Real-time Scheduling Research Articles

A framework of cloud-edge collaborated digital twin for flexible job shop scheduling with conflict-free routing

Synchronous scheduling of transportation resources such as automated guided vehicles (AGVs) and production resources can effectively improve the production efficiency of an automated flexible job shop, however limited transportation resources and persistent abnormal disturbances compromise the accuracy, reliability and practicability of such scheduling. In order to address the flexible job shop scheduling problem with limited transportation resources (FJSP-T) under abnormal disturbances, here we propose a cloud-edge combined digital twin (DT) flexible job shop scheduling (CE-DTFJSS) framework to realize real-time scheduling. Firstly, a time-space network based zero-one programming (TSN-ZOP) model is established to optimize makespan and energy consumption of flexible job shop scheduling with conflict-free routing. Subsequently, to achieve real-time interaction between scheduling system and physical workshop, a cloud-edge architecture is introduced to allocate computing resources for scheduling-supporting tasks such as simulation, state monitoring and distributed control in the DT system. Additionally, we design a cloud edge collaboration rescheduling strategy based on a rolling window, to update the parameters’ values in the improved time window Dijkstra nested multi-layer coding genetic algorithm we proposed, thus real-time scheduling can be achieved. The effectiveness of CE-DTFJSS are verified by a numeric simulation based on a real-world case of a digitalized automobile welding workshop. The experimental results indicate that compared with the scheduling scheme without considering AGVs routing problem, the CE-DTFJSS framework can reduce makespan and energy consumption by 8.30% and 3.73%, respectively, and real-time responses to abnormal disturbances can be rendered within 0.1 s.

Autonomous Scheduling for Reliable Transmissions in Industrial Wireless Sensor Networks

Deploying Internet of Things (IoT) on low-power lossy wireless sensor/actuator networks (LLN) in harsh industrial environments presents challenges such as dynamic link qualities due to noise, signal attenuations and spurious interferences. However, the critical demand for industrial applications is reliability of data delivery on low-cost low-power sensor/actuator devices. To address these issues, this paper proposes a fully autonomous scheduling approach, called Auto-Sched, which ensures reliability of data delivery for both downlink and uplink traffic scheduling and enhances network robustness against node/link failures. To ensure reliability, Auto-Sched assigns retransmission time slots based on the reliability constraints of the communication link. To avoid collision issues, Auto-Sched creates an upward pipeline-like communication schedule for uplink end-to-end data delivery, and a downward pipeline-like communication schedule for downlink scheduling. For enhancing network robustness, we propose a simple algorithm for real-time autonomous schedule reconstruction, when node or link failures occur, with minimal influence on communication overhead. Performance evaluations quantified the performance of our proposed approaches under a variety of scenarios comparing them with existing approaches.

Deep reinforcement learning for solving steelmaking-continuous casting scheduling problems under time-of-use tariffs

This paper proposes a novel intelligent scheduling method based on deep reinforcement learning (DRL) to solve the multi-objective steelmaking-continuous casting (SCC) scheduling problem, under time-of-use (TOU) tariffs for the first time. The intelligent scheduling system architecture is designed, and a mathematical model is established to minimise the total sojourn time and electricity cost. To effectively reduce production costs by avoiding peak periods of electricity consumption, the ‘start time’ of the system is generated based on the Markov Decision Process (MDP), and heuristic scheduling rules related to power cost are used as the action space, with corresponding reward functions designed according to the characteristics of these two objectives. To satisfy the continuous casting which is a particular SCC constraint, a backward strategy is developed. Additionally, a branching duelling double deep Q-network (BD3QN) is adapted to guide action selection and avoid blind search in the iteration process, and then applied to real-time scheduling. Numerical experiments demonstrate that the proposed method outperforms comparison algorithms in terms of solution quality and CPU times by a large margin.

A variable neighborhood search heuristic for real-time barge scheduling in a river-to-sea channel with tidal restrictions

The scheduling of barge transportation in narrow channels is critical to transportation efficiency, especially when the tide exists and affects the channel navigation of barges. Barge movements in channels are frequently correlated with other activities (e.g., berth allocation) and must thus be considered concurrently. To enhance transportation efficiency, effective scheduling algorithms are required. In this study, we examine the barge scheduling problem arising from a river-to-sea channel with tidal restrictions in the real world, where barges’ loading and unloading operations are placed at both ends of the navigation channel and must be planned and coordinated with their channel movements. To reduce the overall completion time for all barges, we develop a mixed integer linear programming model with several valid inequalities. To satisfy the second-level computational efficiency requirement, a straightforward yet efficient variable neighborhood search algorithm is proposed. Real-world case studies based on a barge transportation project in Boffa, Guinea are conducted. It is demonstrated that the proposed approach is applicable for efficient decision-making and effective in assisting barges to avoid missing feasible tide windows by slightly altering the first-come-first-serve schedule of the barges. With the proposed approach, the size and speed of the barge fleet can be optimized, and the tidal influence is also evaluated quantitatively.

A real-time production scheduling method for RFID-enabled semiconductor back-end shopfloor environment in industry 4.0

PurposeA real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.Design/methodology/approachThe architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.FindingsThe authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.Originality/valueThis paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

Real-Time Scheduling of Conditional DAG Tasks With Intra-Task Priority Assignment

The conditional DAG task model can represent the conditional execution flows that commonly exist in many real-time parallel applications. Previous work has shown that by properly assigning the priority among vertices inside a non-conditional DAG task, we can reduce the task response time and achieve better system schedulability. This paper studies how to apply intra-task priority assignment to conditional DAG tasks. We develop a response time bound that theoretically dominates the state-of-the-art and present a novel algorithm to compute the bound in polynomial time. We further extend the proposed approach to the general setting of multiple conditional DAG tasks. Experiments with one conditional DAG task and multiple conditional DAG tasks demonstrate that our method consistently outperforms the state-of-the-art by a considerable margin.

MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform

Heterogeneous architecture-based systems-on-chip enable the development of flexible and powerful multifunctional RF systems. In complex and dynamic environments where applications arrive continuously and stochastically, real-time scheduling of multiple applications to appropriate processor resources is crucial for fully utilizing the heterogeneous SoC’s resource potential. However, heterogeneous resource-scheduling algorithms still face many problems in practical situations, including generalized abstraction of applications and heterogeneous resources, resource allocation, efficient scheduling of multiple applications in complex mission scenarios, and how to ensure the effectiveness combining with real-world applications of scheduling algorithms. Therefore, in this paper, we design the Multi-Application Scheduling Algorithm, named MASA, which is a two-phase scheduler architecture based on Deep Reinforcement Learning. The algorithm is made up of neural network scheduler-based task prioritization for dynamic encoding of applications and heuristic scheduler-based task mapping for solving the processor resource allocation problem. In order to achieve stable and fast training of the network scheduler based on the actor–critic strategy, we propose optimization methods for the training of MASA: reward dynamic alignment (RDA), earlier termination of the initial episodes, and asynchronous multi-agent training. The performance of the MASA is tested with classic directed acyclic graph and six real-world application datasets, respectively. Experimental results show that MASA outperforms other neural scheduling algorithms and heuristics, and ablation experiments illustrate how these training optimizations improve the network’s capacity.

Improved reinforcement learning-based real-time energy scheduling for prosumer with elastic loads in smart grid

In this study, we investigate the dynamic energy-scheduling problem of a prosumer (producer/consumer) with an energy storage device (EDS) and elastic loads. The goal is to develop efficient real-time scheduling strategies for prosumers, and to minimise their total long-term costs (i.e. cost of energy purchased from the external grid and depreciation cost of the EDS). The challenges are twofold: the uncertainty of the energy output of the prosumer and the time-coupling constraints of the EDS and elastic loads. To address these challenges, we first describe the dynamic energy-scheduling problem as a Markov decision process. Then, an approximate state dual-agent Q-learning algorithm is proposed to solve the optimal dynamic scheduling problem by improving the model-free reinforcement learning(RL) method. Compared with the traditional RL method, the proposed algorithm reduces the system-state dimensions and exhibits improved performance. The proposed algorithm can only be assisted by mutual interactions between the environment with a reward feedback mechanism to dynamically respond to uncertain changes in the environments, without modelling or predicting the system environments. Finally, extensive empirical evaluations using real-world traces are conducted to study the effectiveness of the proposed algorithm. The results show that the proposed algorithm can reduce the total cost of the prosumer by up to 6.3%, 11.7% and 22.4% compared with the traditional RL method, Lyapunov optimisation and greedy algorithm, respectively.

A Blockchain-Based Multi-Unmanned Aerial Vehicle Task Processing System for Situation Awareness and Real-Time Decision

With the rapid advancement of Unmanned Aerial Vehicle (UAV) technology, UAV swarms are being extensively applied in various fields, such as intelligent transportation, search and rescue, logistics delivery, and aerial mapping. However, the utilization of UAV swarms in sustainable transportation also presents some challenges, such as inefficient task allocation and data transmission security issues, highlighting the importance of privacy protection in this context. To address these issues, this study applies blockchain technology to multi-UAV tasks and proposes a blockchain-based multi-UAV task processing system for situation awareness and real-time decisions. The primary objective of this system is to enhance the efficiency of UAV swarm task scheduling, bolster data transmission security, and address privacy protection concerns. Utilizing the highly secure features of blockchain technology, the system constructs a distributed task processing network. System tasks are stored in the blockchain through smart contracts, ensuring the immutability and verifiability of task information. Smart contracts have an automatic execution capability, whereby the system can efficiently coordinate tasks and maintain the consistency of task execution information through consensus mechanisms. Additionally, adopting the Pointer Network structure for intelligent path planning based on task allocation results leads to the attainment of the shortest service routes, consequently expanding the service coverage of sustainable transportation systems while reducing energy consumption. This further advances the realization of urban sustainable transportation. Through experimental results, we verify that the proposed system enables real-time task scheduling and collaborative processing for multiple UAVs, significantly enhancing the efficiency, security, and privacy protection level of UAV swarm task execution in the context of sustainable transportation. It makes a positive contribution to building more sustainable urban transportation systems.

Deep reinforcement learning-based approach for dynamic disassembly scheduling of end-of-life products with stimuli-activated self-disassembly

Remanufacturing is one of the most critical strategies for end-of-life product management to promote a circular economy; however, it has been seen very limited implementation due to the labor-intensive and time-consuming disassembly processes for component retrieval. The newly emerged 4D printing technology enables the fabrication of stimuli-responsive reconfigurable structures, outlining new ways to achieve non-destructive and simultaneous self-disassembly of components with different geometry. However, large uncertainties and increased process dynamics have also emerged directly pertaining to the real-time scheduling in disassembly lines with self-disassembly workstations, which the existing scheduling methods are not equipped to handle. In this study, a constrained multi-agent deep reinforcement learning approach is proposed to maximize the disassembly profit by dynamically changing the batch mixing ratios of different-sized components in self-disassembly workstations and adapting real-time scheduling to stochastic product quality, changes in operational sequences, and self-disassembly failures. The proposed approach is validated on a disassembly line for hand pulse detectors that contain heat-activated self-disassembly components. Numerical results show that the proposed achieves stable convergence under uncertainties, and the implementation of a dynamic batch mixing scheme in self-disassembly operations yields a substantial improvement in disassembly profit over the scheduling period. In addition, sensitivity analyses are conducted to evaluate the impacts of system uncertainties on the profitability of the disassembly line.

Protection Window Based Security-Aware Scheduling against Schedule-Based Attacks

With widespread use of common-off-the-shelf components and the drive towards connection with external environments, the real-time systems are facing more and more security problems. In particular, the real-time systems are vulnerable to the schedule-based attacks because of their predictable and deterministic nature in operation. In this paper, we present a security-aware real-time scheduling scheme to counteract the schedule-based attacks by preventing the untrusted tasks from executing during the attack effective window (AEW). In order to minimize the AEW untrusted coverage ratio for the system with uncertain AEW size, we introduce the protection window to characterize the system protection capability limit due to the system schedulability constraint. To increase the opportunity of the priority inversion for the security-aware scheduling, we design an online feasibility test method based on the busy interval analysis. In addition, to reduce the run-time overhead of the online feasibility test, we also propose an efficient online feasibility test method based on the priority inversion budget analysis to avoid online iterative calculation through the offline maximum slack analysis. Owing to the protection window and the online feasibility test, our proposed approach can efficiently provide best-effort protection to mitigate the schedule-based attack vulnerability while ensuring system schedulability. Experiments show the significant security capability improvement of our proposed approach over the state-of-the-art coverage oriented scheduling algorithm.

A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors

As multiprocessor (or multicore) real-time systems become popular, there has been much research on multiprocessor real-time scheduling algorithms. This work evaluates EDZL (Earliest Deadline until Zero Laxity), a scheduling algorithm for real-time multiprocessor systems. First, we compare the performance of EDZL schedulability tests. We measure and compare the ratio of task sets admitted by each test. We also investigate the dominance between EDZL schedulability tests and discover that the union of the demand-based test and the utilization-based test is an effective combination. Second, we compare the schedulability of EDZL and EDF(k). We prove that the union of the EDZL schedulability tests dominates the EDF(k) schedulability test, i.e., the union of the EDZL schedulability tests can admit all task sets admitted by the EDF(k) schedulability test. We also compare the schedulability of EDZL and EDF(k) through scheduling simulation by measuring the ratio of successfully scheduled task sets. EDZL can successfully schedule 7.0% more task sets than EDF(k).

Transactive Demand–Response Framework for High Renewable Penetrated Multi-Energy Prosumer Aggregators in the Context of a Smart Grid

Demand–response (DR) can provide the economic flexibility required to adapt a high proportion of renewable energy in the context of a smart grid. This paper proposes a transactive DR framework to enable the multi-time-scale proactive participation of demand-side flexible multi-energy resources. In this framework, the distribution system operator distributes the real-time DR request and the high renewable penetrated multi-energy prosumer aggregators provide the ancillary services based on their adjustable potential. To facilitate such multi-time-scale prosumer–operator interactions, a flexibility potential evaluation method is developed for the quantification and pricing of prosumer flexibility. The positive and negative flexibility potential of the demand-side prosumer aggregators are defined as deviations from the optimal pre-dispatch operation, which are further quantified using the aspects of flexible time and power. Based on the introduction of a flexibility pricing mechanism to identify the economically optimal ancillary service requirements, each prosumer aggregator performs an optimal real-time DR scheduling. Case studies over several DR schemes are performed to confirm the effectiveness and superiority of the proposed method on the economy and flexibility of the system.

Generating Datasets for Real-Time Scheduling on 5G New Radio.

A 5G system is an advanced solution for industrial wireless motion control. However, because the scheduling model of 5G new radio (NR) is more complicated than those of other wireless networks, existing real-time scheduling algorithms cannot be used to improve the 5G performance. This results in NR resources not being fully available for industrial systems. Supervised learning has been widely used to solve complicated problems, and its advantages have been demonstrated in multiprocessor scheduling. One of the main reasons why supervised learning has not been used for 5G NR scheduling is the lack of training datasets. Therefore, in this paper, we propose two methods based on optimization modulo theories (OMT) and satisfiability modulo theories (SMT) to generate training datasets for 5G NR scheduling. Our OMT-based method contains fewer variables than existing work so that the Z3 solver can find optimal solutions quickly. To further reduce the solution time, we transform the OMT-based method into an SMT-based method and tighten the search space of SMT based on three theorems and an algorithm. Finally, we evaluate the solution time of our proposed methods and use the generated dataset to train a supervised learning model to solve the 5G NR scheduling problem. The evaluation results indicate that our SMT-based method reduces the solution time by 74.7% compared to existing ones, and the supervised learning algorithm achieves better scheduling performance than other polynomial-time algorithms.

A Multi-core Based Real-time Scheduler Supporting Periodic and Sporadic Threads and Processes

A Multi-core Based Real-time Scheduler Supporting Periodic and Sporadic Threads and Processes

Optimal scheduling of CCHP-based resilient energy distribution system considering active microgrids' multi-carrier energy transactions

This paper introduces a two-stage two-level optimization method for optimal day-ahead and real-time scheduling of multicarrier energy distribution systems and microgrids. The model considers the incentive-based and price-based demand response programs to encourage microgrids to transact electrical, heating, and cooling energy carriers with the energy distribution system, which is named hereafter as the energy system. Further, the model formulates the resilient operation of the energy system considering the energy transactions with the electrical, heating, and cooling markets. The main contribution of this paper is the integration of demand response procedures of microgrids in energy transactions with the energy system considering the switching of electrical switches and heating and cooling control valves. The optimization process is another contribution of this paper that is decomposed into two stages that consist of day-ahead and real-time horizons. The first stage is also decomposed into two levels that determine the optimal scheduling of the energy system and microgrids in day-ahead markets. The second stage is comprised of two levels that commit the energy system and microgrids resources. A resiliency index is proposed to assess the resiliency of the energy system in shock conditions. The proposed method was simulated for the 123-bus test system. Different types of microgrids, incentive-based and price-based demand response processes were considered. Simulation results confirmed that the proposed method can reduce the costs of residential, industrial, and commercial microgrids by about 4.47%, 3.88%, and 5.47% concerning only the real-time pricing process. Further, the model can increase the aggregated benefits of the energy system in the day-ahead and real-time markets by about 0.608 Million Monetary Units (MMUs) and 1.10 MMUs, respectively.

A multi-time scale coordinated control and scheduling strategy of EVs considering guidance impacts in multi-areas with uncertain RESs

With the increased electrification of transportation sector, the electric vehicles (EVs) are deemed to be key players in energy scheduling act to realize more economical operation of distribution networks. EVs have the function of energy space–time transfer, and energy-space coupling effects need to be considered in scheduling. In this paper, EVs’ spatial characteristics from multi-areas and characteristics of transferable charging power are both taken into account, then a multi-time scale coordinated control and scheduling strategy is proposed to achieve optimal schedules in both day-ahead (DA) and real-time (RT) periods. First, in DA periods, EVs are modeled as shiftable and location-flexible loads to participate in multi-areas’ scheduling task managed by a unified distribution system operator (DSO). To attract more spatially distributed EVs to different charging stations and avoid charging congestion, a price-based transfer model (PBTM) is established to realize EVs charging guidance in different areas while integrated into the DA stochastic scheduling. Next, in RT periods, EVs are modeled as controllable loads to compensate RT power errors caused by uncertain renewable energy sources (RESs) and inaccuracies associated with DA prediction. Both DA and RT scheduling are coordinated with a RT control strategy for EVs, in which a state space model (SSM) is constructed to calculate charging power and then form 1-min control signals to realize the tracking of multi-time scale schedule. Simulation results demonstrate that the proposed coordinated control and scheduling strategy can guide more EVs to be grid-connected, promote multi-time scale economic scheduling, and benefit the EV users meanwhile.

Multi-objective sizing and real-time scheduling of battery energy storage in energy-sharing community based on reinforcement learning

Reducing peak demand and enhancing self-sufficiency have made the battery energy storage system (BESS) essential in microgrids when combined with photovoltaic (PV) systems. Previous studies have shown that BESS usage remains expensive, and it is more practical to share it within a community rather than installing individual systems. Therefore, this study aimed to propose a framework for BESS sizing and scheduling in an energy-sharing community based on reinforcement learning. To validate the proposed framework, a case study was conducted based on the BESS ownership scenarios (i.e., individual-owned BESS (IOB) and community-shared BESS (CSB)) considering the following purposes of potential stakeholders: (i) enhancing SSR; (ii) reducing peak demand; and (iii) increasing economic profit. The maximum SSR of CSB was 12.4% higher than IOB, while the peak load of CSB was 0.07% lower than IOB. As the BESS size increased, the total cost of BESS increased, and the total profits on electricity decreased. The economic profit increased by about 38% when CSB was used compared to IOBs. The proposed framework offers more promise for a broader range of building loads and PV generation within the energy-sharing community. Ultimately, this study would allow prosumers, community practitioners, and policymakers to better understand the sharing mechanisms in the community and provide decision guidelines for ownership types, operational strategies, and sizing of BESS and PV systems.

Contention-Free Scheduling for Single Preemption Multiprocessor Platforms

The Contention-Free (CF) policy has been extensively researched in the realm of real-time multi-processor scheduling due to its wide applicability and the performance enhancement benefits it provides to existing scheduling algorithms. The CF policy improves the feasibility of executing other real-time tasks by assigning the lowest priority to a task at a moment when it is guaranteed not to miss its deadline during the remaining execution time. Despite its effectiveness, existing studies on the CF policy are largely confined to preemptive scheduling, leaving the efficiency and applicability of limited preemption scheduling unexplored. Limited preemption scheduling permits a job to execute to completion with a limited number of preemptions, setting it apart from preemptive scheduling. This type of scheduling is crucial when preemption or migration overheads are either excessively large or unpredictable. In this paper, we introduce SP-CF, a single preemption scheduling approach that incorporates the CF policy. SP-CF allows a preemption only once during each job’s execution, following a priority demotion under the CF policy. We also propose a new schedulability analysis method for SP-CF to determine whether each task is executed in a timely manner and without missing its deadline. Through simulation experiments, we demonstrate that SP-CF can significantly enhance the schedulability of the traditional rate-monotonic algorithm and the earliest deadline first algorithm.

Adaptive real-time reconfiguration gate scheduling scheme using time perceptive stream

An adaptive real-time gate scheduling scheme for time perceptive stream or packet flow is proposed to improve the standards of Ultra Low Latency during data transmission. For highly dynamic network conditions, the conventional configuration scheme is not suitable and therefore an adaptive real-time gate scheduling method is proposed for time perceptive streams. This dynamicity reconfiguration is difficult and the scheduling problem is formulated using Field Programmable Gate Array – Boolean Satisfiability Problem (FPGA-BSP) solver. This proposed scheme highly helps in network dynamicity conditions with good bandwidth utilization and high flexibility. End-to-end latency is required to be on sub-milliseconds order deal with the applications such as Industrial Internet of Things, 5G and 6G mobile, tactile internet and so on. Simulation analysis is carried out to prove the efficiency of the proposed model.