Schedule Components Research Articles

A new low carbon scheduling method for source load multi time scale in power systems

Abstract The impact of global climate change caused by carbon dioxide emissions on human production and life is becoming increasingly severe. New energy sources, led by wind and solar power, have become the backbone of reducing carbon emissions. The main focus of this article is to develop advantages such as multi-energy complementarity, efficient integration, flexibility and stability, and low-carbon environmental protection. While meeting the diverse energy needs of the energy consumption side, it is necessary to reasonably allocate resources on the energy supply side and effectively absorb renewable energy. To begin with, this paper develops a multi-time-scale economic dispatch model for a holistic energy system, focusing on collaborative carbon reduction between energy sources and loads. The upper layer serves as a long-term energy scheduling mechanism, dynamically updating electricity prices and conveying them to the lower layer model. The lower layer functions as a short-term energy scheduling component, directing load management according to electricity prices and providing feedback on the load plan to the upper layer model. Both the upper and lower-layer models strive to minimize the overall operational costs as their primary objective. Secondly, the probability distribution of wind and photovoltaic power is discretized using sequence operation theory. The CPLEX is used to solve the above model. Finally, taking a comprehensive energy system as an example, the impact of source load side carbon reduction measures on the system scheduling results under multiple time scales is analyzed.

Research on urban water demand prediction based on machine learning and feature engineering

ABSTRACT Urban water demand prediction is not only the foundation of water resource planning and management, but also an important component of water supply system optimization and scheduling. Therefore, predicting future water demand is of great significance. For univariate time series data, the issue of outliers can be solved through data preprocessing. Then, the data input dimension is increased through feature engineering, and finally, the LightGBM (Light Gradient Boosting Machine) model is used to predict future water demand. The results demonstrate that cubic polynomial interpolation outperforms the Prophet model and the linear method in the context of missing value interpolation tasks. In terms of predicting water demand, the LightGBM model demonstrates excellent forecasting performance and can effectively predict future water demand trends. The evaluation indicators MAPE (mean absolute percentage error) and NSE (Nash–Sutcliffe efficiency coefficient) on the test dataset are 4.28% and 0.94, respectively. These indicators can provide a scientific basis for short-term prediction of water supply enterprises.

Analisis Perawatan Mesin Kapal dengan Metode Reliability Centered Maintenance (RCM) di PT Jasa Armada Indonesia Tbk

Maintenance of the ship is a task aimed at ensuring that the ship continues to operate in a normal and seaworthy condition. This work is related to the ship's machinery system and other equipment on board. If the ship does not receive regular maintenance, it can impact the condition and performance of the ship. By implementing Reliability Centered Maintenance (RCM), information about the steps needed to ensure optimal operation of the engine or equipment can be obtained. RCM is a method used to develop Preventive Maintenance. In Time Directed (TD), there are two components, namely the schottel Asd component and the valve on the cylinder head. In Condition Directed (CD), there are three components found in the three pipe components: fuel pipe, oil, and engine coolant. In Failure Finding (FF), there are four components found in the seachast component, injector pump, fuel filter, and air. Based on the application of the RCM method used, the recommended maintenance actions for the Finding Failure (FF) category include daily, weekly, and monthly inspection activities. Meanwhile, for the Time Directed (TD) category, maintenance actions involve scheduling component replacements based on predetermined intervals. Keywords: FMEA, LTA, Maintenance, RCM

Towards practicality: Navigating challenges in designing predictive-reactive scheduling

Today’s agile production systems face an ever-increasing complexity due to individualized mass production, a volatile customer demand and dynamic events which disrupt the baseline schedule. Robustness and optimality of the schedule affect the efficiency of the production systems, which in turn negatively affects resource consumption and waste generation. To achieve near-optimal performance, while adhering to due dates and robustly handling disturbances, predictive-reactive scheduling is often proposed. In the current state-of-the-art research on predictive-reactive scheduling there still exist some open issues. One issue to address is a current deficit in generalization, that is, when handling new, previously unseen data. Arguably the main culprit is the reactive (re)scheduling component. Designing such highly complex systems imposes many difficulties. We identify existing problems which ultimately limit the applicability of predictive-reactive scheduling and investigate and evaluate a design approach for predictive-reactive scheduling. We propose predictive-reactive scheduling, which uses match-up scheduling and promising decentralized multi-agent reinforcement learning, which allows for fast and flexible real-time decision-making. The aim of this study is to provide insights and improve the applicability of research on predictive-reactive scheduling.

A Novel Approach for Energy-Efficient Container Migration by Using Gradient Descent Namib Beetle Optimization

Cloud services are increasingly available through containers due to their scalability, portability, and reliable deployment, particularly in microservices and smart vehicles. The scheduler component of cloud containers plays a crucial role in optimizing energy efficiency and minimizing costs due to the diversity of workloads and cloud resources. The growing demand for cloud services poses a challenge in terms of energy consumption. Optimizing energy consumption in servers is possible by utilizing live migration technology. This study aims to propose a hybrid model that facilitates the migration of containers from one server to another using Gradient Descent Namib Beetle Optimization (GNBO) algorithms, thereby reducing the energy consumption of cloud servers. The work is carried out through cloud simulation using Physical Machines (PM), Virtual Machines (VM), and Containers. Tasks are allocated to VMs in a round-robin manner. The Actor-Critic Neural Network (ACNN) is employed to predict the load of PMs, and overloading and underloading conditions are determined based on the load. The proposed GNBO hybrid optimization calculates the optimal solution considering predicted load, migration costs, resource utilization, energy consumption, and network bandwidth. This approach achieves a load of 0.177 MIPS, migration costs of 10.146 J, and optimizes energy consumption to 0.068 W.

RACS2: a framework of a remote autonomous control system for telescope observation and its application

With the increasing demand for astronomical observations, telescope systems are becoming increasingly complex. Thus, the observatory control software needs to be more intelligent. It has to control each instrument inside the observatory, finish the observational tasks autonomously, and report the information to users if needed. We developed a distributed autonomous observatory control framework named the Remote Autonomous Control System 2nd (RACS2) to meet these requirements. Rich features are integrated into the RACS2 framework. The RACS2 is a modular framework, in which each device control software and system services are implemented as different components. Furthermore, the RACS2 framework assimilates new techniques, such as a lightweight message and serialization mechanism. RACS2 also has good compatibility with other frameworks or ecosystems such as Python and the EPICS (Experimental Physics and Industrial Control System). The RACS2 framework can communicate with EPICS-based and Python-based software. With the help of these features, key system components like executor, scheduler are developed. The executor component can support sophisticated tasks. Autonomous observation can be achieved by the scheduler component. A set of web-based graphical user interface (GUI) is designed to help control and manage the framework remotely. Based on the RACS2 framework, we have implemented the Dome A Twins (DATs) telescope’s observation system and the space object observation system. The systems have been operated for more than 1 year. The RACS2 framework also has been used in our other projects like camera control system of Wide Field Survey Telescope (WFST) and its observatory control system, and the design can be a useful reference for the development of other frameworks.

Cross Layer based Energy Aware and Packet Scheduling Algorithm for Wireless Multimedia Sensor Network

Video transmission using sensor networks plays a most significant role in industrial and surveillance applications. Multimedia transmission is also a challenging task in case of guaranteeing quality of service in conditions like limited bandwidth, high congestion, multi-hop routing, etc. Cross layer approach is carried out to handle multimedia transmission over sensor networks for improving network adaptivity. Cross layer based energy aware and packet scheduling algorithm is proposed here to reduce congestion ratio and to improve link quality between the routing nodes. Link quality estimation among nodes is done using Semi-Markov process. Node congestion rate is determined for identifying node’s data channel rate. Packet scheduling process determines the highly prioritized packets by using queue scheduler component thereby the active nodes are selected through link quality process and the packets are transmitted to sink based on prioritize level. Simulation analysis is carried out and the efficiency of the proposed mechanism is proved to be better while comparing with the conventional schemes.

Pragmatic evaluation of privacy preservation security models targeted towards fog-based deployments

Fog layer sits between cloud layer and edge-layer and responsible for selection of edge-nodes to process cloud tasks. Fog devices manage routers, gateways and other scheduling components, which makes them highly vulnerable to security attacks. Attackers inject malicious packets fog-server, middleware or sensing layers which causes a wide variety of attacks. These attacks include node capturing, signal jamming, node outage, authorization, selective forwarding, data disclosure etc. To remove these attacks, wide variety of solutions are proposed by researchers, which include authorization, cryptography, error correction, firewall, broadcast authentication, selective disclosure etc. Moreover, these solutions vary with respect to privacy and security quality metrics, attack prevention capabilities and deployment quality of service (QoS). Thus, testing and deployment of these solutions is time consuming, requires additional manpower for performance validation. Hence fog deployments require larger time-to-market and are costly than their corresponding cloud deployments. In order to reduce the time for testing and validation of these resilience techniques, this text reviews various fog security & privacy preservation models and discusses their nuances, advantages, limitations and future research scopes. Furthermore it also performs a detailed performance comparison between the reviewed models, which assists in selecting best possible approach for a given application scenario. This text also recommends various fusion based approaches that can be applied to existing security and privacy models in order to further improve their performance. These approaches include hybridization, selective augmentation and Q-learning based models that assist in improving efficiency of encryption, privacy preservation, while maintaining high QoS levels.

An approach for managing the Internet of things’ resources to optimize the energy consumption using a nature-inspired optimization algorithm and Markov model

The Internet of Things (IoT) is a developed communication idea in which connectivity and intelligence are added to nearly every device in a real-world context. From a technological standpoint, IoT provides a computational and communications capability to an item to connect to the Internet. Resource management policies are integral to IoT systems to ensure service quality and prevent energy loss and resource fragmentation. The resource management architecture includes some parts that coherently control resources in a way that minimizes resource loss in addition to offsetting the application service quality limitations. To this aim, planning and scheduling components are critical in determining the condition of available resources and the best candidates for hosting a program module. We proposed a method to handle IoT's resources utilizing a nature-inspired optimization algorithm and a Markov model to solve these restrictions. The performance of the proposed method was evaluated to current IoT access management systems in this article. In comparison to earlier approaches, the results demonstrated the efficacy of the new approach in terms of execution time and energy consumption.

Design of impulsive asteroid flybys and scheduling of time-minimal optimal control arcs for the construction of a Dyson ring (GTOC 11)

This paper describes the approach used by the team named the Eccentric Anomalies to obtain the sixth best solution to the problem of the eleventh Global Trajectory Optimization Competition, whose futuristic scenario of Dyson sphere building remained mathematically relevant for current space mission design and engineering in general. As usual for this recurring challenge, it involved large-scale combinatorics at a high level and a multitude of optimal control problems at a lower level. Furthermore it proposed additional layers of complexity by adding a strong scheduling component to the usual flyby sequencing, and by featuring both impulsive and continuous-thrust trajectories. The authors took advantage of modern theoretical techniques and open-source tools to put together a sequential process including analysis based on analytical trajectory models, tree searches using efficient data structures, global and local finite-dimensional optimization and multi-objective trade-offs. The optimal control part was both tackled with direct transcription as well as indirect shooting methods, and the mixed-integer scheduling reformulated as a bi-level optimization. From a programming point of view, the main framework was set in an interpreted language whilst using as much as possible dependencies written in compiled ones for speed.

Text-Based Intervention Increases Mammography Uptake at an Urban Safety-Net Hospital.

BackgroundThe low mammography rates at the authors’ safety-net hospital (SNH) are associated with higher rates of late-stage disease. Previously, they showed that a phone call-based intervention with reminder and scheduling components significantly increased mammography uptake by 12% in their population, but implementation was resource-heavy. This study analyzed whether a text-based intervention with reminder and scheduling components could increase mammography uptake at 3 months compared with usual care.MethodsThis randomized controlled study analyzed 1277 women ages 50 to 65 years who were overdue for a mammogram but had established care at a primary-care clinic within an urban SNH. The patients received intervention 1 (a text reminder with specific scheduling options), intervention 2 (a text reminder with open-ended scheduling options), or usual care (control). Differences in the percentage of mammography uptake at 3 months were compared between the intervention and control groups using a two-tailed chi-square test.ResultsThe patients receiving a text-based reminder and scheduling opportunity were significantly more likely to receive mammograms within 3 months than those in the usual-care control group (10.2% vs 6.2%; χ2 = 5.6279; p = 0.03). In the intervention group, 10.3% of the participants scheduled an appointment for a mammogram via text, and 63% of these participants received a mammogram. Finally, mammography compliance did not differ by the type of scheduling offered (specific vs general) or by primary care clinic.ConclusionsLeveraging technology for reminders and scheduling via two-way text messaging is effective in increasing mammography uptake in an urban safety-net setting and may be used as part of a multi-tiered intervention to increase breast cancer screening in a safety-net setting.

A Transformer-Based Multi-Entity Load Forecasting Method for Integrated Energy Systems

Energy load forecasting is a critical component of energy system scheduling and optimization. This method, which is classified as a time-series forecasting method, uses prior features as inputs to forecast future energy loads. Unlike a traditional single-target scenario, an integrated energy system has a hierarchy of many correlated energy consumption entities as prediction targets. Existing data-driven approaches typically interpret entity indexes as suggestive features, which fail to adequately represent interrelationships among entities. This paper, therefore, proposes a neural network model named Cross-entity Temporal Fusion Transformer (CETFT) that leverages a cross-entity attention mechanism to model inter-entity correlations. The enhanced attention module is capable of mapping the relationships among entities within a time window and informing the decoder about which entity in the encoder to concentrate on. In order to reduce the computational complexity, shared variable selection networks are adapted to extract features from different entities. A data set obtained from 13 buildings on a university campus is used as a case study to verify the performance of the proposed approach. Compared to the comparative methods, the proposed model achieves the smallest error on most horizons and buildings. Furthermore, variable importance, temporal correlations, building relationships, and time-series patterns in data are analyzed with the attention mechanism and variable selection networks, therefore the rich interpretability of the model is verified.

Literature Review on Problem Models and Solution Approaches for Managing Real-Time Passenger Train Operations: The Perspective of Train Operating Companies

This study presents a comprehensive review of different problem models for managing railway operations by problem-type classification. Railway terminology was used to identify the studies that encompass the existing body of knowledge. The 28 articles analyzed showed that existing studies are focused on the individual schedule components, such as rolling stock, schedules, crews, and passengers. Few studies have adopted a broader scope by covering several of those components. Two of the most popular approaches include the integer linear program and the mixed integer linear program variant. The difference between them is that integer programming uses discrete decision-making variable data, while mixed integer programming also admits continuous variable data. In contrast, few studies involve combining computational algorithms with human knowledge-based approaches. This analysis reveals that the most significant variables for managing disruptive events are related to verifying suppressed circulation and the discrete events of real-time traffic, such as departures and arrivals at stations.

Maintenance Strategies for Industrial Multi-Stage Machines: The Study of a Thermoforming Machine.

The study of reliability, availability and control of industrial manufacturing machines is a constant challenge in the industrial environment. This paper compares the results offered by several maintenance strategies for multi-stage industrial manufacturing machines by analysing a real case of a multi-stage thermoforming machine. Specifically, two strategies based on preventive maintenance, Preventive Programming Maintenance (PPM) and Improve Preventive Programming Maintenance (IPPM) are compared with two new strategies based on predictive maintenance, namely Algorithm Life Optimisation Programming (ALOP) and Digital Behaviour Twin (DBT). The condition of machine components can be assessed with the latter two proposals (ALOP and DBT) using sensors and algorithms, thus providing a warning value for early decision-making before unexpected faults occur. The study shows that the ALOP and DBT models detect unexpected failures early enough, while the PPM and IPPM strategies warn of scheduled component replacement at the end of their life cycle. The ALOP and DBT strategies algorithms can also be valid for managing the maintenance of other multi-stage industrial manufacturing machines. The authors consider that the combination of preventive and predictive maintenance strategies may be an ideal approach because operating conditions affect the mechanical, electrical, electronic and pneumatic components of multi-stage industrial manufacturing machines differently.

A Low Latency Secure Communication Architecture for Microgrid Control

The availability of secure, efficient, and reliable communication systems is critical for the successful deployment and operations of new power systems such as microgrids. These systems provide a platform for implementing intelligent and autonomous algorithms that improve the power control process. However, building a secure communication system for microgrid purposes that is also efficient and reliable remains a challenge. Conventional security mechanisms introduce extra processing steps that affect performance by increasing the latency of microgrid communication beyond acceptable limits. They also do not scale well and can impact the reliability of power operations as the size of a microgrid grows. This paper proposes a low latency secure communication architecture for control operations in an islanded IoT-based microgrid that solves these problems. The architecture provides a secure platform that optimises the standard CoAP/DTLS implementation to reduce communication latency. It also introduces a traffic scheduler component that uses a fixed priority preemptive algorithm to ensure reliability as the microgrid scales up. The architecture is implemented on a lab-scale IoT-based microgrid prototype to test for performance and security. Results show that the proposed architecture can mitigate the main security threats and provide security services necessary for power control operations with minimal latency performance. Compared to other implementations using existing secure IoT protocols, our secure architecture was the only one to satisfy and maintain the recommended latency requirements for power control operations, i.e., 100 ms under all conditions.

Activity-Based Scheduling of Science Campaigns for the Rosetta Orbiter

Rosetta was a European Space Agency (ESA) cornerstone mission that launched in March 2004, exited hibernation in January 2014, entered orbit around the comet 67P/Churyumov-Gerasimenko in August 2014, and escorted the comet through September 2016, executing the most detailed study of a comet ever undertaken by humankind. The Rosetta Orbiter had 11 scientific instruments (4 remote sensing) and the Philae Lander to make complementary measurements of the comet nucleus, coma (gas and dust), and surrounding environment. The ESA Rosetta Science Ground Segment (SGS) used a science scheduling system that included an automated scheduling capability to assist in developing science plans for the Rosetta Orbiter. While the automated scheduling was a small portion of the overall SGS as well as the overall scheduling system, this paper focuses on the automated and semi-automated scheduling software (called Automated Scheduling and Planning ENvironment - Rosetta Science Scheduling Component (ASPEN-RSSC)) discussing: 1) the scheduling and constraint checking capabilities of ASPEN-RSSC; and 2) how the software was used pre-exit from hibernation, prelander delivery, and escort phase of the mission; 3) challenges in using the software and lessons learned for future use of automated scheduling technology for future space missions.

An approach for discovery and scheduling replaceable service on edge environment

In an edge computing environment, in order to provide highly accessible services and computing resources to nearby users, edge servers are usually deployed on base stations and other types of equipment. However, due to the limited storage space of edge servers, it is very difficult to manage services with a failure. Therefore, the rapid restoration of services in the edge computing environment will become an important means to ensure the resilience of the system. Among them, service replacement with similarities within the edge server is one of the effective technologies to ensure system resilience. In this paper, we regard the discovery and scheduling problem of replaceable services replacement as the discovery and scheduling component, and develop an approach based on replaceable service form the app vendor’s perspective for solving the none replaceable services environment. We have evaluated our approach in a real experimental environment. The results show that in the case of large mirroring, the DAS approach can effectively reduce the recovery time required by the system due to failure.

Utilizing digital image processing and two-source energy balance model for the estimation of evapotranspiration of dry edible beans in western Nebraska

Having an accurate yet simple method to estimate crop evapotranspiration (ETC) is a vital component of reliable irrigation scheduling. In this study, two versions of the two-source energy balance (TSEB) model: the TSEB model with the Priestley–Taylor equation (TSEB-PT) and the Penman–Monteith equation (TSEB-PM), were used to estimate ETC of dry edible beans in western Nebraska. Compared with previous studies, this study is unique in that a Visual Basic software—Crop Canopy Image Analyzer (CCIA) was developed to process digitally captured RGB canopy images to obtain necessary canopy cover (CC) parameters for the TSEB models such as CC percentage and leaf shape factor (leaf area divided by its perimeter). Software-estimated CC percentage was closely correlated with commercial sensor-derived CC percentage with an R2 of 0.96. Additionally, estimated leaf shape factor was closely correlated with measured leaf shape factor with R2 of 0.99. Both TSEB-PT and TSEB-PM models estimated ETC well for fully irrigated dry edible beans with a root-mean-square error (RMSE) that ranged from 0.95 to 1.63 mm day−1 in 2018, and 0.75 to 1.35 mm day−1 in 2019, as compared to ETC estimated from FAO56. Furthermore, ETC from TSEB-PT and TSEB-PM were compared with a soil water balance-derived ETC and the RMSE ranged from 2.03 to 9.65 mm in an approximate 1-week period under four irrigation treatments ranging from dry land to fully irrigated. The proposed methods in this study, by integrating digital image processing with TSEB models, have great potential to be automated and used in field-scale operations for various irrigation management scenarios of many crops.

Optimizing e-commerce last-mile vehicle routing and scheduling under uncertain customer presence

The recent increase in online orders in e-commerce leads to logistical challenges such as low hit rates (proportion of successful deliveries). We consider last-mile vehicle routing and scheduling problems in which customer presence probability data are taken into account. The aim is to reduce the expected cost resulting from low hit rates by considering both routing and scheduling decisions simultaneously in the planning phase. We model the problem and solve it by the means of an adaptive large neighborhood search metaheuristic which iterates between the routing and scheduling components of the problem. Computational experiments indicate that using customer-related presence data significantly can yield savings as large as 40% in system-wide costs compared with those of traditional vehicle routing solutions.

Robust Gain-Scheduled Estimation With Dynamic D-Scalings

We provide a convex solution to the robust gain-scheduled estimation problem based on integral quadratic constraints with dynamic D-scalings for both the uncertain and the scheduled component. This closes an important gap since, so far, merely static scalings could be used for the scheduled component in estimation problems. To this end, we provide novel synthesis criteria in terms of linear matrix inequalities for the design of nominal gain-scheduled estimators that allow for a direct combination with available results on robust estimation. We illustrate the benefit of our design approach by means of a numerical example.