Stable Scheduling Research Articles

Stochastic optimal scheduling strategy for a campus-isolated microgrid energy management system considering dependencies

Isolated microgrids have been widely used on campuses, becoming an important part of their power-supply infrastructure. In this study, a stochastic optimal scheduling strategy that considered dependencies was proposed for the energy management system (EMS) of an isolated microgrid on campus, which consists of three main components: an accurate forecasting model on both the demand and supply sides, an excellent core scheduling strategy, and an effective uncertainty analysis. First, the forecasting model involved data correction, feature selection, core forecasting, and error analyses to obtain consistently good forecasting results. A multilayer hybrid forecasting model was developed, including a stacked multilevel denoising autoencoder, mutual information, bidirectional long short-term memory network (LSTM), and LSTM, corresponding to the above four model elements. Based on the forecasting results, improved episodic deep reinforcement learning was used to build a stochastic optimal scheduling strategy for the EMS, which could realize rapid propagation of reward values and achieve stable updating of episodic memory, effectively improving sample efficiency. The objective of optimizing the scheduling was to obtain the lowest operational cost. Finally, the uncertainties in the forecasting process were analyzed using the Latin hypercube sampling method, and a regular vine copula was used to determine the dependence of the uncertainty analysis between buildings. To the best of our knowledge, this is the first study to consider dependence in the uncertainty analysis of a campus microgrid. The performance of the proposed method was evaluated by comparing it with other existing models, aiming to obtain more efficient and stable scheduling results.

Film dance creation practice supported by Cyber Physical System.

The traditional eight arts include film and dance. Dance is often included in the creation of films. With the progress of the times, dance has shown unprecedented vitality in film. This also puts forward higher requirements for shooting technology in film creation. To solve the contradiction between high performance of equipment and energy sensing, a new energy aware scheduling framework is proposed based on Cyber Physical System, which utilizes the balance between performance and energy consumption optimization, and uses allocation technology and list scheduling to ensure energy constraints. Besides, a highly energy-efficient and stable scheduling algorithm for film creation is constructed. The algorithm problems are mainly divided into functional safety requirements, verification problems, and energy consumption optimization problems under functional safety. The experimental results show that the system can obtain better schedulability at a lower time complexity and reasonably reflect the dynamic and static energy constraints ratio. The basic framework system based on dynamic step size also achieves better time performance than other step sizes. According to the experiment's findings, the energy consumption of all scheduling components is relatively low and can be maintained within a specific range. The research and analysis of this study can provide a theoretical reference for the equipment algorithm of film dance creation practice, promote interaction with technical practice, and assist in promoting the development process of film dance creation.

Shifting Hours: Unstable Work Scheduling Practices

Stable work schedules are a key component of job quality and of supporting a thriving labor force. Stable scheduling practices are associated with improved job attachment, lower turnover, and higher revenues.i On the other hand, employer-initiated unstable scheduling practices have been shown to destabilize workers’ finances, sleep, caregiving, education, other employment, and community and leisure activities, and are associated with negative health outcomes, reduced worker satisfaction, and increased turnover.ii Though unstable scheduling practices are widespread, with about 41% of all workers experiencing such practices, hourly and part-time workers and workers in low-wage occupations are especially affected. Further, due to occupational segregation,iii workers of color are disproportionately impacted by unstable scheduling and its negative outcomes. These inequities in scheduling instability were only compounded by the COVID-19 pandemic.iv As jobs have been added back into low-wage industries hit hard by the pandemic, workers’ hours in many of those jobs have remained low and unstable.v Unstable scheduling practices exacerbate the increasingly steep tradeoffs that low-income households face in navigating the costs of low-wage work, including the costs of transportation, housing within a reasonable distance of work, and caregiving. This instability in work hours and income makes it particularly difficult to qualify for employer and state benefits, access reliable care, pursue education or training, and consistently cover rising basic costs of living. As these factors play a role in household employment decisions, understanding unstable scheduling is an important consideration for the Federal Reserve’s dual mandate. This brief outlines the employer practices that comprise unstable scheduling, how underwork and overwork—and fluctuations between the two—can be understood as intertwined with unstable scheduling, and how we can measure the prevalence of unstable scheduling practices across industries. What follows is a discussion of the inequitable distribution and impacts of unstable scheduling, the negative effects of unstable scheduling practices for all workers, and the benefits of stable scheduling for both workers and employers. The final section discusses how potential solutions could address both quality and quantity of work hours, as well as the related challenges of accessing benefits and affordable care.

Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background.

Large-scale and widely dispersed distributed energy resource (DER) can be gathered by a virtual power plant (VPP) in a given area, and its parameters can be combined into a single external operation profile. Each distributed energy source in the VPP has a complete backup of the critical information for the entire network because it is a node of blockchain. The distribution network can be accessed by DER freely and adaptable under the scientific management of the VPP, and it can offer the system high-reliability, high-quality, and high-security power services. An energy blockchain network model based on particle swarm optimization (PSO) to optimise the neural network is proposed in this paper as a solution to the issues with the current VPP models. This will enable distributed dispatching of the VPP and reasonable load distribution among units. According to the simulation results, this algorithm's error is minimal and its accuracy can reach 94.98 percent. This model can more accurately capture demand-side real-time information, which benefits VPP's stable scheduling with a welcoming environment and transparent information. It also enhances the system's data security and storage security. This system can successfully address the issues of subject-to-subject mistrust and high information interaction costs in the VPP.

Robust scheduling for flexible machining job shop subject to machine breakdowns and new job arrivals considering system reusability and task recurrence

This paper focuses on the production scheduling problem of flexible job shops. In the production process of flexible job shop, there are dynamic events such as machine breakdowns or new job arrivals, which will interfere with the implementation of scheduling scheme and reduce the stability of the system. In response to this problem, this paper proposes a new robust optimization method that considers dynamic events and designs two indicators for evaluating the robustness of the system, namely the reusability of the system and the reproducibility of processing tasks. Two indicators are used to evaluate the comprehensive reusability of the system jointly. In the process of system rescheduling, this paper proposes a dynamic event response strategy (DERS) considering the comprehensive reusability of the system and establishes a multi-objective optimization model considering the total energy consumption, the makespan, and the comprehensive reusability of the system. In order to solve the model efficiently and obtain the optimal Pareto frontier, a multi-objective particle swarm arithmetic optimization (PSAO) is proposed in this paper. Finally, this paper designs experiments based on standard data cases, solves them based on this model and compares them with other algorithms. The final results show that in the flexible job-shop scheduling process, this method can effectively adjust the scheduling plan to respond to dynamic events to achieve stable scheduling in an uncertain environment.

Stabilizing transmission intervals and delays in nonlinear networked control systems through hybrid-system-with-memory modeling and Lyapunov–Krasovskii arguments

This article employs the hybrid-system-with-memory formalism to attain transmission intervals and delays that provably stabilize Networked Control Systems (NCSs). Nonlinear time-varying plants and controllers with variable discrete and distributed input, output and state delays along with nonconstant discrete and distributed network delays are considered. We bring together nominal system L2-stability, Uniformly Globally Exponentially Stable (UGES) scheduling protocols and linear upper bounds of network-induced output error dynamics to infer Uniform Global pre-Asymptotic Stability (UGpAS) of the closed-loop system via Lyapunov–Krasovskii arguments. Namely, we replace the Lyapunov–Razumikhin conditions and trajectory-based small-gain theorem with linear L2-gains arguments, featured in our previous works, with Lyapunov–Krasovskii functionals to prove UGpAS of interconnected hybrid systems with memory. The present methodology allows for more general delays (e.g., multiple input/output/state discrete and distributed delays) and output error dynamics (e.g., multiple discrete and distributed delays) as well as less conservative estimates of Maximally Allowable Transfer Intervals (MATIs). Our results are applicable to control problems with output feedback and the so-called large delays, that is, delays larger than the transmission intervals. In addition, model-based estimators between two consecutive updates, rather than merely the Zero-Order Hold (ZOH) strategy, are allowed for in order to prolong MATIs. Lastly, a nonlinear numerical example involving a single-link robot arm and observer–predictor-based control law is provided to illustrate our theoretical findings.

Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution

In this paper, a self-adaptive differential evolution (DE) algorithm is designed to solve multi-objective flow shop scheduling problems with limited buffers (FSSPwLB). The makespan and the largest job delay are treated as two separate objectives which are optimized simultaneously. To improve the performance of the proposed algorithm and eliminate the difficulty of setting parameters, an adaptive mechanism is designed and incorporated into DE. Moreover, various local search and hybrid meta-heuristic methods are presented and compared to improve the convergence. Through the analysis of the experimental results, the proposed algorithm is able to tackle the FSSPwLB problems effectively by generating superior and stable scheduling strategies.

Robust and stable flexible job shop scheduling with random machine breakdowns: multi-objectives genetic algorithm approach

In this paper, robust and stable scheduling for a flexible job-shop problem with random machine breakdowns has been discussed. A two-stage genetic algorithm is used to generate the predictive schedule. The first stage optimises the primary objective, which minimises the makespan, where all data is considered to be deterministic with no expected disruptions. The second stage optimises two objectives, makespan and stability, function in the presence of random machine breakdowns. For the second stage two different versions of multi-objective genetic algorithm, non-dominated sorting genetic algorithm II and non-dominated ranking genetic algorithm, is used. A simulator is proposed to simulate random machine breakdowns. An experimental study and analysis of variance is conducted to study the results of each multi-objective algorithm and breakdown simulator. The results of their comparison indicate that, non-dominated ranking genetic algorithm (NRGA) performs better and also shows a significant difference between various repair times in the proposed breakdown simulator.

Stability analysis of networked linear control systems with direct-feedthrough terms

We consider networked control systems (NCSs) composed of a linear plant and a linear controller interconnected by packet-based communication channels with communication constraints. We are interested in the setup where direct-feedthrough terms are present in the plant and/or in the controller, a case that is largely ignored in the literature due to its inherent complexity and counterintuitive results in the analysis despite its relevance for important classes of controllers including Proportional–Integral (PI) regulators. This setup calls for a novel stability analysis, for which we take a renewed look at the concept of uniformly globally exponentially stable (UGES) scheduling protocols that turned out to be instrumental in earlier approaches. We provide a generalization of the UGES property, called (DP,DC)-UGES with DP∕DC being the direct-feedthrough matrices of the plant/controller, respectively, and we present generic conditions on these direct-feedthrough terms DP∕DC such that the classical UGES property of scheduling protocols implies (DP,DC)-UGES. This allows us to derive conditions leading to a maximally allowable transmission interval (MATI) such that stability of the overall NCS is guaranteed. In addition, it is shown that it is possible to get more tailored results for the well-known sampled-data (SD), round-robin (RR), and try-once-discard (TOD) protocols leading to less conservative conditions on the direct-feedthrough terms than the generic ones. We also introduce new (DP,DC)-UGES scheduling protocols, designed to handle the direct-feedthrough terms in a more effective way than existing protocols. Our results are illustrated using the example of a batch reactor.

Scheduling Policies for Wireless Downlink With Correlated Random Connectivity and Multislot Reconfiguration Delay

Stable and minimum delay scheduling of wireless downlinks, with time correlated random connectivity between the base station and each associated user, is an important problem in modern communication systems. We assume that the base station of the wireless downlink dynamically switches between the users to transmit packets to each user. The dynamic switching of the base station incurs a reconfiguration delay. We find that as the reconfiguration delay increases the stability region of the wireless downlink with correlated channel connectivity shrinks to that achieved by time sharing among the users over large time periods. Since existing scheduling policies are designed either for single slot reconfiguration delay and correlated random connectivity, or multislot reconfiguration delay but without correlated connectivity, we propose scheduling policies which explicitly takes into account the correlated channel connectivity and multislot reconfiguration delay and show using simulations that the average delay is reduced compared to existing policies.

Virtual Network Function Scheduling: A Matching Game Approach

Network function virtualization is a promising technique for telecom providers to efficiently manage network services at low cost. However, existing works mainly focus on resource allocation and thus leave behind an important issue: the virtual network function (VNF) scheduling. Current approaches, e.g., round-robin scheduling or heuristic algorithms, still expose some unsolved issues, such as high computational cost and inability to perform online scheduling. In this letter, we propose a matching-based algorithm to solve the NP-hard VNF scheduling problem. This approach can guarantee a stable scheduling, in which all network services are satisfied with the assignment. Finally, the effectiveness of our method is verified through numerical evaluation, showing that our approach can increase the number of completed VNFs by 36.8% compared with the current round-robin method.

Be Stable and Fair: Robust Data Scheduling for Vehicular Networks

The stable and fair data transmission of vehicular networks can improve transport efficiency and reduce traffic accident. It is challenging to ensure the stability and fairness of data transmission in dynamic vehicular networks. However, existing works based on opportunistic scheduling cannot support reliable transmission since stability and fairness are difficult to be guaranteed at the same time. In this paper, we propose a stable scheduling scheme for dynamic vehicular networks based on fair allocation of available channel resources. We formulate the problem of stability and fairness as network utility maximization, and propose an algorithm, called SF-NUM, to solve it. SF-NUM uses the stability scheduling method based on back pressure vector for the first time in vehicular networks. In order to achieve the fairness distribution of channel resources of the vehicular networks in the distributed computing way, multiple factors are considered in the SF-NUM algorithm. The experimental results show that the proposed algorithm outperforms other algorithms.

Bee and Frog Co-Evolution Algorithm and its application

In order to obtain better generalization abilities and mitigate the impacts of the best and worst individuals during the process of optimization, this paper suggests Bee and Frog Co-Evolution Algorithm (abbreviation for BFCEA), which combines Mnemonic Shuffled Frog Leaping algorithm With Cooperation and Mutation (abbreviation for MSFLACM) with improved Artificial Bee Colony (abbreviation for ABC). The contrast experimental study about different iteratively updating strategies was acted in BFCEA, including strategy of integrating with ABC, regeneration of the worst frog and its leaping step. The key techniques focus on the first 10 and the last 10 frogs evolving ABC in BFCEA, namely, the synchronous renewal strategy for those winner and loser should be applied, after certain G times’ MSFLACM-running, so as to avoid trapping local optimum in later stage. The ABC evolution process will be called between all memes’ completing inner iteration and all frogs’ outer shuffling, the crossover operation is removed from MSFLACM for its little effect on time-consuming and convergence in this novel algorithm. Besides, in ABC, the scout bee is generated by Cauchy mutating instead at random. The performance of proposed approach is examined by well-known 16 numerical benchmark functions, and obtained results are compared with basic Shuffled Frog Leaping algorithm (abbreviation for SFLA), ABC and four other variants. The experimental results and related application in cloud resource scheduling show that the proposed algorithm is effective and outperforms other variants, in terms of solution quality and convergence, and the improved variants can obtain a lower degree of unbalanced load and relatively stable scheduling strategy of resources in complicated cloud computing environment.

Stabilizing Transmission Intervals for Nonlinear Delayed Networked Control Systems

In this note, we consider a nonlinear process with delayed dynamics to be controlled over a communication network in the presence of disturbances and study robustness of the resulting closed-loop system with respect to network-induced phenomena such as sampled, distorted, delayed and lossy data as well as scheduling protocols. For given plant-controller dynamics and communication network properties (e.g., propagation delays and scheduling protocols), we quantify the control performance level (in terms of ${\mathcal{L}}_{p}$ - gains) as the transmission interval varies. Maximally Allowable Transfer Interval (MATI) labels the greatest transmission interval for which a prespecified ${\mathcal{L}}_{p}$ - gain is attained. The proposed methodology combines impulsive delayed system modeling with Lyapunov-Razumikhin techniques to allow for MATIs that are smaller than the communication delays. Other salient features of our methodology are the consideration of variable delays, corrupted data and employment of model-based estimators to prolong MATIs. The present stability results are provided for the class of Uniformly Globally Exponentially Stable (UGES) scheduling protocols. The well-known Round Robin (RR) and Try-Once-Discard (TOD) protocols are examples of UGES protocols. Finally, a nonlinear example is provided to demonstrate the benefits of the proposed approach.

A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms

This paper addresses the stable scheduling of multi-objective problem in flexible job shop scheduling with random machine breakdown. Recently, numerous studies are conducted about robust scheduling; however, implementing a scheme which prevents a tremendous change between scheduling and after machine breakdown (preschedule and realized schedule, respectively) can be critical for utilizing available resources. The stability of the schedule can be detected by a slight deviation of start and completion time of each job between preschedule and realized schedule under the uncertain conditions. In this paper, two evolutionary algorithms, NSGA-II and NRGA, are applied to combine the improvement of makespan and stability simultaneously. A simulation approach is used to evaluate the state and condition of the machine breakdowns. After the introduction of the evaluation criteria, the proposed algorithms are tested on a variety of benchmark problems. Finally, through performing statistical tests, the algorithm with higher performance in each criterion is identified.

A Stable Online Scheduling Strategy for Real-Time Stream Computing Over Fluctuating Big Data Streams

The issue of high system stability is one of the major obstacles for real-time computing over fluctuating big data streams. A stable scheduling is more important than an efficient scheduling for stream applications, especially when a scheduling is to be rescheduled dynamically at runtime. In this paper, a stable online scheduling strategy with makespan guarantee SOMG is discussed, which includes the following features: 1) profiling mathematical relationships between system stability, response time, and resource utilization, and indicating conditions to meet the high system stability and acceptable response time objectives; 2) optimizing the structure of a data stream graph by quantifying and adjusting vertices of the graph; and 3) scheduling a data stream graph with heuristic critical path scheduling mechanism, which is subject to response time constraints, rescheduling only key vertices on dynamically changing critical path of the graph, and considering the historical information of current scheduling to maximize system stability with response time aware. Experimental results conclusively demonstrate that the SOMG framework has higher potential of providing enhancement on efficient system stability and guaranteeing significant response time. It efficiently and effectively makes a tradeoff between high system stability and acceptable response time objectives in big data stream computing environments.

Capacity and Stable Scheduling in Heterogeneous Wireless Networks

Heterogeneous wireless networks (HetNets) provide a means to increase network capacity by introducing small cells and adopting a layered architecture. HetNets allocate resources flexibly through time sharing and cell range expansion/contraction allowing a wide range of possible schedulers. In this paper, we define the capacity of a HetNet down link in terms of the maximum number of downloads per second, which can be achieved for a given offered traffic density. Given this definition we show that the capacity is determined via the solution to a continuous linear program (LP). If the solution is smaller than 1 then there is a scheduler such that the number of mobiles in the network has ergodic properties with finite mean waiting time. If the solution is greater than 1 then no such scheduler exists. These results have clear implications for network planning. The above results continue to hold if a more general class of schedulers is considered.

MPEC strategies for efficient and stable scheduling of hydrogen pipeline network operation

Hydrogen pipeline network operation is important for balancing the hydrogen producing and consuming unit onsite. To make the operation efficient and stable, this article presents a mathematical program which incorporates equilibrium constraints (MPECs) for scheduling of hydrogen pipeline network between hydrogen producing and consuming units within a refinery. The developed model not only handles the multi-component and non-ideal nature of the hydrogen pipeline network, but also allows flow reversals and flow transitions inside the pipeline. A real refinery case study shows that the optimal schedule of pipeline storage could stabilize the hydrogen production, which improves the efficiency of the system. Operating safety is also ensured by the pressure constraints.

Randomized scheduling algorithm for queueing networks

There has recently been considerable interest in design of low-complexity, myopic, distributed and stable scheduling algorithms for constrained queueing network models that arise in the context of emerging communication networks. Here we consider two representative models. One, a queueing network model that captures randomly varying number of packets in the queues present at a collection of wireless nodes communicating through a shared medium. Two, a buffered circuit switched network model for an optical core of future internet to capture the randomness in calls or flows present in the network. The maximum weight scheduling algorithm proposed by Tassiulas and Ephremides [IEEE Trans. Automat. Control 37 (1992) 1936–1948], leads to a myopic and stable algorithm for the packet-level wireless network model. But computationally it is expensive (NP-hard) and centralized. It is not applicable to the buffered circuit switched network due to the requirement of nonpreemption of the calls in the service. As the main contribution of this paper, we present a stable scheduling algorithm for both of these models. The algorithm is myopic, distributed and performs few logical operations at each node per unit time.

Padded Frames: A Novel Algorithm for Stable Scheduling in Load-Balanced Switches

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The load-balanced Birkhoff-von Neumann switching architecture consists of two stages: a load balancer and a deterministic input-queued crossbar switch. The advantages of this architecture are its simplicity and scalability, while its main drawback is the possible out-of-sequence reception of packets belonging to the same flow. Several solutions have been proposed to overcome this problem; among the most promising are the Uniform Frame Spreading (UFS) and the Full Ordered Frames First (FOFF) algorithms. In this paper, we present a new algorithm called Padded Frames (PF), which eliminates the packet reordering problem, achieves 100% throughput, and improves the delay performance of previously known algorithms. </para>