect is evaluated as good. The data fully proved the effectiveness of the management optimization method for industrial enterprises proposed in this paper in increasing productivity and improving working conditions.

DSP‐based energy optimization management of household single‐phase electric energy router with historical and real‐time data

AbstractThe development and use of single‐phase electric energy router (SPEER) makes the household power system operate more reliably and bring economic income at the same time. This paper presents an economic energy optimization management method of SPEER with historical data of previous day and real‐time data in the grid‐connected mode of SPEER, which can be realized by DSP. First, based on the exchange power between the SPEER and the grid, the optimal charging and discharging power of the battery to make up for the energy difference between the previous day and the real time is obtained through the particle swarm intelligence algorithm, and the optimal economic model of SPEER operation is established. Then, on the hardware experimental platform, DSP is used to realize the real‐time optimization, and the optimal economic optimization algorithm proposed in this paper is implemented and verified on hardware experimental platform. The contradiction between the complexity of optimization algorithm and the limitation of hardware resources is solved in the proposed energy management system.

Establishing a dynamic and static knowledge model of the manufacturing cell management system: An active push approach

The workshop operation management (WOM) system contains a large amount of multi-source heterogeneous data, but these data often lack effective correlation, posing challenges in extracting valuable information for achieving unified system modelling at the knowledge level. Due to the lack of knowledge active support in the actual WOM system, managers could only passively rely on their own past experiences. To bridge this gap, the knowledge active push method for optimal operation management of the manufacturing cell (OOMMC) is studied based on knowledge graph (KG). Firstly, a dynamic knowledge model is established according to the knowledge application scenario of manufacturing cell, in which the Local Tangent Space Alignment (LTSA) + k-means algorithm is used for active abnormal warning. Secondly, a static knowledge model based on user needs using feature vocabulary and naive Bayesian classifier is established, where managers can obtain knowledge from the KG based on professional noun keywords or simple query questions. These two models complement and promote each other: the dynamic knowledge model using a service-based collaborative filtering method to push the nearest knowledges in the KG to users, effectively inspiring them to retrieve more relevant knowledge. While the static knowledge model evaluates the effectiveness of previously pushed knowledge by setting the knowledge demand index and providing feedback to the system to ensure accuracy of knowledge push. Finally, an active push simulation system is designed and developed to verify the feasibility and effectiveness of the proposed architecture and key enabling technologies.

A controllable neural network-based method for optimal energy management of fuel cell hybrid electric vehicles

Neural Networks (NNs) can be used for energy management of hybrid vehicles, but they are hard to tune in inference to adapt to different driving conditions. To make the NN-based energy management strategy more flexible, this paper proposes a controllable NN for optimal energy management of fuel cell hybrid electric vehicles. Inspired by the equivalent factor in the Equivalent Consumption Minimization Strategy (ECMS), we introduce an adjustable target variable for the final state as an input to the NN-based strategy. During training, classification and regression networks with single-step and multi-step inputs are considered. An efficient shooting method and an adaptive method are then introduced to realize the precise control of the final state and online parameter adaptation. Simulations of the proposed method and the benchmarking method are carried out in different battery discharge modes. Results demonstrate that the proposed shooting neural classifier can achieve 99.7% fuel optimality of dynamic programming in a similar computational time to the shooting ECMS, and the proposed adaptive neural classifier can adapt to different driving conditions and has better fuel economy than the adaptive ECMS.

Transformation of transverse fractures of the acetabulum in T-shaped fractures and sequential approaches

Transverse acetabular fractures are difficult to reduce and generally associated with poorer clinical outcomes than other types of acetabular fractures. For these fractures affecting both columns, the surgical strategy is subject to controversy and there is currently no optimal method of management. The transformation of these transverse fractures in to a T-shaped fracture makes it possible to make the anterior and posterior components of the fracture independent and to reduce them sequentially. This technique has made it possible to obtain a satisfactory reduction of the fracture in certain cases and gives hope for good long-term functional results. Level of evidenceIV.

Data-Driven Prediction of Students’ Online Learning Needs and Optimization of Knowledge Library Management

Thanks to the advancement of information technology, online learning has become a crucial tool of modern education, and the management of modern education is facing the challenges of how to effectively predict students’ learning needs and how to optimize the management of the knowledge library to support these needs. However, existing data-driven prediction approaches are flawed in handling complex learning environments and timely adapting to changes, so this study attempts to solve these questions by exploring the correlation between learning needs, the extraction of knowledge linkages, and the optimization of knowledge libraries based on rule updates. In our work, a new method was proposed for extracting learning needs and knowledge linkages to more accurately identify and predict students’ learning needs, and a rule-based knowledge library management optimization method was introduced to allow the knowledge library to more flexibly adapt to students’ learning needs and the changes in educational resources. In this way, this study offers a comprehensive and flexible solution for education management via the combination of these two aspects, which not only increases student satisfaction and improves teaching quality but also reduces resource waste and gives students a more personalized and efficient learning experience. Furthermore, the methods and findings of this study could also be used as references for data-driven decision-making in other fields.

The Management of Postoperative Atrial Fibrillation (POAF): A Systematic Review.

Postoperative atrial fibrillation (POAF) refers to new-onset atrial fibrillation (AF) that develops after surgery and is associated with an increased risk of mortality and thromboembolic events. The optimal management and treatment methods for POAF complications are not yet fully established. This systematic reviewaimed to evaluate the various treatment and management approachescurrently available in terms of their suitability, efficacy, and side effects in handling POAF incidence post-surgery. Google Scholar and PubMed electronic databases were searched extensively for relevant articlesexamining the various management techniques currently used to manage POAF and published between 2018 and 2023. Data were collected on the type of surgery the patients underwent, POAF definition period, intervention, and outcome of interest. Following a systematic assessment guided by the inclusion criteria, 10 of the 579 studies retrieved were included in this study, and 293,417 POAF cases were recorded. Three of these studies used different rhythm control and rate control treatments to manage POAF cases, while seven studies used various anticoagulation therapies to manage POAF incidence. For asymptomatic patients within one to three days of surgery, rate control is sufficient to manage POAF, and routine rhythm control is not needed; rhythm control should be reserved for patients who develop complications such as hemodynamic instability. Anticoagulation was performed in patients whose POAF exceededfour days after surgery. Anticoagulation was associated with an increased risk of mortality, stroke, thromboembolic events, and major bleeding in patients who underwent coronary artery bypass graft (CABG) surgery. In contrast, in a few other studies, anticoagulation treatment led to improved outcomes in patients who developed POAF. A wide range of management methods are available for POAF after different types of surgery. However, there is only limited evidence to guide the clinical practice. The data available are mainly retrospective and insufficient to accurately evaluate the efficacy of the various management methods available for POAF. Future research should make efforts to standardize the treatment for this condition.

A novel optimal management method for smart grids incorporating cloud-fog layer and honeybee mating optimization algorithm

As a result of cloud-fog (CF) technology, today's society is experiencing low delay, great performance, improved safety, and lower operating costs. As a result of integrating Smart Grid (SG) into the CF platform, the power supply is of higher quality, and the transmission, distribution, and production of power are secure; management of demand and supply is continuous. The study proposes integrating SG with CF-driven environments to improve source distribution. There are 6 fogs in various areas. Every fog connects to a cluster, and there are 500 smart houses in every cluster. Home energy demands are fulfilled by fogs using various load balancing algorithms (LBAs), which are implemented on Digital Twin Virtual Machines (DTDTVMs) and provided with a fast response time (RT) and processing time (PT). The study proposes a Honeybee Mating Optimization Algorithm to balance loads using improved service broker policies. After comparing the outcomes of the suggested LBA to the particle swarm optimization algorithm (PSO), it can be concluded the suggested LBA performs better in comparison with PSO. All the simulation results and comparisons have been performed on digital twin environment to provide more realistic framework for this study.