Real-time Scheduling Research Articles

Microgrid Formation and Real-Time Scheduling of Active Distribution Networks Considering Source-Load Stochasticity

The post-disruption microgrid (MG) formation and the subsequent scheduling are resilience-enhancing measures for active distribution networks (ADNs) against disastrous events. This paper proposes an integrated MG formation and scheduling solution, considering stochastic loads and distributed generators (DGs). Specifically, a first-stage MG formation model and a sec-ond-stage MG scheduling model are proposed to flexibly identify MG boundaries and restore as many critical loads as possible. The MG formation problem is formulated over multiple time steps to address time-varying generation outputs of intermittent DGs. Moreover, in the MG scheduling problem, the stochasticity of load demands and DG outputs is modeled as joint chance constraints (JCCs) to describe the possibility of security violations in the over-all system. An efficient approximation method is next developed to convexify the JCCs into solvable second-order cones. The interac-tion between the MG formation and scheduling by the model predictive control implementation achieves a responsive solution for enhancing the emergency support capability of ADNs by incor-porating power forecasts and real-time scheduling. The performance of the proposed method is verified in a modified real-world 136-node test feeder under catastrophic failures.

Medconnect (Android App): Linking Doctors, Patients, and Prescriptions Digitally

Abstract: Medconnect, an innovative Android application, emerges as a transformative platform within the healthcare sector, seamlessly bridging the communication gap between doctors, patients, and pharmacies. This application introduces a novel approach to managing prescriptions digitally, ensuring accuracy, enhancing medication adherence, and streamlining the healthcare experience. With functionalities like e-prescriptions, real-time appointment scheduling, and a comprehensive medical history database, Medconnect prioritizes patient care and data security. The integration of advanced technologies facilitates a more efficient healthcare delivery system, reduces the potential for prescription errors, and promotes a collaborative environment between healthcare providers and patients. By embodying the principles of accessibility, reliability, and user-centric design, Medconnect stands at the forefront of digital healthcare innovation, promising a future where managing health and wellness is more integrated and intuitive.

Real-Time Production Scheduling and Industrial Sonar and Their Application in Autonomous Mobile Robots

In real-time production planning, there are exceptional events that can cause problems and deviations in the production schedule. These circumstances can be solved with real-time production planning, which is able to quickly reschedule the operations at each work centre. Mobile autonomous robots are a key element in this real-time planning and are a fundamental link between production centres. Work centres in Industry 4.0 environments can use current technology, i.e., a biomimetic strategy that emulates echolocation, with the aim of establishing bidirectional communication with other work centres through the application of agile algorithms. Taking advantage of these communication capabilities, the basic idea is to distribute the execution of the algorithm among different work centres that interact like a parasympathetic system that makes automatic movements to reorder the production schedule. The aim is to use algorithms with an optimal solution based on the simplicity of the task distribution, trying to avoid heuristic algorithms or heavy computations. This paper presents the following result: the development of an Industrial Sonar algorithm which allows real-time scheduling and obtains the optimal solution at all times. The objective of this is to reduce the makespan, reduce energy costs and carbon footprint, and reduce the waiting and transport times for autonomous mobile robots using the Internet of Things, cloud computing and machine learning technologies to emulate echolocation.

Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

In recent years, autonomous mobile platforms have seen an increase in usage in several applications. One of which is street-sweeping. Although street-sweeping is a necessary process due to health and cleanliness, fleet operations are difficult to plan optimally. Since each vehicle has several constraints (battery, debris, and water), path planning becomes increasingly difficult to perform manually. Additionally, in real-world applications vehicles may become inactive due to a breakdown, which requires real-time scheduling technology to update the paths for the remaining vehicles. In this paper, the fleet street-sweeping problem can be solved using the proposed lower-level and higher-level path generation methods. For the lower level, a Smart Selective Navigator algorithm is proposed, and a modified genetic algorithm is used for the higher-level path planning. A case study was presented for Uchi Park, South Korea, where the proposed methodology was validated. Specifically, results generated from the ideal scenario (all vehicles operating) were compared to the breakdown scenario, where little to no difference in the overall statistics was observed. Additionally, the lower-level path generation could yield solutions with over 94% area coverage.

A bidirectional workflow scheduling approach with feedback mechanism in clouds

Workflow scheduling poses a significant challenge in cloud computing environments, offering the potential to optimize execution time and reduce economic costs. In this paper, we present BFWS, a bidirectional workflow scheduling approach with a feedback mechanism, designed to address the deadline-constrained cost-aware workflow scheduling problem. BFWS is built upon a bidirectional scheduling framework that facilitates resource allocation from both the forward and the backward directions of the workflow. This framework incorporates a dynamic sub-deadline prediction method, leveraging prior knowledge and real-time scheduling progress to enhance the accuracy of task sub-deadline predictions. Additionally, a cost-aware resource selection operator is integrated into the framework, enabling the selection of appropriate virtual machines based on considerations of cost and time constraints. To further enhance decision-making and optimize performance, we introduce a feedback mechanism that enables BFWS to adapt and improve over time. Through extensive experiments using real-world workflows, the superior performance of BFWS is validated compared to the state-of-the-art algorithms. Moreover, the effectiveness of the bidirectional scheduling approach and the feedback mechanism is demonstrated individually through rigorous validation experiments. These findings highlight the potential of BFWS in achieving efficient and effective workflow scheduling in cloud computing environments.

Distributionally robust decarbonizing scheduling considering data-driven ambiguity sets for multi-temporal multi-energy microgrid operation

As concerns about environmental sustainability continue to grow, the demand for effective low-carbon energy management becomes increasingly pressing. This study presents a novel framework for multi-temporal multi-energy microgrids (MMGs), integrating advanced low-carbon technologies to meet this imperative. The framework ensures flexible operations to navigate uncertainties stemming from renewable energy sources (RES) and fluctuating energy demand. Facilitating multi-energy transactions, encompassing gas and power exchanges in both markets, the model accommodates uncertainties from RES and demand fluctuations. Objectives include reducing carbon emissions and improving economic efficiency. To address uncertainties in the MMG system, a data-driven distributionally robust optimization (DRO) method is employed. Day-ahead scheduling utilizes a two-stage three-level approach, deploying the column-and-constraints generation (C&CG) algorithm, showcasing the efficiency of DRO in minimizing energy waste and carbon emissions while remaining cost-effective. Practicality is demonstrated through real-time intra-day scheduling using the model predictive control (MPC) algorithm, building upon hourly day-ahead results. The effectiveness of both strategies is evaluated using empirical data from an MMG based on the IEEE 33-bus test system. This cost-saving framework not only achieves a significant carbon reduction of 10.6 % but also provides reliable and adaptable solutions, effectively addressing real-world variations in renewable energy and mitigating potential risks.

Integrating the dynamic frequency security in the real-time scheduling considering the accurate models and network constraints

Integrating the dynamic frequency security in the real-time scheduling considering the accurate models and network constraints

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.

Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM

Fog–cloud-based hierarchical task-scheduling methods are embracing significant challenges to support e-Health applications due to the large number of users, high task diversity, and harsher service-level requirements. Addressing the challenges of fog–cloud integration, this paper proposes a new service/network-aware fog–cloud hierarchical resource-mapping scheme, which achieves optimized resource utilization efficiency and minimized latency for service-level critical tasks in e-Health applications. Concretely, we develop a service/network-aware task classification algorithm. We adopt support vector machine as a backbone with fast computational speed to support real-time task scheduling, and we develop a new kernel, fusing convolution, cross-correlation, and auto-correlation, to gain enhanced specificity and sensitivity. Based on task classification, we propose task priority assignment and resource-mapping algorithms, which aim to achieve minimized overall latency for critical tasks and improve resource utilization efficiency. Simulation results showcase that the proposed algorithm is able to achieve average execution times for critical/non-critical tasks of 0.23/0.50 ms in diverse networking setups, which surpass the benchmark scheme by 73.88%/52.01%, respectively.

O-M3: Real-Time Multi-Cell MIMO Scheduling in 5G O-RAN

O-M³: Real-Time Multi-Cell MIMO Scheduling in 5G O-RAN

Enhancing fault tolerance: dual Q-learning with dynamic scheduling

<span>Cloud computing has revolutionized IT delivery by offering scalable on-demand internet services encompassing software, platforms, and infrastructure. However, cloud services face significant performance challenges due to their susceptibility to failures given their vast operational scale. Implementing fault tolerance in dynamic cloud services is a key challenge, with complex configurations and dependencies complicating deployment. This paper introduces an innovative approach that combines double deep Q-learning (DDQL) with a dynamic fault-tolerant real-time scheduling algorithm (DFTRTSA) to enhance fault tolerance in real-time systems. DDQL, an extension of deep Q-learning, optimizes the fault-tolerance decision-making process. The algorithm adjusts scheduling strategies dynamically based on system conditions and errors. The fusion of DDQL and DFTRTSA aims to create a resilient and adaptive fault-tolerant mechanism, ensuring uninterrupted operation while meeting real-time requirements. This adaptive approach efficiently manages resources, meets deadlines, and gracefully handles errors, as demonstrated through experiments. Our DDQL-DFTRTSA method outperforms conventional fault-tolerant mechanisms in defect tolerance, energy efficiency, downtime reduction, and system dependability. It proves to be an ideal solution for real-time systems in dynamic and unpredictable environments.</span>

Robust optimal dispatch strategy of integrated energy system considering CHP-P2G-CCS

Integrated energy systems (IESs) can improve energy efficiency and reduce carbon emissions, essential for achieving peak carbon emissions and carbon neutrality. This study investigated the characteristics of the CHP model considering P2G and carbon capture systems, and a two-stage robust optimization model of the electricity-heat-gas- cold integrated energy system was developed. First, a CHP model considering the P2G and carbon capture system was established, and the electric-thermal coupling characteristics and P2G capacity constraints of the model were derived, which proved that the model could weaken the electric-thermal coupling characteristics, increase the electric power regulation range, and reduce carbon emissions. Subsequently, a two-stage robust optimal scheduling model of an IES was constructed, in which the objective function in the day-ahead scheduling stage was to minimize the start-up and shutdown costs. The objective function in the real-time scheduling stage was to minimize the equipment operating costs, carbon emission costs, wind curtailment, and solar curtailment costs, considering multiple uncertainties. Finally, after the objective function is linearized with a ψ-piecewise method, the model is solved based on the C&CG algorithm. Simulation results show that the proposed model can effectively absorb renewable energy and reduce the total cost of the system.

Schedule Optimization in Construction Projects using Building Information Modeling (BIM)

The construction industry has witnessed significant advancements with the integration of Building Information Modeling (BIM) into project management practices. This paper explores the application of BIM in the context of schedule optimization for construction projects. BIM, as a comprehensive digital representation of a project's physical and functional characteristics, offers a paradigm shift in how construction schedules are developed, managed, and executed. Through a thorough review of literature and case studies, this paper highlights the key benefits of employing BIM in schedule optimization. These benefits include enhanced communication and collaboration among project stakeholders, improved visualization of construction processes, early clash detection, and real-time schedule updates. The integration of BIM also aids in risk mitigation by identifying potential issues before they impact project timelines and budgets. However, successful implementation of BIM for schedule optimization necessitates a commitment to training and education, as well as the investment in the necessary technological infrastructure. This paper emphasizes the importance of effective collaboration and information sharing among all project participants as crucial factors in harnessing the full potential of BIM for schedule optimization

A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System

Logistics is significantly impacted by quality/quantity issues associated with data collection and data sharing restrictions. Nonetheless, public data from national entities and internet-of-things (IoT) solutions enable the development of integrated tools for performance analysis and real-time optimization of logistics networks. This study proposes a three-module data-driven system architecture that covers (a) logistics data collection tools, (b) logistics services performance evaluation, and (c) the transition to synchromodal systems. Module 1 integrates multisource data from national logistics platforms and embedded devices placed within intermodal containers. A multigraph representation of the problem is conceived. Environmental, economic, and operational data are generated and injected into a digital twin. Thus, key performance indicators (KPIs) are computed by simulation or direct transformation of the collected data. Module 2 uses Multi-directional Efficiency Analysis, an optimization algorithm that benchmarks multimodal transportation routes of containers using prior KPIs. Outputs are a technical performance index relevant to logistics clients and improvement measures for logistics service providers. A real case study application of the solution proposed for Module 2 is presented. Module 3 provides real-time scheduling and assignment models using CP-sat solvers, accommodating varying system dynamics and resource availability, minimizing makespan and operational costs.

A Deep Learning-Based Microgrid Energy Management Method Under the Internet of Things Architecture

Given that the current microgrid incorporates highly connected distributed energy sources, the conventional model control methods do not suffice to support complex and ever-changing operating scenarios. This paper proposes a deep learning-based energy optimization method for microgrid energy management in the new power system scenarios. This article constructs a microgrid cloud edge collaboration architecture, which collects interactive network status data through terminal devices and network edge sides. A microgrid energy management model is constructed based on Bi-LSTM attention in the network cloud. And the model is sunk to provide real-time and efficient comprehensive load and power generation prediction output optimal scheduling decisions at the edge of the network, achieving collaborative control of microgrid light load storage. The simulation based on the actual available microgrid data shows that the proposed Bi-LSTM attention energy management model can achieve rapid analysis and optimize decision-making within 7.3 seconds for complex microgrid operation scenarios.

Optimal energy management of energy hub: A reinforcement learning approach

Increasing energy demand in today’s world emphasizes the importance of optimal scheduling for distributed energy resources to minimize energy costs and greenhouse gas (GHG) emissions. The efficiency of this decision-making process relies on accurate modeling. In this paper, reinforcement learning (RL), an artificial intelligence-based approach, is proposed to optimize the energy management system (EMS) of an energy hub (EH). This EH contains renewable energy resources (RER), a combined heat and power (CHP), and a gas furnace. In order to meet electrical and thermal energy demand, available options such as day-ahead and real-time purchases from the main grid, RERs, and natural gas consumption are managed, with the preference of RERs to minimize GHG emissions and energy costs. With the adaptable RL method, a non-linear model of the CHP operation is constructed, considering the operational costs of the CHP. Furthermore, the natural gas tariff is varied according to the consumption level of the microgrid. Finally, this paper presents an RL-based method for EMS optimization of an EH with day-ahead and real-time scheduling, applied to a 24-hour case study with linear and nonlinear modeling of the problem and sensitivity analysis of the parameters. Corresponding simulation results show the efficiency of the presented approach.

Water level prediction model based on blockchain and LSTM

Aiming at the shortcomings of traditional water level prediction methods such as insufficient information mining ability and unclear mechanism of heuristic algorithms, this paper proposes for the first time a water level prediction method based on blockchain technology fused with long short-term memory (LSTM) network. The method utilizes blockchain and LSTM neural network to build a combined model, and directly uploads monitoring data such as import and export water flow and water level to predict the water level, which avoids the secondary error brought by the indirect calculation of flow. In this paper, the flow compensation strategy is proposed for the first time, and the monitoring data with large deviations are compensated accordingly to reduce the prediction error from the source. The results show that the combined Blockchain-LSTM model has the smallest prediction error after adopting the compensation strategy, with the MAE of 0.290 and the RMSE of 0.490, which are smaller than those of other models, and has high prediction accuracy and practicability, which provides technical support for real-time scheduling of the South-to-North Water Diversion Reservoir.

Knowledge-transfer based genetic programming algorithm for multi-objective dynamic agile earth observation satellite scheduling problem

The multi-objective dynamic agile earth observation satellite scheduling problem (MO-DAEOSSP) aims to schedule a set of real-time arrival requests and form a reasonable observation plan to satisfy various criteria. According to the requirements in practical applications, the total profit and the average image quality of scheduled requests are taken as optimization goals in this study. Compared to manually designed heuristics and iterative-based methods used in previous research, genetic programming based hyper heuristics (GPHH) can automatically evolve high-quality heuristic rules (HRs) for real-time scheduling without being highly dependent on expert knowledge. In this paper, a knowledge-transfer based multi-objective GPHH framework (KT-MOGP) is proposed, equipped with a heuristic-based simulation considering the idle monitoring, to evolve non-dominated HRs for solving MO-DAEOSSP. The heuristic-based simulation generates feasible schedules and returns fitness values for given HRs, which are the individuals evolved by KT-MOGP. KT-MOGP applies a knowledge transfer mechanism to accelerate convergence. Once a source problem is trained, its non-dominated solutions are extracted and their feature importance is transferred to guide the initialization of another target problem, by which the knowledge generated during the training process can be fully utilized. Experimental results on three sets of instances show that KT-MOGP outperforms the existing GPHH-based method and that the evolved HRs are competitive compared to several classical constructive heuristics and multi-objective evolutionary algorithms. The results also show the effectiveness of the proposed knowledge transfer-based initialization. To the best of our knowledge, this study is the first attempt to consider both multi-objective scenarios and real-time arrival requests.

Differential Game-Based Deep Reinforcement Learning in Underwater Target Hunting Task.

To meet requirements for real-time trajectory scheduling and distributed coordination, underwater target hunting task is challenging in terms of turbulent ocean environments and dynamic adversarial environment. Despite the existing research in game-based target hunting area, few approaches have considered dynamic environmental factors, such as sea currents, winds, and communication delay. In this article, we focus on a target hunting system consisted of multiple unmanned underwater vehicles (UUVs) and a target with high maneuverability. Besides, differential game theory is leveraged to analyze adversarial behaviors between hunters and the escapee. However, it is intractable that UUVs have to deploy an adaptive scheme to guarantee the consistency and avoid the escape of the target without collision. Therefore, we conceive the Hamiltonian function with Leibniz's formula to obtain feedback control policies. In addition, it proves that the target hunting system is asymptotically stable in the mean, and the system can satisfy Nash equilibrium relying on the proposed control policies. Furthermore, we design a modified multiagent reinforcement learning (MARL) to facilitate the underwater target hunting task under the constraints of energetic flows and acoustic propagation delay. Simulation results show that the proposed scheme is superior to the typical MARL algorithm in terms of reward and success rate.

Learning-Based Real-Time Aggregate Flexibility Provision and Scheduling of Electric Vehicles

Learning-Based Real-Time Aggregate Flexibility Provision and Scheduling of Electric Vehicles