Scheduling Control Research Articles

Гибель и травмирование людей при пожарах на предприятиях промышленности с учетом их категории риска

The regulatory & legal and scientific & methodological aspects of developing risk-oriented approaches in the Russian Federation have been considered. The procedure and criteria for classifying protected facilities as a certain risk category in the field of fire safety are determined by the resolutions of the Government of the Russian Federation of October 12, 2020, № 1662, and September 14, 2023, № 1502. In the framework of the study, research to evaluate the efficiency of risk-oriented approaches in the industry has been conducted. For the period 2020–2022, the highest number of fires (33 %) in the Russian Federation occurred at the facilities not subject to classification. 25 % of fires occurred at facilities classified as low-risk, whereas only 2 % of fires occurred at high and extremely high-risk facilities. In order to evaluate the efficiency of rescue operations by firefighting divisions, the index «proportion of enterprise employees rescued during fires as compared with the total numbers of fatalities and rescued persons» has been applied. Based on the analysis, it has been concluded that the personnel of industrial facilities classified as low risk or not subject to classification have the least chance to escape the area exposed to fire hazards, 54 % and 57 %, respectively. The index of «proportion of injured persons during fires as compared with the total numbers of injured persons and fatalities» enables us to conclude that persons within the area exposed to fire hazardous factors survive but can be injured, which is damage to health. Persons within the area exposed to fire hazardous factors at low-risk facilities have the least chance of surviving. i.e., 46 %, whereas for the high-risk facilities, this index is 90 %. The probability of exposure to fire hazardous factors is to be evaluated by the relation of number of victims to the total number of exposed to the risk hazardous factors (evacuated, rescued persons, and fatalities). The highest probability of a fatality during a fire has been obtained for the personnel of low-risk category facilities: 85 fatalities per 1000 employees, whereas for high-risk faculties, the probability of fatalities is an order of magnitude less. The analysis of the results of social consequences of fire at industrial facilities indicates a need to enhance the role of occupational safety services at enterprises to implement measures of fire prevention, especially at low-risk industrial enterprises where the state fire supervision agencies take no scheduled control (supervision) measures.

Co-Design of Bandwidth-Aware Communication Scheduler and Cruise Controller for Multiple High-Speed Trains

Co-Design of Bandwidth-Aware Communication Scheduler and Cruise Controller for Multiple High-Speed Trains

Optimizing Software Effort Estimation Models Using Enhancement of Firefly Algorithm

Abstract: Estimating the amount of work required in order to create software development is regarded as essential to the software development life cycle and to the control of project costs, schedules, and quality. As a result, precise estimation plays a critical role in project success and risk mitigation. Software effort estimation has drawn a lot of interest from scholars recently and presented a problem to the software business. Three COCOMO-based models' parameters are to be optimized using a metaheuristic method called improvement of Firefly Algorithm, which is proposed in this research. The basic COCOMO model and the other two models that have suggested in literature as expansions of basic COCOMO model are among these models. Root Mean Square Error (RMSE) is one of the evaluation measures used to assess the created estimate models.

Decentralized Scheduling and Dispatch Control for the Traditional Labor-Intensive Assembly System

The fourth industrial revolution has broadly transformed the manufacturing system. However, this transformation is somewhat lacking in traditional or manual production systems due to the absence of IT infrastructure. Such traditional industries need to have the advantage of real-time control and monitoring. This study has developed economic assembly planning, scheduling, and control for a traditional assembly system. We used the concept of the configurable virtual workstation as the digitalization framework. Then, we employed the decentralized scheduling concept to reduce the computational effort in scheduling the complex product. The implementation result showed that scheduling and planning have transformed the traditional assembly process into intelligent scheduling and control with low digitalization effort

Optimized Forecasting Approach for Scheduling Wind Generation Plants and Maximizing Renewable Energy Utilization

This study focuses on optimizing wind generation plant scheduling to enhance renewable energy utilization while minimizing costs and ensuring grid stability. An advanced forecasting approach is proposed to predict wind generation plant availability for future intervals, integrating accurate demand prediction with renewable energy forecasting. Evaluation metrics like Mean Error (ME), Root Mean Square Error (RMSE), and Correlation assess the proposed solution’s performance, showing its superiority over baseline models with an RMSE of 9.39. Results highlight the effectiveness of the approach in forecasting wind generation plant availability, essential for efficient renewable energy utilization. By reducing reliance on traditional energy sources, the method contributes to cost minimization and promotes sustainability. The study underscores the potential of advanced forecasting techniques in optimizing renewable energy scheduling, offering opportunities for grid stability and reduced environmental impact. The proposed method involves training an LSTM model on preprocessed wind turbine data, integrating it with a scheduling algorithm for optimized task scheduling, and continuously learning to improve efficiency. The Optimized LSTM model outperforms other models in wind power forecasting, providing accurate predictions crucial for energy management decisions. Integration with SCADA control and maintenance scheduling further enhances operational efficiency and cost reduction in wind turbine management.

Distributed Optimization of Islanded Microgrids Integrating Multi-Type VSG Frequency Regulation and Integrated Economic Dispatch

The question of how to simultaneously perform frequency regulation and integrated economic scheduling for microgrids with low-inertia islanding operation under communication constraints is a difficult problem that needs to be solved for many current applications. To this end, this paper establishes a microgrid scheduling control model containing a virtual synchronous generator (VSG) with multiple types of power sources and proposes a distributed optimization algorithm that integrates frequency regulation and comprehensive economic scheduling to simultaneously realize frequency regulation and economic scheduling in a microgrid. Firstly, a distributed economic dispatch problem is proposed based on a comprehensive consideration of the costs and benefits of various types of power VSGs, as well as the overall inertia and standby capacity requirements of the microgrid, which minimizes the integrated costs incurred by the participation of each type of VSG in the frequency regulation and improves the stable operation of the microgrid in terms of frequency under perturbation. Then, the optimal scheduling problem is solved by reconstructing the optimization problem based on considering the dynamic characteristics of microgrid inverters and using event-triggered communication to sense and compensate for the supply-demand imbalance online. The proposed method can avoid inter-layer coordination across time scales, improve the inertia, frequency regulation capability, and economy of the system, and enhance its robustness to short-term communication failures. Finally, simulation results are used to verify the effectiveness of the method.

Petri Net Model Predictive Control Method for Batch Chemical Systems

In order to address the problem of the real-time scheduling and control of batch chemical systems, this work proposes a model predictive control method based on Petri nets. First, a method is presented to construct a batch chemical system’s timed Petri net model. Second, a control structure is designed to augment the Petri net model to control the valves. This results in timed Petri nets that formally represent the process specifications of a batch chemical system. Third, a model predictive control method is developed to schedule and control timed Petri nets, where a proposed heuristic function is utilized to perform the optimization computation. The model parameters are dynamically adjusted using online data, and both scheduling and valve control instructions are calculated in real time. Finally, a series of experiments is carried out in a beer canning plant to verify the proposed method. According to the experimental results, the scheduling and control problem can be solved in real time, where the online computations can be performed in milliseconds, and the resulting scheduling strategies are optimal or near-optimal.

Iterative Reconstruction of Micro Computed Tomography Scans Using Multiple Heterogeneous GPUs.

Graphics processing units (GPUs) facilitate massive parallelism and high-capacity storage, and thus are suitable for the iterative reconstruction of ultrahigh-resolution micro computed tomography (CT) scans by on-the-fly system matrix (OTFSM) calculation using ordered subsets expectation maximization (OSEM). We propose a finite state automaton (FSA) method that facilitates iterative reconstruction using a heterogeneous multi-GPU platform through parallelizing the matrix calculations derived from a ray tracing system of ordered subsets. The FSAs perform flow control for parallel threading of the heterogeneous GPUs, which minimizes the latency of launching ordered-subsets tasks, reduces the data transfer between the main system memory and local GPU memory, and solves the memory-bound of a single GPU. In the experiments, we compared the operation efficiency of OS-MLTR for three reconstruction environments. The heterogeneous multiple GPUs with job queues for high throughput calculation speed is up to five times faster than the single GPU environment, and that speed up is nine times faster than the heterogeneous multiple GPUs with the FIFO queues of the device scheduling control. Eventually, we proposed an event-triggered FSA method for iterative reconstruction using multiple heterogeneous GPUs that solves the memory-bound issue of a single GPU at ultrahigh resolutions, and the routines of the proposed method were successfully executed on each GPU simultaneously.

An Advanced Multi-Objective Ant Lion Algorithm for Reservoir Flood Control Optimal Operation

Multi-objective reservoir operation of reservoir flood control involves numerous factors and complex model solving, and exploring effective methods for solving the operation models has always been a hot topic in reservoir optimization operation research. The Multi-Objective Ant Lion Algorithm (MOALO) is an emerging heuristic intelligent optimization algorithm, but it has not yet been applied in reservoir optimization operation. Testing the effectiveness of this method on multi-objective reservoir scheduling and further improving the optimization performance of this method is of great significance for enhancing the overall benefits of reservoir operation. In this study, MOALO is applied to the optimal scheduling of reservoir flood control. To increase the search efficiency of MOLAO, the advanced MOALO method (AMOLAO) is proposed by reconstructing the search distribution in MOALO using a power function. Taking the Songshu Reservoir and Dongfeng Reservoir in the Fuzhou River Basin in Dalian City as an example, MOALO, AMOLAO, and other two traditional methods are applied for solving the multi-objective reservoir operation problem. Results show that the AMOALO method has high search efficiency, strong optimization ability, and good stability. AMOALO performs better than MOALO and the two traditional methods. The study provides an efficient method for solving the problems in multi-objective reservoir operation.

Application of Trapezoidal Fuzzy Numbers in the Inventory Problem of Decision Science

The objectives of this research study focus on managing production networks efficiently through a systematic and integrated approach. The central idea is to combine inventory control and transportation scheduling to optimize the entire supply chain. The study introduces the concept of the overtime fuzzy inventory-transportation problem (FITP), specifically aiming to determine an optimal distribution plan that considers overtime to minimize total distribution costs. The use of trapezoidal fuzzy numbers (TrFN) is highlighted as a key methodology for addressing uncertainty and incomplete information in real-world problems. TrFNs are considered a generalization of crisp and fuzzy numbers, providing a flexible framework for modelling imprecision. The process of de-fuzzification is mentioned as a means to convert fuzzy numbers into crisp numbers for practical applications, thereby obtaining clear and precise solutions.

Hardware Schemes for Smarter Indoor Robotics to Prevent the Backing Crash Framework Using Field Programmable Gate Array-Based Multi-Robots.

The use of smart indoor robotics services is gradually increasing in real-time scenarios. This paper presents a versatile approach to multi-robot backing crash prevention in indoor environments, using hardware schemes to achieve greater competence. Here, sensor fusion was initially used to analyze the state of multi-robots and their orientation within a static or dynamic scenario. The proposed novel hardware scheme-based framework integrates both static and dynamic scenarios for the execution of backing crash prevention. A round-robin (RR) scheduling algorithm was composed for the static scenario. Dynamic backing crash prevention was deployed by embedding a first come, first served (FCFS) scheduling algorithm. The behavioral control mechanism of the distributed multi-robots was integrated with FCFS and adaptive cruise control (ACC) scheduling algorithms. The integration of multiple algorithms is a challenging task for smarter indoor robotics, and the Xilinx-based partial reconfiguration method was deployed to avoid computational issues with multiple algorithms during the run-time. These methods were coded with Verilog HDL and validated using an FPGA (Zynq)-based multi-robot system.

Research on Adaptive Cycle Engine Mode Transition Control Method

Mode transition is an important dynamic process of an adaptive cycle engine (ACE). In order to obtain a smooth mode transition process, the closed-loop controller is designed based on the strong robust augmented linear quadric regulator (ALQR) method, and with the objective of minimizing the thrust fluctuation in the process of mode transition, an open-loop geometrical mechanism control schedules optimization method based on Bézier curves is proposed, so that the closed-loop control and the open-loop control can work in coordination. The simulation results at the subsonic cruise operating point and supersonic cruise operating point show that based on the optimized open-loop geometrical mechanism control schedules and the designed closed-loop ALQR control system, the ACE achieves fast and smooth geometrical mechanisms and engine output transition during the mode transition process with a maximum thrust fluctuation of 2.58%, which is much smaller than that of the traditional linear variation geometric mechanisms with a maximum 4.64% thrust fluctuation, which verifies the effectiveness of the proposed control method.

Optimal charge scheduling and on-board control of an urban electrified BRT fleet considering synthetic representative driving cycles

This paper presents a comprehensive approach for optimal charge scheduling and on-board vehicular control of electrified fleets based on synthetic driving cycles. The proposed approach is conducted within a real case-study in Cairo, Egypt, whereto a representative distance-based driving cycle has been synthesized using K-means clustering over a sliding horizon of gathered data-sets. Two multi-objective problems defining optimal charge scheduling and vehicular control have been formulated to achieve minimal energy consumption and operating cost of the fleet . Non-dominant genetic algorithm (NSGA-II) has been implemented to solve the optimization problems jointly considering fluctuating electricity cost of the grid. The comparative evaluation of results reveals an improvement of 19% and 28% in energy consumption and retention of on-board energy accordingly, with less than 2% mitigation of driveability. Moreover, a reduction of 40.8%, 20%, and 21.9% in fleet size, required charging stations, and annual recharging cost respectively has been realized. The main innovation of this work can be put forward as the ability to address the above-mentioned quadrilateral objectives of electrified fleets in a single comprehensive approach, considering synthetic driving cycles and electricity prices to yield a customized-optimal solution.

Effective Patient Scheduling

In the physician’s office, the schedule itself is the most powerful and influential aspect of the physician’s day — and it can control you or you can control it. This article offers a few thoughts on keeping control of your schedule and making it work for you. In addition, it also offers an Action Step that will assist you in developing a plan to improve patient flow.

Optimization Method of RTOS System Delay Scheduling Based on Multi-Core Processor

In order to improve the task execution efficiency of multi-core processor, this paper studies the system delay scheduling optimization method of real-time operating system based on multi-core processor. Firstly, the core structure analysis model of multi-core processor data fusion and data acquisition in RTOS system is established, and then the task information flow of delay scheduling in RTOS system is constructed by using the correlation dimension feature fusion method. Based on the power domain non orthogonal multiple access control method, the data fusion state factor of multi-core processor is calculated for the initial scheduling node, and multi-core processor is used to control load balancing in the delay balancing scheduling process of RTOS system, The delay scheduling and balance control of real-time operating system are realized by multi-core processor. Simulation results show that the algorithm has low bit error rate and low task overhead in RTOS system, and can improve the task execution efficiency of multi-core processor.

Dynamic Control and Time-Delayed Channel Scheduling Co-Design for Voltage Control in Active Distribution Networks

Dynamic Control and Time-Delayed Channel Scheduling Co-Design for Voltage Control in Active Distribution Networks

Development of a prediction-based scheduling control strategy with V2B mode for PV-building-EV integrated systems

Extending the energy boundary from the building-integrated photovoltaic to E-mobility is an effective alternative to further improve the integrated energy system performance. In this study, a new prediction-based scheduling control (PSC) strategy was developed considering vehicle-to-building mode for PV-building-EV integrated systems. The control strategies consisted of three components, model predictive control (MPC), simple operation mode, and schedule control. First, the MPC was based on the weather forecast, while the calculated daily renewable power generation and building energy consumption were used for mode selections. Second, the simple operation mode was based on system operation using conventional methods. Third, the developed scheduling control strategy was activated based on the periods of the day, involving different operating modes (e.g., V2B mode, renewable-to-building mode, etc.). Overall, the developed PSC can effectively improve energy performance and achieve cost savings. Validation tests were conducted for an office building, whose results showed higher renewable energy penetration and renewable power use efficiency at 9.56% and 30.48%, respectively. The levelized cost of energy (LCOE) values for building energy consumption and EV charging decreased to 0.4561 CNY/kWh and 0.6304 CNY/kWh, respectively. It indicated that the annual cost savings of building energy consumption and EV charging decreased by 16.5% and 42.7%, respectively.

A Temporal Deep Q Learning for Optimal Load Balancing in Software-Defined Networks.

With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. Most proposed solutions in the literature are based on the static deployment of multiple controllers without the consideration of flow fluctuations and traffic bursts, which ultimately leads to a lack of load balancing among controllers in real time, resulting in increased network latency. Moreover, some methods addressing dynamic controller mapping in multi-controller SDNs consider load fluctuation and latency but face controller placement problems. Earlier, we proposed priority scheduling and congestion control algorithm (eSDN) and dynamic mapping of controllers for dynamic SDN (dSDN) to address this issue. However, the future growth of IoT is unpredictable and potentially exponential; to accommodate this futuristic trend, we need an intelligent solution to handle the complexity of growing heterogeneous devices and minimize network latency. Therefore, this paper continues our previous research and proposes temporal deep Q learning in the dSDN controller. A Temporal Deep Q learning Network (tDQN) serves as a self-learning reinforcement-based model. The agent in the tDQN learns to improve decision-making for switch-controller mapping through a reward-punish scheme, maximizing the goal of reducing network latency during the iterative learning process. Our approach-tDQN-effectively addresses dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. A multi-objective optimization problem for flow fluctuation is formulated to divert the traffic to the best-suited controller dynamically. Extensive simulation results with varied network scenarios and traffic show that the tDQN outperforms traditional networks, eSDNs, and dSDNs in terms of throughput, delay, jitter, packet delivery ratio, and packet loss.

Prediction of Water Content in Subgrade Soil in Road Construction Using Hyperspectral Information Obtained through UAV

In road construction, the compaction of the subgrade layer, which is one of the earthwork fields, is an essential procedure to support the pavement layer and traffic load. For the quality control of subgrades, water content must be measured. Currently, the measurement of water content is performed at specific locations in a large area of subgrades and has the disadvantage of taking a long time to derive. Because this is difficult to immediately confirm, inefficiencies arise in terms of construction schedule and quality control. Therefore, in this study, a CCM (Color-Coded Map) was proposed through hyperspectral remote sensing using drones. This method is a range-type water-content measurement method that can acquire data in a short time (about 20 min) and can be easily confirmed visually. For this, a predicted equation that can convert hyperspectral information into water content information is developed. Multivariate linear regression, a machine learning technique, was applied to the database (of actual measured water content and hyperspectral information). The predicted and measured water contents showed a coefficient of determination of 0.888, and it was confirmed that CCMs can also be presented in various ways depending on user settings.

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the inter-vehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.