RGV dynamic scheduling optimization model based on greedy algorithm

In this paper classic static and dynamic scheduling strategy is analyzed first, and then communication network of schedule ability judgment basis is given. An improved dynamic EDF scheduling algorithm is proposed in order to improve the scheduling task of real-time. The scheduling strategy is to change task priority according to the transmission error over deadline task when applying dynamic EDF scheduling strategy. True Time tool is used to build CAN network control system simulation platform. Dynamic EDF scheduling algorithm and improved scheduling algorithm are simulated respectively. The effectiveness of improved scheduling algorithm is verified by the simulation Keywords-Network control system; Scheduling Algorithm; True Time toolbox result.

An important component of the cyber-defense mechanism is the adequate staffing levels of its cybersecurity analyst workforce and their optimal assignment to sensors for investigating the dynamic alert traffic. The ever-increasing cybersecurity threats faced by today’s digital systems require a strong cyber-defense mechanism that is both reactive in its response to mitigate the known risk and proactive in being prepared for handling the unknown risks. In order to be proactive for handling the unknown risks, the above workforce must be scheduled dynamically so the system is adaptive to meet the day-to-day stochastic demands on its workforce (both size and expertise mix). The stochastic demands on the workforce stem from the varying alert generation and their significance rate, which causes an uncertainty for the cybersecurity analyst scheduler that is attempting to schedule analysts for work and allocate sensors to analysts. Sensor data are analyzed by automatic processing systems, and alerts are generated. A portion of these alerts is categorized to be significant , which requires thorough examination by a cybersecurity analyst. Risk, in this article, is defined as the percentage of significant alerts that are not thoroughly analyzed by analysts. In order to minimize risk, it is imperative that the cyber-defense system accurately estimates the future significant alert generation rate and dynamically schedules its workforce to meet the stochastic workload demand to analyze them. The article presents a reinforcement learning-based stochastic dynamic programming optimization model that incorporates the above estimates of future alert rates and responds by dynamically scheduling cybersecurity analysts to minimize risk (i.e., maximize significant alert coverage by analysts) and maintain the risk under a pre-determined upper bound. The article tests the dynamic optimization model and compares the results to an integer programming model that optimizes the static staffing needs based on a daily-average alert generation rate with no estimation of future alert rates (static workforce model). Results indicate that over a finite planning horizon, the learning-based optimization model, through a dynamic (on-call) workforce in addition to the static workforce, (a) is capable of balancing risk between days and reducing overall risk better than the static model, (b) is scalable and capable of identifying the quantity and the right mix of analyst expertise in an organization, and (c) is able to determine their dynamic (on-call) schedule and their sensor-to-analyst allocation in order to maintain risk below a given upper bound. Several meta-principles are presented, which are derived from the optimization model, and they further serve as guiding principles for hiring and scheduling cybersecurity analysts. Days-off scheduling was performed to determine analyst weekly work schedules that met the cybersecurity system’s workforce constraints and requirements.

Body Area Networks (BANs), targeting everywhere anytime health monitoring, operate in license-exempt frequency bands. They require a cross layer design that jointly considers the requirements of (1) access delay to the medium and (2) packet delivery ratio (PDR). In this paper, we experimentally evaluate the PDR of a BAN under static and dynamic scheduling schemes. Investigations show that static multi-hop routing enhances PDR with the help of packet relaying, but at the cost of using more transmission slots in the shared medium, which may compromise access delay. To reduce this cost, we propose a dynamic scheduling approach that adapts to BAN topology changes resulting from body movements. Our extensive experimental results indicate that for BANs colloccated with other wireless systems, our dynamic multi-hop scheduling is mandatory for satisfaction of the PDR constraint.

In the formulation of single process fault free intelligent RGV dynamic scheduling strategy, a double objective optimization model is established to improve the efficiency of RGV and shorten the production time of single material. For the solution of the model, firstly, the shortest moving path of RGV is determined according to Greedy algorithm and Tabu search algorithm, and the loading and unloading sequence of RGV is determined according to the short job priority scheduling algorithm. For intelligent RGV dynamic scheduling problem with single process failure, The failure time is determined by the cumulative failure probability model of the system, According to the period of the fault point, after determining the fault CNC, a new dynamic scheduling model is obtained according to the no fault scheduling strategy. Finally, the model and the algorithm are verified by the data.

Research on Design Method of Dynamic Shop Floor Scheduling System Based on Human-computer Interaction

Aiming at the field of high-demand takeaway delivery, a dynamic scheduling strategy based on multi-rider collaborative delivery status is proposed, and the model implementation is obtained by constructing a collaborative dynamic scheduling optimization model and using the optimization TS algorithm to obtain a multi-rider scheduling scheme in a dynamic environment, so that the total delivery time is minimized. Finally, the experimental results show the effectiveness and feasibility of the model and algorithm implementation.

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods.

Most of resource-constrained scheduling problems are dynamic, where we do not know all the time variables of tasks in advance. Recently, solving such dynamic problems is a hot topic of research in manufacturing systems. In this paper we analyze the constraints in the dynamic scheduling problems and we introduce the concept of monotonic about constraints which appear in general dynamic constraint models. We prove the soundness of the dynamic constraint solving algorithm for solving the monotonic constraints in the intelligent schedule system. Finally, An example of its application to discrete resource-constrained scheduling problem (ship-loading problem) is given.

The IES dynamic time-scale scheduling strategy based on multiple load forecasting errors

Heterogeneous systems with multiple different compute devices have come into common use recently, and the heterogeneity of the compute device is mainly reflected in three aspects: hardware architecture, instruction set architecture, and processing capability. Heterogeneous CPU-accelerator systems have attracted increasing attention especially. To make full use of multiple CPUs and accelerators to execute data-parallel applications, programmers may need to manually map computation and data to all available compute devices, which is tedious, error-prone, and difficult. Especially for some data-parallel applications, the inter-device communication could easily become the performance bottleneck of multi-device co-execution. Therefore, firstly, a runtime system is designed for supporting heterogeneous cooperative execution (HCE) of data-parallel applications, which can help programmers to automatically and efficiently map computation and data to multiple compute devices. Secondly, an incremental data transfer method is designed to avoid redundant data transfers between devices, and a three-way overlapping communication optimization method based on software pipelining is designed to effectively hide the inter-device communication overhead. Based on our previously proposed feedback-based dynamic and elastic task scheduling (FDETS) scheme and asynchronous-based dynamic and elastic task scheduling (ADETS) scheme, the modified FDETS that supports incremental data transfer and the modified ADETS that supports three-way overlapping communication optimization are proposed, which not only can effectively partition and balance the workload among multiple compute devices but also can significantly reduce data transfer overhead between devices. Thirdly, a prototype of the proposed runtime system is implemented, which provides a set of runtime APIs for task scheduling, device management, memory management, and transfer optimization. Our experimental results show that the communication overhead between devices is greatly reduced using the proposed inter-device communication optimization methods and the multi-device co-execution significantly outperforms the best single-device execution.

With the rise of wireless power transfer technology, charging scheduling issue is prevalent in wireless rechargeable sensor networks (WRSNs). Most prior arts focused on two-dimensional (2-D) networks with homogeneous mobile chargers. However, three-dimensional (3-D) networks with collaborations among heterogeneous mobile chargers are more practical. In this paper, we consider 3-D networks in which wireless charging vehicles (WCVs) are employed with unmanned aerial vehicles (UAVs). To prolong network lifetime, we focus on device sleep time and energy usage and propose a 3-D Dynamic Collaborative Scheduling scheme (3DCS). Theoretical values of energy threshold and partition number are determined to assign charging tasks to chargers. Then, scheduling algorithms that include target selection, infeasibility test, and target update, are developed. In addition, a collaborative algorithm is developed to re-assign charging tasks from busy chargers toward their neighboring chargers to further improve charging efficiency. Test-bed experiments and extensive simulations reveal that, compared with several distinguished scheduling schemes, our scheme has a superior performance in charging throughput, energy efficiency, and other characteristics.

Co-design of dynamic scheduling and quantized control for networked control systems

D-STHARk: Evaluating Dynamic Scheduling of Tasks in Hybrid Simulated Architectures

Most of the earlier research in dynamic scheduling (DS) of diffusion furnace (DF), considers only future arrival of jobs. However, in reality along with the future arrival of jobs, various unexpected real time events (RTE) related to jobs, and/or resources will occur. Hence, this study addresses the important real life characteristics of both future arrival jobs and the occurrence of a RTE while scheduling DF, called as dynamic real time scheduling (DRTS), with the scheduling objective of minimising total weighted tardiness (TWT). This study first explains the mathematical model for DS of single DF to minimise TWT. Then, this study proposes 12 variants of meta-heuristics (six variants of simulated annealing and six variants of tabu search) by considering six different initial solutions obtained from six variants of greedy heuristic algorithm for DRTS of DF. From empirical and statistical analyses on 270 problem instances, this study observed that one of the variants of simulated annealing consistently performing better. Copyright © 2020 Inderscience Enterprises Ltd.

In the Software Defined Networking (SDN) based Data Center Networks (DCN), load balancing is one of the research hotspots. This paper proposes a Dynamic Traffic Scheduling (DTS) strategy for load balancing taking the advantages of the SDN central controller. Considering the realtime network status, the paths can be adjusted during the flow transmission. A flow equilibrium degree is defined as the trigger of enabling DTS. The implementation process of DTS are also described in detail. An OpenFlow based SDN simulation platform is constructed. The experimental results indicate that DTS strategy generate better performance compared with other routing strategy without dynamic rerouting mechanism under various load.