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In view of the machine failure in the manufacturing process of the plate custom furniture mixed flow workshop, in this paper, a comparative analysis of three typical machine failure scenarios was made, and three rescheduling schemes were generated. On this basis, a dynamic scheduling model considering fault recoverability was constructed with the minimum completion time as the goal, and a method combining empire competition algorithm and complete rescheduling strategy was adopted to solve the problem. The results showed that the model and method proposed in this paper can effectively reduce the impact of machine faults on production, and the scheduling efficiency of the three fault scenarios can be increased by 8%, 16.3% and 14.5% respectively, so as to improve the efficiency of shop scheduling and shorten the completion time of sheet parts. This study could provide guidance for optimizing the production scheduling problem of plate customization furniture enterprises under machine fault. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the characteristics of high-speed mobility and limited resources of the vehicle itself, the vehicle networks scenarios using traditional communication and computing methods cannot meet the increasing data computing needs and the quality of experience(QoE) required by users. New vehicle networks scenarios built with 5 G and mobile edge computing(MEC) technologies can meet the above requirements, but due to changes in network structure, it is necessary to design vehicle task communication and computing strategies suitable for the new scenarios. This paper studies the dynamic offloading of mobile vehicle tasks in the 5 G vehicle networks scenario, and proposes a corresponding dynamic task allocation strategy and a low-latency algorithm for offloading scheduling. The vehicle will offload the unfinished computing tasks to the corresponding MEC server or vehicle according to the proposed strategy and algorithm, And the calculation result will be directly returned to the vehicle owner through the joint communication between the edge servers or directly from the nearby idle vehicle selected to accept the offloading task. Simulation results show that the proposed strategy and algorithm have good performance in optimizing offload latency and improve the quality of user experience. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intelligent manufacturing is an inevitable trend in the development of our country' s manufacturing industry, and intelligent shop scheduling is a key technology for the integration of manufacturing upgrades and deepening. This paper mainly studied the application of reinforcement learning algorithms in shop scheduling problems, which layed the foundation for sub sequent research. Shop scheduling mainly included static scheduling and dynamic scheduling, reinforcement learning algorithms mainly included value-based functions and Actor-Critic (AC) networks. First of all, this article described the research status of reinforcement learning methods on the two major issues of Job-Shop scheduling and Flow-Shop scheduling in general. Secondly, it classified the establishment rules of mathematical model of the shop scheduling problem and the most critical Mark ov model in reinforcement learning algorithms. Finally, according to the research status and the current needs of industrial digital trans- formation, it prospected the future research direction of intelligent workshop scheduling technology . [ABSTRACT FROM AUTHOR]

Copyright of Journal of Liaoning Technical University (Natural Science Edition) / Liaoning Gongcheng Jishu Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of Liaoning Technical University (Natural Science) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming al the problem of high energy consumption in data centers, this paper proposed a virtual machine scheduling method based on load prediction, which used double exponential smoothing approach to predict the load of the host, used hybrid strategy of MMT and MM to choose the virtual machine which should be moved, and used the best resources fit(BRF) strategy to select the physical host. The prediction model of the proposed scheduling method could improve the accuracy of triggered migration. With the increase of the number of scheduling rounds, virtual machines, which had complementary demand for resources, would be integrated into the same physical host and reduced the limes of migration thereby. Finally, this paper compared the method with FT_MMT, CDLC and AR_MMT strategy on the CloudSim simulation platform. The results show that the scheduling method has improvement in energy saving and migration limes reduction. [ABSTRACT FROM AUTHOR]

Objectives: With the rapid development of the Chinese economy, logistics distribution has put forward higher requirements on the real‑time performance of vehicle scheduling and the ability to deal with emergencies. As traditional vehicle scheduling algorithms usually consider static scheduling tasks, it is difficult to deal with emergencies such as vehicle failure or adding new tasks in real‑time. A local search of the dynamic scheduling method is proposed based on the ruin and recreate algorithm.Methods: When some vehicle breaks down or a new task is added, the location of the added task node or broken vehicle is obtained and all the nodes are found within a certain range around the location. Based on the initial solution, all the unfinished nodes are searched that are outside the range and on the same path with the nodes mentioned above. The two sets of nodes compose a collection of removal task nodes, which act as the input of the ruin and recreate algorithm to generate new vehicle routes.Results: To demonstrate the performance of the pro‑ posed method,three experiments are designed and conducted: (1)The data in the Solomon benchmark examples are used in the static scheduling experiment, which is divided into 9 C‑type (clustered customers) and 8 R‑type (uniformly distributed customers) datasets. The multi‑objective hybrid ant colony optimization algorithm is employed as the reference method. Results on the Solomon benchmark datasets show that ruin and recreate algorithm outperforms the reference method with respect to distance cost and the number of vehicles in 14 of 17 datasets. (2) The second experiment is a dynamic scheduling test, and it uses Lackner's benchmark datasets that can be used to demonstrate five different dynamic degrees of 10%, 30%, 50%, 70%, and 90%. The test data contains 240 sub‑datasets and is divided into four groups: C1, C2, R1, and R2. In this experiment, the ruin and recreate algorithm is employed as the reference method. Results show that the local search algorithm proposed in this paper outperforms the ruin and recreate algorithm in terms of vehicle usage and driving distance on C2 and R1 datasets, but the ruin and recreate algorithm performs better than the proposed method on C1 data set. For the R2 dataset, the proposed method reduces the number of vehicles but increases the driving distance. (3) The third experiment is conducted on a real scheduling scenario dataset that contains 35 vehicles and 307 task nodes. Ruin and recreate algorithm is also employed as the reference method in this experiment.In the case of vehicle breakdowns, the proposed method changes 37.5% of vehicle routes while the reference method changes all the routes, and the performance of the pro‑ posed method is much better than that of the ruin and recreate algorithm with respect to program running time and driving distance cost. In the case of adding new tasks, the proposed method adjusts only 8.3% vehicle routes while the reference method changes 87.5% of routes. Conclusions: The proposed method per‑ forms much better than the reference method in terms of running time,but the proposed method costs an additional 0.5% driving distance than the reference method. From the above three experiments, it can be concluded that the ruin and recreate algorithm has advantages in terms of vehicle usages and driving distance for the static scheduling tasks, and for the dynamic scheduling scenario that has a lot of distribution task nodes, the proposed method can solve small‑scale dynamic vehicle routing problem within a very short time and with minor route adjustments. [ABSTRACT FROM AUTHOR]

In the production process of aerospace structural parts, there coexist batch production tasks and research and development (R&D) tasks. Personalized small-batch R&D and production tasks lead to frequent emergency insertion orders. In order to ensure that the task is completed on schedule and to solve the flexible job shop dynamic scheduling problem, this paper takes minimization of equipment average load and total completion time as optimization goals, and proposes a dual-loop deep Q network (DL-DQN) method driven by a perception-cognition dual system. Based on the knowledge graph, the perception system realizes the representation of workshop knowledge and the generation of multi-dimensional information matrix. The cognitive system abstracts the scheduling process into two stages: resource allocation agent and process sequencing agent, corresponding to two optimization goals respectively. The workshop status matrix is designed to describe the problems and constraints. In scheduling decision, action instructions are introduced step by step. Finally, the reward function is designed to realize the evaluation of resource allocation decision and process sequence decision. Application of the proposed method in the aerospace shell processing of an aerospace institute and comparative analysis of different algorithms verify the superiority of the proposed method.

Since the plant simulation method based on three-dimensional geometric grid model consumes a lot of memory and the plant simulation method based on two-dimensional plane model has a blur defect when it is near viewpoint in large scale plant simulation, this paper proposed a large scale plant simulation method based on three layers of plant model. This method divided the large scale plants into three layers; a geometric mesh model layer with high accuracy, an impostor model layer with moderate accuracy and a billboard model with lower accuracy. According to the distance between plant and viewpoint, the method automatically loaded the model in the corresponding layer. When the plant was closer to the viewpoint, it loaded the model in the model layer of the mesh model. When plant was far away from the viewpoint, it loaded the model in the model layer of the impostor model. When the plant was very far away from the viewpoint, it loaded the model in the model layer of the billboard model. Finally, it compared the rendering effects of the three kinds of plant simulation methods by using Ogre graphics engine. The experimental results show that the method can obtain the same realistic feeling as the plant simulation method based on three-dimensional geometric grid model, and the rendering speed is similar to that of the plant simulation method based on two-dimensional plane model in low memory consumption. [ABSTRACT FROM AUTHOR]

Battlefield resource scheduling problem is a research hotspot in the operational field. This paper described the problem, analyzed the dynamic resource scheduling requirement and built a dynamic resource scheduling model that contained interval parameters. To solve this model, three greedy strategies which were called basic greedy strategy, dual greedy strategy and consistent greedy strategy were designed and an integrated greedy algorithm was put forward. Finally, the proposed method was tested with a case of joint campaign and the results show that the method is well-performed in the uncertain battlefield environment. [ABSTRACT FROM AUTHOR]

In recent years, a novel application domain of scheduling, namely carrier-based aircraft deck operation scheduling is emerged. As an unexplored domain of scheduling, carrier-based aircraft deck operation scheduling has attracted more and more attention from both academic and industrial experts in the filed of scheduling, control theory and operations research et al. In this paper, a survey of carrier-based aircraft deck operation scheduling problem is presented both at home and abroad. Firstly, the carrier-based aircraft deck operation scheduling problem is formulated as a fundamental mixed integer programming mathematical model; secondly, a comprehensive review at abroad is given, which mainly focuses on United States; thirdly, four related topics of carrier-based aircraft deck operation scheduling at home are classified, including system simulation, system design, system optimization, and route planning. Finally, we conclude this survey with future trends and further research directions. This study aims to promote theoretical research and engineering applications of carrier-based aircraft deck operation scheduling problem at home. [ABSTRACT FROM AUTHOR]

CPU-GPU heterogeneous multi-core system has been widely applied because of its acceleration effects for computeintensive applications. However, the problem of workload imbalance is serious. Therefore, this paper proposed a dynamic task scheduling algorithm (DTSA) based on CPU-GPU heterogeneous multi-core system. In order to guarantee that all cores were doing useful work, it made full use of CPU and GPU. Furthermore, it could accurately measure the computational power of GPUs and CPUs respectively, dynamically adjusted the size of data blocks to be executed on CPUs and GPUs, and finally reduced the total executing time of workloads and increased the system speedup. According to the results of experiments by using this algorithm, the system speedup increases by 34% ~ 103%. [ABSTRACT FROM AUTHOR]

In order to satisfy the deadline for MapReduce jobs, this paper presented a dynamic scheduling algorithm based on deadline(DCDS).This algorithm could monitor the real-time implementation procedure of jobs, and estimated the time of jobs dynamically to compute the priority of the job. For emergency jobs, this algorithm used preemptive strategy to ensure jobs completed under the deadline. Experimental results show that the scheduling algorithm not only meets the deadline of the job, but also improves the efficiency and throughput of the system resources compared with the existing Hadoop scheduling algorithm. [ABSTRACT FROM AUTHOR]

Focused on dynamic scheduling of single-processor real-time system, this paper defined and analyzed the sensitivity and influence of task attributes including arrival time, execution time, deadline and laxity, then it proposed a scheduling algorithm PTBM based on priority table, which made that a task with small deadline and large laxity had higher priority. Compared with PTBM with EDF, LLF and PTD, simulated results verify CMRN of the sensitivity and influence, and show PTBM outperforming on scheduling success ratio. The proposed algorithm can be applied to dynamically schedule real-time tasks in realtime systems. [ABSTRACT FROM AUTHOR]

This paper focused on relief materials dynamic scheduling after large emergency. We quantified the loss of victims in terms of unmet needs, and formulated a mixed integer programming model in order to minimize victims' loss and vehicle scheduling expense. The main decisions were determining the delivery routes and allocating the relief supplies from relief distribution center (RDC) to save points without sufficient vehicle quantity. We used the hierarchical thinking to reduce solution space, improved the coding method and designed the genetic algorithm for this problem. Finally, experimental results show the dynamic scheduling scheme under different vehicle quantity, and determine the optimal vehicle quantity in RDC through comparative analysis, the validity of this model and algorithm is verified combining with realistic distribution scheme. [ABSTRACT FROM AUTHOR]