Comprehensive Schedule Research Articles

A two-phase robust comprehensive optimal scheduling strategy for regional distribution network based on multiple scenarios

A two-phase robust comprehensive optimal scheduling strategy for regional distribution network based on multiple scenarios

Comprehensive Optimal Network Scheduling Strategies for Wireless Control Systems

Although wireless control is one of the key technologies for future industries, most wireless networks are only used for monitoring. When wireless networks are applied to transmit control commands, the uncertain link qualities and limited network resources may destroy the performance of multi-loop control systems. Hence, it is critical to allocate these resources to optimize the control performance as the network condition changes and plants evolve. This article presents comprehensive optimal scheduling strategies for wireless control systems based on adaptive dynamic programming. First, we propose an effective adaptive dynamic programming scheduling (ADPS) strategy to solve the optimal scheduling problem based on the single-step control performance at runtime while significantly reducing computational complexity. Moreover, to overcome the “short-sightedness” of single-step performance prediction, we extend ADPS to ADPS-m ( m ulti-step prediction), which optimizes multi-step performance by incorporating a longer-horizon evolution of the plants. Furthermore, we propose ADPS-H ( H eterogeneous flow scheduling) to support heterogeneous flows with different data rates and sizes and ADPS-H-m ( m ulti-step prediction for H eterogeneous flow scheduling), which schedules heterogeneous flows in a longer prediction horizon. We prove that all these scheduling strategies can achieve optimality and stability under mild assumptions. Extensive experiments integrating TOSSIM and MATLAB/Simulink are performed to evaluate all of the proposed methods in case studies of four- and ten-loop control systems. The simulation results demonstrate that these strategies can effectively improve the control performance at lower computing costs under both cyber and physical disturbances. Under the noise level of \(-\) 76 dBm, for the four-loop case, ADPS achieves the same control performance as the linear programming while saving 99.5% of the execution time. ADPS-m further improves the control performance by up to 27.0% compared with ADPS at the prediction horizon of 3, and ADPS-H-m improves the performance by up to 32.3% and 8.4% compared with round-robin and ADPS-H, respectively. The ten-loop case indicates the effectiveness and scalability of the proposed approaches.

Robust scheduling of a pulp and paper mill considering flexibility provision from steam power generation

The production scheduling and flexibility provision analysis are of great importance for industrial facilities to provide support for the main power grid. This study proposes a comprehensive scheduling model for an industrial pulp and paper (P&P) mill that considers the flexibility provision from steam power generation. In this model, the internal relationships between the production and consumption of electricity, steam, and pulp in the operational processes of the P&P mill are investigated. Meanwhile, the available flexibility of steam power generation is quantified and further enhanced for participation in the manual frequency restoration reserve (MFRR) market. Furthermore, a robust scheduling model that uses support vector clustering (SVC) technology is proposed to assess the impact of uncertainties in steam temperature and pressure on the available flexibility of steam power generation, thereby reducing the probability of failing to provide the contracted flexibility for MFRR. Simulation results demonstrate that the comprehensive scheduling model significantly increases the available flexibility of steam power generation for MFRR, resulting in a daily revenue increase of $1839.8. Moreover, a balance between the revenue obtained from the MFRR market and the probability of economic penalties incurred for failing to meet the contracted flexibility is achieved by comparing different levels of conservatism in the robust scheduling model.

On the Integration of VLBI Observations to GENESIS into Global VGOS Operations

The upcoming European Space Agency (ESA) satellite mission GENESIS is an Earth-orbiting satellite carrying instruments of all four space geodetic techniques. The onboard transmitter for Very Long Baseline Interferometry (VLBI) will allow the observation of the satellite with VLBI radio telescopes. The objective of this study is to investigate the integration of VLBI observations of GENESIS into the operations of the VLBI Global Observing System (VGOS). Based on both current and foreseeable modern VGOS antenna networks, we consider the realistic observability of both geodetic radio sources and GENESIS. We conduct a comprehensive scheduling and perform extensive simulations of the VLBI observations. We assume that observations of GENESIS are scheduled within regular, geodetic experiments. The integration of GENESIS as an additional source in the scheduling results in a minimal degradation in the geodetic parameter estimation of station positions and dUT1 of less than 0.09 mm and 0.06 μs, respectively. The results suggest to schedule scans of GENESIS at intervals of about 5 min to limit the decrease in the number of observations of geodetic sources to less than 5% with respect to schedules containing only geodetic radio sources. The schedules for 24 h experiments comprise about 150 to 200 scans and 1000 to 5000 observations of GENESIS, depending on the size of the utilized network. The frame tie accuracy between the VLBI and GENESIS frames is assessed in the form of station positions, which are solely estimated from observations of GENESIS. Multiple 24 h experiments are simulated over 52 weeks with assumed session cadences of two to three experiments per week. By stacking the normal equations from three months of experiments, we obtain station position estimates with a precision of less than 10 mm. After 12 months, the repeatabilites are reduced to less than 5 mm.

A comprehensive design schedule for electrosprayed thin films with different surface morphologies

A comprehensive design schedule for electrosprayed thin films with different surface morphologies

Low‐carbon economic schedule of the H2DRI‐EAF steel plant integrated with a power‐to‐hydrogen system driven by blue hydrogen and green hydrogen

AbstractHydrogen direct reduction iron coupled with electronic arc furnace (H2DRI‐EAF) technology, as an important technology for decarbonisation in the iron and steel industry, has the advantages of high electrification and low carbon emissions. However, the large demand for hydrogen in this technology relies significantly on the production of electrolytic hydrogen, leading to a substantial increase in power consumption in the steel production process. Moreover, the use of an unclean power source in electrolytic hydrogen production leads to increases in indirect carbon emissions, reducing the low‐carbon attributes of the technology. This study investigates the integrated flexible operation mode of a steel plant. An illustrating method is utilised for modelling the entire steel production process and power to hydrogen (PtH2) process in detail for the H2DRI‐EAF steel plant, which includes natural gas, photovoltaic, wind power self‐provided power plants, and carbon capture and storage (CCS) systems. A mixed integer linear programming (MILP) model is developed for the comprehensive scheduling of the steel mill. The results of the case studies indicate that by reliably integrating the production of renewable energy and natural gas power plants, the PtH2 system can fully consume the renewable energy output while ensuring the smooth progress of steel production and maximising the reduction of carbon emissions from hydrogen production and the total cost of steel production.

Optimal scheduling of a community Multi-Energy system in energy and flexible ramp markets considering Vector-Coupling storage Devices: A hybrid Fuzzy-IGDT/Stochastic/Robust optimization framework

Community multi-energy systems (CMESs) are emerging as a pivotal solution to address the pressing issues of the current energy crisis and environmental pollution. This paper introduces a comprehensive multi-horizon risk-averse scheduling strategy for CMESs, targeting participation in energy and flexible ramp markets while meeting the demands of electric, natural gas, and hydrogen vehicle charging stations, data centers, and residential and commercial buildings. The proposed CMES incorporates power-to-x vector coupling storage (PtX-VCS) systems, enabling the storage of electricity in various forms to cater to multi-energy demands. Additionally, an integrated demand response (IDR) program, incorporating incentive and load-shifting strategies, is applied to enhance operational efficiency and system flexibility. To manage uncertainties effectively, a hybrid stochastic/robust/fuzzy information gap decision theory (IGDT) method is employed. This approach, formulated as a tri-level optimization model, is transformed into a single-level model using strong duality theory. The results demonstrate a notable reduction in daily operational costs by approximately 5% and 4% through the implementation of the IDR program and PtX-VCS technologies, respectively. Moreover, the CMES’s potential to participate in the flexible ramping market leads to an approximately 7% reduction in total operational costs.

Prediction and scheduling of multi-energy microgrid based on BiGRU self-attention mechanism and LQPSO

To predict renewable energy sources such as solar power in microgrids more accurately, a hybrid power prediction method is presented in this paper. First, the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network (BiGRU) to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results. Subsequently, an improved quantum particle swarm optimization (QPSO) algorithm is proposed to optimize the hyperparameters of the combined prediction model. The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively. In addition, considering the coordinated utilization of various energy sources such as electricity, hydrogen, and renewable energy, a multi-objective optimization model that considers both economic and environmental costs was constructed. A two-stage adaptive multi- objective quantum particle swarm optimization algorithm aided by a Lévy flight, named MO-LQPSO, was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system. This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems. The effectiveness and superiority of the proposed scheme are verified through comparative simulations.

Enhanced bi-level optimal scheduling strategy for distribution network with multi-microgrids considering source-load uncertainties

With the increasing integrations of renewable energy resources into distribution networks (DNs) and microgrids (MGs), the imperative for an effective market scheduling mechanism becomes paramount to enhance the operational safety, reliability, and economic efficiency of distribution grids. Taking advantage of bi-level programming theory, this study meticulously formulates a comprehensive optimization scheduling model for the multi-MGs distribution network. The upper-level optimization objective is to minimize both the operational losses and total costs of the DN. Concurrently, the lower-level optimization pursues the maximization of daily operational revenue for MGs. Recognizing the pervasive impact of the inherent uncertainty associated with renewable energy sources on system safety and reliability, a cutting-edge scenario-based stochastic planning framework is introduced. The methodology integrates a heuristic matrix matching approach to effectively handle the intricate challenges posed by uncertainties from wind and photovoltaic generations. Moreover, in addressing the proposed nonlinear models, a sophisticated method is employed, utilizing the second-order cone relaxation and linearization methods. These methods meticulously transform the upper and lower-level models into second-order cone planning and mixed-integer linear programming issues, respectively. Finally, the proposed methodologies are rigorously scrutinized and validated with intricate case studies, providing a nuanced understanding of their efficacy. The empirical results underscore the theoretical feasibility and superiority of the proposed scheduling scheme. Notably, the operational performance of the DN as well as the economic viability of multiple MGs can also be significantly improved.

An examination of the usefulness of a quantitative appraisal method in nursing human resource management in primary hospital operating rooms: An example of integrated collaborative scheduling.

In recent years, China medical and health services have made great development. However, the management of nursing human resources in operating room of primary hospitals still faces a series of challenges. In the nursing work of operating room, high-quality nursing human resource management is important for improving the efficiency of operating room and ensuring the safety of patients. From January 2022 to December 2022, comprehensive collaborative scheduling and quantitative scoring evaluation methods were carried out in our hospital, and relevant data were collected. The flexible scheduling combined quantitative scoring performance appraisal system and the traditional scheduling plus average distribution performance appraisal system were statistically analyzed and compared in terms of annual surgical cases, annual overtime hours, annual back work hours, annual compensatory rest hours, and average daily working hours. This study was based on 30 medical staff (27 females and 3 males) in the operating room of a primary hospital. The annual operation volume increased by 387 cases compared with before, and the attitudes of patients to the service attitude and preoperative waiting time were significantly improved, reaching more than 95%. In addition, in the survey of surgeons, it was found that their satisfaction with preoperative preparation and operation time was significantly higher than that of the traditional scheduling method, and reached more than 95%. In the survey of nursing staff, it was found that the satisfaction with the traditional scheduling method was about 80%, and the satisfaction directly reached 100% after the comprehensive collaborative scheduling system. Based on the above survey, the satisfaction of nurses, doctors and patients with the new comprehensive collaborative scheduling system has improved compared with before. After the implementation of the comprehensive collaborative scheduling system, the annual surgical volume has increased significantly, and the average daily working hours of nursing staff have decreased. Comprehensive collaborative scheduling is an effective method of nursing human resource management in operating room, which can effectively improve the work efficiency of nurses and the satisfaction of patients, doctors and nurses. In practice, this method needs to be continuously explored and refined to adapt to different application scenarios and requirements.

Implementation of Real-Time Bus Tracking and Passenger Information System for MSRTC Mr. Vikrant Talole, Mr. Prasad Sonavane, Miss. Snehal Saykar

The "Real-Time Bus Tracking and Passenger Information System for Maharashtra State Road Transport Corporation (MSRTC)" is a cutting-edge technological solution designed to revolutionize the public transportation experience. In a world where efficiency, accuracy, and real-time information are paramount, this system stands as a beacon of innovation. The system offers real-time tracking capabilities, allowing passengers to monitor the exact location of MSRTC buses in real-time through intuitive mobile applications and web interfaces. Leveraging GPS technology, this feature not only ensures accurate tracking but also enables precise predictions of bus arrival times at designated stops. Passengers gain access to comprehensive route information, including stops, schedules, and landmarks, enhancing their ability to plan journeys effectively. The Real Time Bus Tracking and Passenger Information System for MSRTC stands as a testament to the power of innovation, ushering in a new era of smart public transportation.

A study of comprehensive resource scheduling under public health events: An improved heuristic quantum algorithm

This study explores the impact of public health events, multi-modal projects, multi-project environments, and multi-capacity resource constraints on project scheduling. It describes the comprehensive resource-constrained project scheduling problem (MCMRCMPSP) specifically for public health events, and proposes two approaches for modelling and solving the problem. The objective is to enhance the practical relevance of project scheduling and enrich the problem itself. To improve efficiency and the algorithm for scheduling problems, an enhanced quantum algorithm based on the quantum particle swarm algorithm (QPSO) is proposed. The enhancements include Gaussian variation and a tournament selection strategy. Furthermore, the article integrates multiple heuristic rules with the algorithm to minimize illogical computations, improve computational efficiency, and enhance solution quality. The proposed algorithm’s effectiveness is validated through performance tests and practical application experiments. The results show that the algorithm has superior convergence performance and solution accuracy compared with the traditional QPSO, particle swarm algorithm (PSO), genetic algorithm, ant colony algorithm, and cuckoo algorithm. Thus, the algorithm provides a targeted resource scheduling plan for real-world cases. This research contributes to the field of project scheduling problems and proposes a new solution.

Multi-objective comprehensive container scheduling and resource allocation for container cloud using tuna swarm optimization algorithm

The paper addresses the optimization challenges in cloud resource task execution within the container paradigm, introducing the Multi-Objective Comprehensive Container Scheduling and Resource Allocation (MOCCSRA) scheme. It aims to enhance cost-effectiveness and efficiency by utilizing the Tuna Swarm Optimization (TSO) technique to optimize task planning and resource allocation. This novel approach considers various objectives for task scheduling optimization, including energy efficiency, compliance with service level agreements (SLAs), and quality of service (QoS) metrics like CPU utilization, memory usage, data transmission time, container-VM correlation, and container grouping. Resource allocation decisions are guided by the VM cost and task completion period factors. MOCCSRA distinguishes itself by tackling the multi-objective optimization challenge for task scheduling and resource allocation, producing non-dominated Pareto-optimal solutions. It effectively identifies optimal tasks and matches them with the most suitable VMs for deploying containers, thereby streamlining the overall task execution process. Through comprehensive simulations, the results demonstrate MOCCSRA’s superiority over traditional container scheduling methods, showcasing reductions in resource imbalance and notable enhancements in response times. This research introduces an innovative and practical solution that notably advances the optimization field for cloud-based container systems, meeting the increasing demand for efficient resource utilization and enhanced performance in cloud computing environments.

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.

Day-Ahead scheduling of rural integrated energy systems based on distributionally robust optimization theory

The integration of diverse industries in rural areas is an important step in building a smart countryside and achieving rural revitalization. Aiming at the problem of high energy consumption and pollution in agricultural production parks under the traditional energy structure, a new rural comprehensive energy park distribution robust scheduling model for water containing product breeding greenhouses is proposed in this paper. Firstly, a greenhouse model for aquaculture based on the principle of thermal flow is established. Secondly, considering energy production, consumption and storage, a rural integrated energy system model is established. In response to the uncertainty of residential electricity load, solar radiation, and environmental temperature within the park, a distributed robust optimization model is established using Kullback Leibler divergence. Finally, a numerical simulation is conducted using energy data from a real rural aquaculture park. The results show that compared to agricultural production parks under traditional energy models, the new rural comprehensive energy system model proposed in this paper fully integrates rural resources, reduces daily operating costs by 74.832 %, and reduces total carbon emissions by 33.75 %, providing theoretical support for rural industrial structure adjustment and energy transformation.

Soft computing implementations for evaluating Los Angeles abrasion value of rock aggregates from Kütahya, Turkey

The Los Angeles abrasion value (LAAV) of rocks is a critical mechanical aggregate property for designing road infrastructures and concrete quality. However, the determination of this critical aggregate property is labour-intensive and time-consuming and thus, in the literature, there are many predictive models to estimate the LAAV for different rock types. However, most of them are based on classical regression analyses, limiting their broader usage. In this study, several soft computing analyses are performed to develop robust predictive models for the evaluation of LAAV of rocks in the Ilıca region (Kütahya – Turkey). The main motivation for implementing soft computing analyses is that precise predictive models might be useful when exploring suitable rock types that are manufactured in crushing–screening plants. For this purpose, a comprehensive laboratory schedule was established to obtain some inputs for the evaluation of LAAV. As a result of the soft computing analyses, four robust predictive models are developed based on artificial neural networks (ANN), multiple adaptive regression spline (MARS), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) methodologies. The performance of the proposed models is investigated by some statistical indicators such as R2 and RMSE values and scatter plots. As a result, the ANFIS-based predictive model turns out to be the best alternative to estimate the LAAV of the investigated rocks.

Enhanced Real-time Scheduling of AVB Flows in Time-Sensitive Networking

Time-Sensitive Networking (TSN) realizes high bandwidth and time determinism for data transmission and thus becomes the crucial communication technology in time-critical systems. The Gate Control List (GCL) is used to control the transmission of different classes of traffic in TSN, including Time-Triggered (TT) flows, Audio-Video-Bridging (AVB) flows, and Best-Effort (BE) flows. Most studies focus on optimizing GCL synthesis by reserving the preceding time slots to serve TT flows with the strict delay requirement, but ignore the deadlines of non-TT flows and cause the large delay. Therefore, this paper proposes a comprehensive scheduling method to enhance the real-time scheduling of AVB flows while guaranteeing the time determinism of TT flows. This method first optimizes GCL synthesis to reserve the preceding time slots for AVB flows, and then introduces the Earliest Deadline First (EDF) method to further improve the transmission of AVB flows by considering their deadlines. Moreover, the worst-case delay (WCD) analysis method is proposed to verify the effectiveness of the proposed method. Experimental results show that the proposed method improves the transmission of AVB flows compared to the state-of-the-art methods.

Enhanced Multiobjective Optimization Algorithm for Intelligent Grid Management of Renewable Energy Sources

Optimal scheduling of microgrids (MGs) is a crucial component of smart grid optimization, playing a vital role in minimizing energy consumption and environmental degradation. However, existing methods tend to consider only a single optimization and do not consider the multiobjective optimization problem of MGs in a comprehensive and integrated way. This study proposes a comprehensive multiobjective optimal scheduling methodology for renewable energy MGs, incorporating demand-side management (DSM) considerations. Initially, a DSM multiobjective optimization model is formulated, focusing on the load shifting of controllable devices within the MG to refine the electricity consumption structure. This model contemplates the renewable energy consumption of the MG, customer electricity purchase costs, and load smoothness. Subsequently, a multiobjective optimization model for grid-connected MGs, encompassing wind and photovoltaic power generation, is constructed with the dual objectives of economic and environmental optimization for the MG. Ultimately, a multimodal multiobjective optimization algorithm, amalgamating a local convergence index and an environment selection strategy, is proposed to solve the model. The experimental results show that compared with other methods, the proposed method in this paper can reduce the integrated cost by 32.6% and 38.9% in summer and 19.4% and 40.2% in winter. This stands out as a unique contribution in the field of MG optimization, as it integrates DSM considerations into a multiobjective optimization model. This methodology achieves a balance between minimizing energy consumption and environmental degradation while also enhancing economic efficiency.

The Implementation Practices of the Teacher Induction Program in the Secondary Schools of Department of Education Davao City Division: A Case Study

Boarding on the transfor mative journey of a teaching career, educators encounter a myriad of challenges that shape their early experiences in the classroom. Recognizing the pivotal nature of these initial years in a teacher’s career, there arises a critical need to explore avenues that provide systematic support and enhance the professional development of educators. This research delved into the exploration of the implementation practices of the Teacher Induction Program (TIP) in public secondary schools in Davao City. Employing a qualitative case study design, the study utilized purposive sampling to select seven Master Teachers actively involved in TIP implementation. The research instrument, a researcher-made interview guide, was validated and enhanced by three Master Teachers, and results were based on key themes and core ideas derived from the discussions. The major themes identified for the implementation practices were structured learning, expert guidance and support, immersion and integration, knowledge and orientation, practice and feedback, and implementation gaps. Additionally, major themes for strategies that can be implemented to enhance the effectiveness of the teacher induction program were personalization and individualization, open communication and collaboration, and continuous improvement and adaptability. These themes were defined, discussed thoroughly, enriched with core ideas, and supported by existing studies and literature. The implications for theory revealed a strong connection between the identified themes and the Human Capital Theory and the Employee Engagement Theory, suggesting that integrating these theories into teacher induction programs enhances their effectiveness. The implications for practice highlighted the importance of structured learning, prioritizing mentoring activities, focus on continuing support, emphasizing practice and feedback, and addressing implementation gaps. Recommendations include designing comprehensive induction schedules, prioritizing mentorship programs, integrating new teachers into the broader educational environment, providing essential information through modules, establishing regular mechanisms for feedback, and addressing implementation gaps through active leadership support. By incorporating these implications into practice, educational institutions can create a more supportive and engaging environment for new teachers, contributing to their professional development and success.

Construction of an Innovative System for Examination Management and Education Based on Artificial Intelligence Technology

Abstract Leveraging artificial intelligence (AI), this study revolutionizes examination management and education in universities by developing an intelligent system encompassing comprehensive management, pre-examination activities, scheduling, and preparation. The system also features a quality management component for educational outcomes. An enhanced genetic algorithm introduces an adaptation function to optimize intelligent grouping, facilitating effective exam paper distribution. Applied at Y University, our innovative approach significantly refines exam paper difficulty (ranging between 0.5016 and 0.5581) and differentiation (0.3845 to 0.4596), showcasing the intelligent algorithm’s effectiveness in exam management and contributing valuable insights to educational research.