Dynamic Scheduling Decisions Research Articles

Comparison study of dispatching rules and heuristics for online scheduling of single machine scheduling problem with predicted release time jobs

In real-life dynamic scheduling systems, the release times of some important and/or urgent jobs are usually known deterministically or predictably in advance. When making dynamic scheduling decisions, we are expected to get better scheduling solutions if these jobs are considered before they actually arrive. However, to the best of our knowledge, we have not yet found corresponding research to date. This paper addresses a dynamic single machine scheduling problem with predicted arriving jobs (DSMSP/PRJs). We give a detailed description and the corresponding mathematical programming model with the objective of minimizing total weighted tardiness (TWT). With respect to the inclusion strategy of PRJs, which is one of the key technologies affecting the solution quality, we propose a job-based inclusion (JBI) procedure that focuses on the influence on the tardiness of PRJs rather than on a certain length of lookahead window. The novel JBI procedure shows obvious superior performance to the time-based method in the literature with respect to TWT. The discrete event simulation based on dispatching rules/heuristics (RHs) is adopted to address the DSMSP/PRJs. Six weighted dispatching rules and two improved heuristics based on the DT-TD (Decision Theory-Tactically Delayed) are proposed. Large-scale and systematic experiments show that the proposed RHs perform well. Especially, the DT-TD2 heuristic has overwhelming superior performance to the others. For the huge volumes of experimental raw data produced by 1350 simulation runs under 135 problem scenarios, we innovatively put forward the overall procedure and methods of data processing and analysis oriented to the different purposes. The systematic data analyses show that, under the various problem scenarios, considering PRJs in advance can significantly improve the scheduling solutions as long as appropriate RHs are used. The analysis results also reveal that, in the scenarios with lower shop utilization, lower percentage of PRJ, higher weight of PRJ, and looser due date of normal job (NJ), it is more obvious in the improvement of solutions, implying that it is more necessary to consider PRJs in advance.

Dynamic Interday and Intraday Scheduling

Adaptive Patient Flow Management Appointment scheduling has significant clinical, operational, and economical impact on healthcare systems. An informed scheduling strategy that can effectively match patient demand and service capacity dynamically is vital for the business of medical providers, quality of care, and patient satisfaction. By regulating patient flow via an appointment system, healthcare providers can mitigate arrival process variability and improve operational performance. The simultaneous consideration of appointment day (interday scheduling) and time of day (intraday scheduling) in dynamic scheduling decisions is an important theoretical and practical problem that has remained open because of its stochastic nature, complex structure, and large dimensionality. Zacharias et al. (2022) fill this critical gap in the literature. They introduce a novel dynamic programming framework, designed with the intention of bridging two independently established streams of literature, and to leverage their latest advances in tackling the joint problem. They advance the theory of the field to provide a rigorous and practically implantable solution.

Design of Digital Twin Sensing Strategies Via Predictive Modeling and Interpretable Machine Learning

Abstract This work develops a methodology for sensor placement and dynamic sensor scheduling decisions for digital twins. The digital twin data assimilation is posed as a classification problem, and predictive models are used to train optimal classification trees that represent the map from observed data to estimated digital twin states. In addition to providing a rapid digital twin updating capability, the resulting classification trees yield an interpretable mathematical representation that can be queried to inform sensor placement and sensor scheduling decisions. The proposed approach is demonstrated for a structural digital twin of a 12 ft wingspan unmanned aerial vehicle. Offline, training data are generated by simulating scenarios using predictive reduced-order models of the vehicle in a range of structural states. These training data can be further augmented using experimental or other historical data. In operation, the trained classifier is applied to observational data from the physical vehicle, enabling rapid adaptation of the digital twin in response to changes in structural health. Within this context, we study the performance of the optimal tree classifiers and demonstrate how they enable explainable structural assessments from sparse sensor measurements and also inform optimal sensor placement.

Energy optimal dispatching of ship's integrated power system based on deep reinforcement learning

With the pressure mounting on environmental protection, all-electric ships(AES)are becoming increasingly popular for maritime transportation. The optimal dispatch of energy is of great significance to achieving AES' safe and economic operation. This paper proposes a deep reinforcement learning (DRL) based energy optimization scheduling method for the ship power system, and the generator and the energy storage system (ESS) are directly driven by the original measurement data of the ship's power system. This paper first describes optimal energy scheduling in the ship power system mathematically and then expresses the scheduling decision problem as a reinforcement-learning framework. Next, it introduces a deep Q network algorithm to optimize the end-to-end control strategy between the measurement data of the ship power system and the action instructions of the generator and ESS. The method proposed in this paper does not need to model the complex system and can realize the dynamic optimization scheduling decision of energy with the goal of economy. Finally, two case studies are analyzed based on the historical data of the ship power system, and the simulation verifies the effectiveness and superiority of the proposed energy optimization scheduling method based on DRL.

An adaptive ensemble deep forest based dynamic scheduling strategy for low carbon flexible job shop under recessive disturbance

Recessive disturbance can gradually lead to machine idling and production status deviation. Its instant influence on system performance is often insignificant. Still, it can be accumulated over time, consequently causing considerable unnecessary carbon emission and flexible system performance degradation, which brings many difficulties to production managers to make a timely and effective response. To cope with this problem, this paper proposes an adaptive hybrid dynamic scheduling strategy for low carbon flexible job shops, which helps production managers understand the production status of the flexible system and decide the optimal strategy to re-optimise the schedule. This strategy consists of two parts: decision feature and decision approach. For one, concerning performance, phase, and adaption capability (PPC), a decision feature is devised to quantify the dynamic production status. For the other, an ensemble deep forest-based dynamic scheduling decision approach is presented to adaptively select the optimal strategy from four typical dynamic scheduling strategies to accommodate schedules to recessive disturbances. The experiments are conducted to verify the effectiveness of the proposed strategy, and the results reveal the proposed strategy delivers excellent performances both in decision accuracy and schedule repairing.

A Co-Scheduling Framework for DNN Models on Mobile and Edge Devices with Heterogeneous Hardware

With the emergence of more and more powerful chipsets and hardware and the rise of Artificial Intelligence of Things (AIoT), there is a growing trend for bringing Deep Neural Network (DNN) models to empower mobile and edge devices with intelligence such that they can support attractive AI applications in a real-time manner. To leverage heterogeneous computational resources (such as CPU, GPU, DSP, etc.) to effectively and efficiently support the concurrent inference of multiple DNN models on a mobile or edge device, we propose a novel online Co-Scheduling framework based on deep REinforcement Learning, called COSREL. COSREL has the following desirable features: 1) it achieves significant speedup over commonly-used methods by efficiently utilizing all the computational resources on heterogeneous hardware; 2) it leverages emerging Deep Reinforcement Learning (DRL) to make dynamic and wise online scheduling decisions based on system runtime state; 3) it is capable of making a good tradeoff among inference latency, throughput, and energy efficiency; and 4) it makes no changes to given DNN models, thus preserves their accuracies. To evaluate COSREL, we conduct extensive experiments on an off-the-shelf Android smartphone. The experimental results show that COSREL consistently outperforms other baselines in terms of throughput, latency, and energy efficiency.

Dynamic Inter-day and Intra-day Scheduling

The simultaneous consideration of appointment day (inter-day scheduling) and time of day (intra-day scheduling) in dynamic scheduling decisions is an important theoretical and practical problem that has remained open due to its stochastic nature, complex structure, and large dimensionality. This article provides the first analytical model and associated theoretical results to address this joint problem within a tractable optimization framework. We build upon two recent studies: one from dynamic inter-day scheduling (Truong 2015), and one from static intra-day scheduling (Zacharias and Yunes 2020). We prove connections between these two studies based on new theoretical results in discrete convex analysis regarding constrained multimodular function minimization. We leverage these novel results and dynamic programming tools to characterize an optimal policy. We derive theoretical upper and lower bounds for the joint problem, based on which we develop a heuristic solution with a theoretically guaranteed optimality gap. Numerical experiments indicate that the optimality gap is less than 1% for practical instances of the problem.

Scheduling Queues with Customer Transfers

We consider a multiclass single-server system wherein customers of each class form their own queue and can either abandon the system or transfer to other queues while waiting. A system manager makes dynamic scheduling decisions to minimize system-wide waiting cost. We solve the stochastic control problem under both infinite-horizon discounted and average cost criteria. By studying a Brownian control problem that arises in the conventional heavy traffic regime, we identify (under each cost criterion) an asymptotically optimal policy that prioritizes across different classes according to a workload-dependent dynamic index rule. The proposed policies acknowledge the impact of customer transfers on the optimal allocation of service resources and generate rich managerial insights. One of the technical difficulties in the proof of the asymptotic optimality result is to identify a natural stability condition and formally show that the sequence of diffusion-scaled processes is uniformly stable under that condition.

RESEARCH ON DYNAMIC SCHEDULING DECISION ALGORITHM IN MIMIC DEFENSE

In this paper, a dynamic scheduling decision algorithm based on mimic defense is proposed. According to the output of the decision feedback executor, different executors are selected by the scheduler to realize the feedback security defense mechanism. Firstly, the scheduling algorithm is used to dynamically select the executor set in the execution pool to reduce overhead and improve security. And then the output of the execution set is determined by the improved majority decision algorithm based on the reliability factor. Finally, the simulation results show that the dynamic feedback scheduling algorithm can effectively improve the security of the system.

Dynamic Pricing of a Multiclass Make-to-Stock Queue

We consider a make-to-stock manufacturing system selling multiple products to price-sensitive customers. The system manager seeks to maximize the long-run average profit by making dynamic pricing, outsourcing, and scheduling decisions: First, she adjusts prices dynamically depending on the system state. Second, when the backlog of work is judged excessive, she may outsource (or reject) new orders thereby incurring outsourcing costs. Third, she decides dynamically on which product to prioritize in the manufacturing process, i.e., she makes dynamic scheduling decisions. This problem appears analytically intractable. Thus, we resort to an approximate analysis in the heavy-traffic regime and consider the resulting Brownian control problem. We solve this problem explicitly by exploiting the solution to a particular Riccati equation. The optimal solution to the Brownian control problem is a two-sided barrier policy with drift rate control: Outsourcing and idling processes are used to keep the workload process above the lower reflecting barrier and below the upper reflecting barrier, respectively. Between the two barriers, a state-dependent drift rate is used to control the workload process. By interpreting this solution in the context of the original model, we propose a joint dynamic pricing, outsourcing, and scheduling policy, and demonstrate its effectiveness through a simulation study.

Meeting deadlines for approximation processing in MapReduce environments

To provide timely results for big data analytics, it is crucial to satisfy deadline requirements for MapReduce jobs in today’s production environments. Much effort has been devoted to the problem of meeting deadlines, and typically there exist two kinds of solutions. The first is to allocate appropriate resources to complete the entire job before the specified time limit, where missed deadlines result because of tight deadline constraints or lack of resources; the second is to run a pre-constructed sample based on deadline constraints, which can satisfy the time requirement but fail to maximize the volumes of processed data. In this paper, we propose a deadline-oriented task scheduling approach, named ‘Dart’, to address the above problem. Given a specified deadline and restricted resources, Dart uses an iterative estimation method, which is based on both historical data and job running status to precisely estimate the real-time job completion time. Based on the estimated time, Dart uses an approach–revise algorithm to make dynamic scheduling decisions for meeting deadlines while maximizing the amount of processed data and mitigating stragglers. Dart also efficiently handles task failures and data skew, protecting its performance from being harmed. We have validated our approach using workloads from OpenCloud and Facebook on a cluster of 64 virtual machines. The results show that Dart can not only effectively meet the deadline but also process near-maximum volumes of data even with tight deadlines and limited resources.

Research on Simulation Analysis of Elimination Conflicts in Large Financial System

Data of financial system traffic randomness large, the data is not fixed to a single channel by multiple users is difficult to form a unified task scheduling rules. In this paper, we put forward a multi-user flexible financial system task scheduling algorithm based on decision distance. The algorithm adopted decision distance can get the data of dynamic relation matrix, and use dynamic feedback scheduling method to control the matrix, in order to ensure the rationality of the task allocation and reduced the probability of task conflict in the system. The dynamic feedback scheduling decisions distance can analyze data from the use of case decisions using the resource nodes, get the dynamic relationship between data matrix, the matrix is ​​analyzed to extract maximum data connection group with no maximum data connections correlation data, which will be filtered with a smaller maximum data associated with the connection data.

Scheduling Simulations: An Experimental Approach to Time-Sharing Multiprocessor Scheduling Schemes

Real time systems that are logically programmed for scientific applications involve frequent job arrivals, thus requires a parallel architecture, so that maximum applications can be executed simultaneously resulting in less waiting time and maximum resource utilization. This must be achieved by workload partitioning & characterization, directs towards the development of Multiprocessor machines, a way to achieve parallel effects. Today, multiprocessor systems cover H/W replications that may replicates complete central processing units asynchronously or multiple executional units synchronously controlled by a different/common clock respectively. This research deals with the multiprocessor scheduling implemented via simulated time sharing environment containing logically programmed virtual processors and batch lists, each batch having its associated arrival time along with number of jobs where each job contains parameters such as Batch_id, Job_id and CPU Burst_time(defined as no. of cycles required) etc. The idea behind this theory is to distribute a number of simultaneously occurring jobs to virtual processor list corresponding to a scheduling algorithm. Synchronous architectures involve SIMD based model with data parallel aspects of computations, whereas Control parallel asynchronous MIMD machines are the future trends leading towards Instruction level parallel processors involving VLIW (very large instruction word) and superscalar machines. General Terms Multiprocessor Scheduling Policies & Mechanisms, Time Sharing & Space Sharing policies, Static vs Dynamic scheduling Decisions.

Blind Fair Routing in Large-Scale Service Systems with Heterogeneous Customers and Servers

In a call center, arriving customers must be routed to available servers, and servers that have just become available must be scheduled to help waiting customers. These dynamic routing and scheduling decisions are very difficult, because customers have different needs and servers have different skill levels. A further complication is that it is preferable that these decisions are made blindly; that is, they depend only on the system state and not on system parameter information such as call arrival rates and service speeds. This is because this information is generally not known with certainty. Ideally, a dynamic control policy for making routing and scheduling decisions balances customer and server needs by keeping customer delays low but still fairly dividing the workload amongst the various servers. In this paper, we propose a blind dynamic control policy for parallel-server systems with multiple customer classes and server pools that is based on the number of customers waiting and the number of agents idling.We show that in the Halfin-Whitt many-server heavy-traffic limiting regime, our proposed blind policy performs extremely well when the objective is to minimize customer holding costs subject to “server fairness,” as defined by how the system idleness is divided among servers. To do this, we formulate an approximating diffusion control problem (DCP) and compare the performance of the nonblind DCP solution to a feasible policy for the DCP that is blind. We establish that the increase in the DCP objective function value is small over a wide range of parameter values. We then use simulation to validate that a small increase in the DCP objective function value is indicative of our proposed blind policy performing very well.

On scheduling a multiclass queue with abandonments under general delay costs

We consider a multiclass queueing system with abandonments and general delay costs. A system manager makes dynamic scheduling decisions to minimize long-run average delay and abandonment costs. We consider the three types of delay cost: (i) linear, (ii) convex, and (iii) convex---concave, where the last one corresponds to settings where customers may have a particular deadline in mind but once that deadline passes there is increasingly little difference in the added delay. The dynamic control problem for the queueing system is not tractable analytically. Therefore, we consider the system in the conventional heavy traffic regime and study the approximating Brownian control problem (BCP). We observe that the approximating BCP does not admit a pathwise solution due to abandonments. In particular, the celebrated cμ rule and its extension, the generalized cμ rule, which is asymptotically optimal under convex delay costs with no abandonments, are not optimal in this case. Consequently, we solve the associated Bellman equation, which yields a dynamic index policy (derived from the value function) as the optimal control for the approximating BCP. Interpreting that control in the context of the original queueing system, we propose practical policies for each of the three cases considered and demonstrate their effectiveness through a simulation study.

Parametric timing estimation with Newton–Gregory formulae

AbstractThis paper presents a novel method for parametric worst‐case execution time (WCET) estimation of loops. The method determines a parametric bound on the iteration space size of loops with both affine and non‐affine loop bounds in an efficient manner using a formulation based on Newton–Gregory interpolating polynomials. Parametric WCET formulae are used to support dynamic scheduling decisions at runtime, where the WCET of a scheduled task might not be known statically. To determine worst‐case execution time estimates of scientific and multimedia codes, which spent most of the execution time on executing loop iterations, efficient and accurate symbolic loop WCET estimation methods must be capable of analyzing loops with symbolic bounds, non‐rectangular loops, zero‐trip loops, loops with multiple critical paths, and loops with non‐unit strides. Copyright © 2006 John Wiley & Sons, Ltd.

Parametric Timing Analysis

Embedded systems often have real-time constraints. Traditional timing analysis statically determines the maximum execution time of a task or a program in a real-time system. These systems typically depend on the worst-case execution time of tasks in order to make static scheduling decisions so that tasks can meet their deadlines. Static determination of worst-case execution times imposes numerous restrictions on real-time programs, which include that the maximum number of iterations of each loop must be known statically. These restrictions can significantly limit the class of programs that would be suitable for a real-time embedded system. This paper describes work-in-progress that uses static timing analysis to aid in making dynamic scheduling decisions. For instance, different algorithms with varying levels of accuracy may be selected based on the algorithm's predicted worst-case execution time and the time allotted for the task. We represent the worst-case execution time of a function or a loop as a formula, where the unknown values affecting the execution time are parameterized. This parametric timing analysis produces formulas that can then be quickly evaluated at run-time so dynamic scheduling decisions can be made with little overhead. Benefits of this work include expanding the class of applications that can be used in a real-time system, improving the accuracy of dynamic scheduling decisions, and more effective utilization of system resources.