Real-time Scheduling Research Articles

Part feeding scheduling for mixed-model assembly lines with autonomous mobile robots: benefits of using real-time data

ABSTRACT Mixed-model assembly is increasingly widespread to meet customer requirements for customisation and short delivery times. Flexible part feeding systems are required to timely replenish assembly stations with materials, avoid station idle times, and limit inventory levels on the shop floor. Part feeding scheduling is a complex and dynamic problem, affected by processing time fluctuations, equipment failures, and variations of product mix. Although real-time data of factory processes and resources is widely available and can be exploited using a digital twin of the part feeding system, there is a lack of scientific evidence on the benefits of using real-time data in part feeding scheduling. This research addresses this gap by developing an agent-based simulation model of a part feeding system with a fleet of autonomous mobile robots (AMRs) and comparing a real-time dynamic part feeding scheduling approach with static benchmark approaches. Numerical results indicate that using real-time data improves the performance of the part feeding system and the assembly system significantly, and allows improving the trade-off between the AMR fleet size and the total storage capacity on the shop floor, resulting in lower investment costs for AMRs given a certain storage capacity or lower required storage capacity given an AMR fleet.

Real-Time rejuvenation scheduling for cloud systems with virtualized software spares

With the increasing popularity of cloud services, there is a growing demand for high reliability and availability of cloud computing. As viable solutions, virtualized software spares and rejuvenation scheduling have been used to maintain highly reliable software platforms and combat Mandelbugs in cloud systems. However, developing real-time rejuvenation schedules for software components with dynamic reliability models has been a challenging task. In this paper, we propose a hybrid approach that integrates preventive and automatic failover strategies to mitigate the harmful effects of Mandelbugs. The approach allows selecting reliability models based on the state of virtualized software components, performing reliability calculations for Software SPare (SSP) gates with up to two virtual hot spares, and scheduling software rejuvenation in real time for cloud systems. Furthermore, the use of Dynamic Fault Tree (DFT) analysis supports the compositional modeling of complex and interconnected systems, alleviating the problem of state-space explosion. Finally, we present a case study of a cloud system with virtualized software spares to demonstrate how rejuvenation schedules can be generated and updated in real time.

Coupled weather and crop simulation modeling for smart irrigation planning: a review

ABSTRACT Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management.

ANDROID HEALTHCARE APPLICATION

The healthcare systems is pivotal in improving the accessibility and management of healthcare services. This paper focuses on the creation of a comprehensive appointment management system to streamline various healthcare interactions. Modern healthcare challenges such as scheduling appointments, ordering medications, and accessing health information necessitate an integrated approach. The proposed system includes modules for user registration, lab test management, medication management, doctor search and appointment booking, health education articles, order tracking, and user logout functionalities. Utilizing user-friendly interfaces and robust backend infrastructure, the system ensures secure user registration, easy browsing of lab tests and medications, efficient search for specialist doctors, convenient appointment booking, access to health articles, and order tracking. Key features include personalized user profiles, real-time appointment scheduling, and secure data storage using a SQLite database. Keywords: Healthcare management, Mobile Applications, Software Engineering

Real-time scheduling of power grid digital twin tasks in cloud via deep reinforcement learning

As energy demand continues to grow, it is crucial to integrate advanced technologies into power grids for better reliability and efficiency. Digital Twin (DT) technology plays a key role in this by using data to monitor and predict real-time operations, significantly enhancing system efficiency. However, as the power grid expands and digitization accelerates, the data generated by the grid and the DT system grows exponentially. Effectively handling this massive data is crucial for leveraging DT technology. Traditional local computing faces challenges such as limited hardware resources and slow processing speeds. A viable solution is to offload tasks to the cloud, utilizing its powerful computational capabilities to support the stable operation of the power grid. To address the need, we propose GD-DRL, a task scheduling method based on Deep Reinforcement Learning (DRL). GD-DRL considers the characteristics of computational tasks from the power grid and DT system and uses a DRL agent to schedule tasks in real-time across different computing nodes, optimizing for processing time and cost. We evaluate our method against several established real-time scheduling techniques, including Deep Q-Network (DQN). Our experimental results show that the GD-DRL method outperforms existing strategies by reducing response time, lowering costs, and increasing success rates.

Symmetric spatiotemporal learning network with sparse meter graph for short-term energy-consumption prediction in manufacturing systems

Short-term energy-consumption prediction is the basis of anomaly detection, real-time scheduling, and energy-saving control in manufacturing systems. Most existing methods focus on single-node energy-consumption prediction and suffer from difficult parameter collection and modelling. Although several methods have been presented for multinode energy-consumption prediction, their prediction performance needs to be improved owing to a lack of appropriate knowledge guidance and learning networks for complex spatiotemporal relationships. This study presents a symmetric spatiotemporal learning network (SSTLN) with a sparse meter graph (SMG) (SSTLN-SMG) that aims to predict multiple nodes based on energy-consumption time series and general process knowledge. The SMG expresses process knowledge by abstracting production nodes, material flows, and energy usage, and provides initial guidance for the SSTLN to extract spatial features. SSTLN, a symmetrical stack of graph convolutional networks (GCN) and gated linear units (GLU), is devised to achieve a trade-off not only between spatial and temporal feature extraction but also between detail capture and noise suppression. Extensive experiments were performed using datasets from an aluminium profile plant. The experimental results demonstrate that the proposed method allows multinode energy-consumption prediction with less prediction error than state-of-the-art methods, methods with deformed meter graphs, and methods with deformed learning networks.

Millisecond-Scale Real-Time Scheduling of Buses: A Controller-Based Approach

Millisecond-Scale Real-Time Scheduling of Buses: A Controller-Based Approach

Real-Time Solar Power Generation Scheduling for Maintenance and Suboptimally Performing Equipment Using Demand Response Unified with Model Predictive Control

This paper proposes a novel approach that unifies a demand response (DR) with a master plan of the model predictive control method focusing on scheduling maintenance and replacement for suboptimal equipment in real-time solar power plants. By leveraging DR mechanisms and MPC algorithms, our proposed framework starts with understanding the correlation between solar module temperature, surrounding temperature, and irradiation—essential for predicting and optimizing the performance of solar energy installations. It extends to evaluate the DC to AC conversion ratio, which is an indicator of the efficiency of the inverters. This integration enables proactive decisions for repair, maintenance, or replacement of equipment. Through exploratory data analysis using Python, we establish the efficiency and benefits of our anticipated approach in identifying the relationship between the factors that affect solar power generation.

Online optimal scheduling for battery swapping charging systems with partial delivery

Battery swapping–charging system (BSCS) is a promising operating paradigm to provide centering charging and battery swapping service for electric vehicles in transportation electrification. Facing real-time information on battery demand under the limited transporting trucks, flexible online optimization of battery delivery and transportation routing is essential for meeting practical requirements. This paper investigates the real-time scheduling problem in BSCS, considering the battery partial delivery, energy demand, delivery deadline, and vehicle routing. Considering the non-deterministic polynomia hardness of battery transportation, the offline BSCS is a time-consuming task and is unsuitable for the online setting. A Lagrangian relaxation-based Benders decomposition is proposed for parallel and real-time implementation, improving the scheduling efficiency. To tackle future information such as battery demands and delivery deadlines, by introducing the dummy copy, the offline algorithm is embedded within a rolling horizon framework to solve in real-time repeatedly. Finally, case studies using real road maps in Shanghai and Belgium have verified the validity of the proposed online framework and confirmed the necessity of considering partial delivery in enhancing the operation flexibility of BSCS. The computational efficiency of the proposed algorithm is studied under different scales of the road network, and the profit from partial delivery and online implementation are highlighted.

TREAFET: Temperature-Aware Real-Time Task Scheduling for FinFET based Multicores

The recent shift in the VLSI industry from conventional MOSFET to FinFET for designing contemporary chip-multiprocessor (CMP) has noticeably improved hardware platforms’ computing capabilities, but at the cost of several thermal issues. Unlike the conventional MOSFET, FinFET devices experience a significant increase in circuit speed at a higher temperature, called temperature effect inversion (TEI), but higher temperature can also curtail the circuit lifetime due to self-heating effects (SHEs). These fundamental thermal properties of FinFET introduced a new challenge for scheduling time-critical tasks on FinFET-based multicores that how to exploit TEI towards improving performance while combating SHEs. In this work, TREAFET , a temperature-aware real-time scheduler, attempts to exploit the TEI feature of FinFET-based multicores in a time-critical computing paradigm. At first, the overall progress of individual tasks is monitored, tasks are allocated to the cores, and finally, a schedule is prepared. By considering the thermal profiles of the individual tasks and the current thermal status of the cores, hot tasks are assigned to the cold cores and vice-versa. Finally, the performance and temperature are balanced on-the-fly by incorporating a prudential voltage scaling towards exploiting TEI while guaranteeing the deadline and thermal safety. Moreover, TREAFET stimulates the average runtime frequency by employing an opportunistic energy-adaptive voltage spiking mechanism, in which energy saving during memory stalls at the cores is traded off during the time slice having the spiked voltage. Simulation results claim TREAFET maintains a safe and stable thermal status (peak temperature below 80 °C) and improves frequency up to 17% over the assigned value, which ensures legitimate time-critical performance for a variety of workloads while surpassing a state-of-the-art technique. The stimulated frequency in TREAFET also finishes the tasks early, thus providing opportunities to save energy by power gating the cores, and achieves a 24% energy delay product (EDP) gain on average.

Design and deployment of a novel decisive algorithm to enable real-time optimal load scheduling within an intelligent smart energy management system based on IoT

Consumers routinely use electrical devices, leading to a disparity between consumer demand and the supply side a significant concern for the energy sector. Implementing demand-side energy management can enhance energy efficiency and mitigate substantial supply-side shortages. Current energy management practices focus on reducing power consumption during peak hours, enabling a decrease in overall electricity costs without sacrificing usage. To tackle the mentioned challenges and maintain system equilibrium, it is essential to develop a flexible and portable system. Introducing an intelligent energy management system could pre-empt power outages by implementing controlled partial load shedding based on consumer preferences. During a demand response event, the system adapts by imposing a maximum demand limit, considering various scenarios and adjusting appliance priorities. Experimental work, incorporating user comfort levels, sensor data, and usage times, is conducted using Smart Energy Management Systems (SEMS) integrated with cost-optimization algorithms.

Machine Learning Techniques to Optimize CPU Scheduling in Real-Time Systems: A Comprehensive Review and Analysis

Real-time systems demand stringent adherence to timing constraints, making CPU scheduling a critical factor for ensuring timely and reliable task execution. Traditional CPU scheduling algorithms, while effective in many scenarios, often fall short in handling the dynamic and complex nature of modern real-time applications. This paper provides a comprehensive review and analysis of machine learning (ML) techniques employed to optimize CPU scheduling in real-time systems. We explore various ML methodologies including supervised learning, reinforcement learning, and deep learning, examining their applications, advantages, and limitations in the context of real-time CPU scheduling. By leveraging ML, these systems can dynamically adapt to changing workloads, predict task execution times, and optimize scheduling policies, thereby improving overall system performance and predictability. Key contributions of this review include a detailed comparison of ML-based approaches against traditional scheduling techniques, insights into their real-time applicability, and identification of future research directions. The analysis underscores the potential of ML to transform CPU scheduling by providing adaptive, intelligent solutions that cater to the evolving demands of real-time systems

BD-TTS: A blockchain and DRL-based framework for trusted task scheduling in edge computing

Edge computing migrates tasks to the edge of the network for execution, which can provide users with lower latency and better quality of service (QoS). However, due to the complexity and dynamism of edge environments, many task scheduling algorithms in edge computing face challenges in achieving real-time scheduling, while there are also trust issues in heterogeneous and dynamic edge environments. To tackle these issues, we introduce a novel framework for trusted task scheduling in edge computing, leveraging blockchain and deep reinforcement learning (DRL) technologies, named BD-TTS. Specifically, we design a blockchain-based trust management scheme tailored for task scheduling in edge computing. The scheme uses blockchain to store, propagate, and update trust information in a decentralized manner to evaluate the trustworthiness of edge servers. In addition, to assign tasks to edge servers with higher trust values for execution, we introduce a DRL-driven task scheduling algorithm. The algorithm dynamically schedules tasks in real-time based on fluctuations in the trust values of edge servers. The experimental results show that compared to other baseline approaches, BD-TTS effectively reduces the number of tasks assigned to malicious edge servers by over 64.4%, reduces the average task response time by at least 13.9%, and improves the success rate by more than 14.7%.

Relative distances approach for multi-traveling salesmen problem

In this paper, we present a novel approach to addressing the Multi-Traveling Salesman Problem (MTSP), a complex optimization challenge involving the simultaneous fulfillment of multiple tasks such as cargo delivery and warehouse placement by multiple autonomous agents. In general there are two primary objectives to be achieved: minimizing the total path cost and minimizing the maximum cost incurred by agents, also known as makespan. While our primary focus centers on cost minimization, prioritizing this aspect alone often amplifies makespan. Our approach strikes a balance by ensuring that makespan remains within a reasonable threshold. Given the problem’s combinatorial nature, attaining cost-optimal solutions proves infeasible under current constraints. Hence, our emphasis shifts towards swift solution discovery to align with practical applicability. Consequently, we posit that the third pivotal objective is to curtail complexity and expedite solution acquisition, thereby enhancing the problem’s real-world viability. The Multi-Traveling Salesman Problem (MTSP) is typically approached in two distinct phases. In the initial phase, tasks are allocated to salesmen using various methods (such as K-Means or DBSCAN). The second phase involves determining the optimal routes for each salesman, a problem identical to the classic Traveling Salesman Problem (TSP). Our novel heuristic approach integrates these phases into a single method through our relative distance model. This model facilitates the easy addition and removal of tasks within the problem space and enables real-time scheduling.

Distributed Cooperative Optimal Operation of Multiple Virtual Power Plants Based on Multi-Stage Robust Optimization

This paper develops a distributed cooperative optimization model for multiple virtual power plant (VPP) operations based on multi-stage robust optimization and proposes a distributed solution methodology based on the combination of the alternating direction method of multipliers (ADMMs) and column-and-constraint generation (CCG) algorithm to solve the corresponding optimization problem. Firstly, considering the peer-to-peer (P2P) electricity transactions among multiple VPPs, a deterministic cooperative optimal operation model of multiple VPPs based on Nash bargaining is constructed. Secondly, considering the uncertainties of photovoltaic generation and load demand, as well as the non-anticipativity of real-time scheduling of VPPs in engineering, a cooperative optimal operation model of multiple VPPs based on multi-stage robust optimization is then constructed. Thirdly, the constructed model is solved using a distributed solution methodology based on the combination of the ADMM and CCG algorithms. Finally, a case study is solved. The case study results show that the proposed method can realize the optimal scheduling of renewable energy in a more extensive range, which contributes to the promotion of the local consumption of renewable energy and the improvement of the renewable energy utilization efficiency of VPPs. Compared with the traditional deterministic cooperative optimal operation method of multiple VPPs, the proposed method is more resistant to the risk of the uncertainties of renewable energy and load demand and conforms to the non-anticipativity of real-time scheduling of VPPs in engineering. In summary, the presented works strike a balance between the operational robustness and operational economy of VPPs. In addition, under the presented works, there is no need for each VPP to divulge personal private data such as photovoltaic generation and load demand to other VPPs, so the security privacy protection of each VPP can be achieved.

Real-time adaptive scheduling optimization for inter-satellite contact window resources in dynamic satellite networks

Real-time scheduling of contact window resources is a fundamental technology for the future construction of Space–Terrestrial Integrated Networks (STIN). The spatio-temporal dynamics of satellite networks result in continuous variations in satellite relative positions and inter-satellite visibility window resource status. Furthermore, frequent changes in task priority make real-time resource scheduling extremely challenging. Centralized online scheduling methods often lead to a significant amount of waiting time, while distributed online scheduling methods struggle to adapt to the high dynamics of satellite networks. Therefore, the real-time scheduling technology for contact window resources in satellite networks is still in the exploration phase. We propose an inter-satellite online resource allocation method to solve the latency issue caused by the seizure of contact window resources. First, we determine the shortest and maximum flow paths set in the satellite network according to task requirements and construct them as the core network. Then, we comprehensively analyze the supply and demand situation of the task and the core network at a specific moment and verify the reliability of our minimum cost flow-solving method based on the core network. Moreover, we consider factors such as changes in task priority and edge modifications and validate the correctness of our method to update the minimum cost flow at the next moment based on local network changes. Finally, through extensive comparative simulations, we evaluate the absolute advantage of our inter-satellite online scheduling method and validate a significant improvement in task scheduling timeliness.

Privacy-preserving multi-level co-regulation of VPPs via hierarchical safe deep reinforcement learning

Large amounts of distributed energy sources have brought challenges in terms of the safe and stable operation of the power grid. As a key technology between user-side energy resources and the distribution network (DN), how to realize the online coordinated scheduling of VPP and DN as well as the real-time response strategy of distributed equipment (DE) within VPP is the focus of this study. Thus, the hierarchical deep reinforcement learning (DRL) Hierarchical-TD3 algorithm is designed based on the unified modeling of adjustable space to achieve the real-time economic scheduling of VPPs. The upper layer DN considers the network security constraints and solves the economic scheduling model of VPPs based on the single-agent TD3 algorithm. Based on the scheduling instructions from the upper layer, the lower layer VPPs consider the requirements of privacy protection and control autonomy and realize real-time response of the DE within VPP via multi-agent MATD3 algorithm. Numerical results in the modified 33-nodes system show that the proposed Hierarchical-TD3 algorithm can achieve privacy protection and the coordinated scheduling of VPP and DE to reduce the operating cost. It differs from the optimal value by only 1.46% but can achieve online decision-making on the millisecond scale. Compared with the traditional centralized and decentralized DRL algorithms, the total cost is reduced by 10.15% and 5.52% respectively. Compared with the traditional soft-constraint method, there is no constraint violation during the training and testing phases. Finally, the actual 116-nodes testing system validates the scalability of the proposed Hierarchical-TD3 algorithm.

A Web-based Ticketing and Booking Application for Enhancing Maritime Travel Operations and Services in Siargao Island

Siargao Island is one of the world-class and most popular tourist destinations in the Philippines. The island is still in its developmental stage in terms of the tourism industry, which oftentimes face difficulties on its maritime travel services because of inefficiencies on manual ticketing and booking processes. With this, the study aimed at investigating the current services offered by the small-scale local maritime providers. A mixed-methodology approach was employed in the study through online survey and interviews to gather data on respondents’ preferences, challenges, and recommendations. The respondents were selected by convenience sampling method. The gathered data were analyzed using frequency distribution, weighted mean score, standard deviation, and ranking. The findings revealed significant results such as thechallenges related to labour-intensive ticketing processes and lack of real-time information are identified, suggesting the need for digital solutions. There is also a high demand for improved travel services, with preferences for online booking, real-time scheduling, and user-friendly interfaces. The study contributes to the development of a web application by providing insights into passenger preferences and challenges, guiding the design and implementation of a system that enhances maritime travel services in Siargao Island

Algorithmic approach leveraging a real-time task scheduler with fan-out strategy

Algorithmic approach leveraging a real-time task scheduler with fan-out strategy

Short-term water demand prediction based on decomposition technique optimization and a multihead attention mechanism

ABSTRACT Short-term water demand prediction is crucial for real-time optimal scheduling and leakage control in water distribution systems. This paper proposes a new deep learning-based method for short-term water demand prediction. The proposed method consists of four main parts: the variational mode decomposition method, the golden jackal optimization algorithm, the multihead attention mechanism, and the bidirectional gated recurrent unit (BiGRU) model. Furthermore, a seq2seq strategy was adopted for multistep prediction to avoid the error accumulation problem. Hourly water demand data collected from a real-world water distribution system were applied to investigate the potential of the proposed method. The results show that the proposed method can yield remarkably accurate and stable forecasts in single-step prediction (i.e., the mean absolute percentage error (MAPE) reaches 0.45%, and the root mean squared error (RMSE) is 25 m3/h). Moreover, the proposed method still achieves credible performance in 24-step prediction (i.e., the MAPE reaches 2.12%, and the RMSE is 126 m3/h). In general, for both single-step prediction and multistep prediction, the proposed method consistently outperforms other BiGRU-based methods. These findings suggest that the proposed method can provide a reliable alternative for short-term water demand prediction.