This paper introduces a novel design scheme which utilizes a programmable logic controller (PLC) network-based, computer distributed control system for the automation of a practical manufacturing process. Part of this...
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This paper introduces a novel design scheme which utilizes a programmable logic controller (PLC) network-based, computer distributed control system for the automation of a practical manufacturing process. Part of this system is modelled as a multi-product batching processing machine(BPM) which can process a batch of jobs simultaneously with a known and fixed number of jobs and their ready time. The optimalscheduling objective is that the different jobs are dispatched and sequenced in order to minimize the makespan and maximize the utilization of the BPM, it leads to a NP-hard combinatorial optimization problem. We apply genetic algorithms to solve this scheduling problem, and an optimal solution is obtained for a practical case study. The genetic algorithms approach demons (rates effectiveness, efficiency and robustness. The paper thus shows that computer distributed control plus evolutionary schedulingalgorithms make the manufacturing system more 'intelligent' and 'flexible'.
With the emergence of multicore processors, the research on multiprocessor real-time scheduling has caught more researchers' attention recently. Although the topic has been studied for decades, it is still an evol...
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With the emergence of multicore processors, the research on multiprocessor real-time scheduling has caught more researchers' attention recently. Although the topic has been studied for decades, it is still an evolving research field with many open problems. In this work, focusing on periodic real-time tasks with quantum-based computation requirements and implicit deadlines, we propose a novel optimalscheduling algorithm, namely boundary fair (Bfair), which can achieve full system utilization as the well-known Pfair schedulingalgorithms. However, different from Pfair algorithms that make scheduling decisions and enforce proportional progress (i.e., fairness) for all tasks at each and every time unit, Bfair makes scheduling decisions and enforces fairness to tasks only at tasks' period boundaries (i.e., deadlines of periodic tasks). The correctness of the Bfair algorithm to meet the deadlines of all tasks' instances is formally proved and its performance is evaluated through extensive simulations. The results show that, compared to that of Pfair algorithms, Bfair can significantly reduce the number of scheduling points (by up to 94%) and the overhead of Bfair at each scheduling point is comparable to that of the most efficient Plait algorithm (i.e., PD2). Moreover, by aggregating the time allocation of tasks for the time interval between consecutive period boundaries, the resulting Bfair schedule can dramatically reduce the number of required context switches and task migrations (as much as 82% and 85%, respectively) when compared to those of Pfair schedules, which in turn reduces the run-time overhead of the system. (C) 2011 Elsevier Inc. All rights reserved.
Task scheduling is one of the key elements in any distributed-memory machine (DMM), and an efficient algorithm can help reduce the interprocessor communication time. As optimalscheduling of tasks to DMMs is a strong ...
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Task scheduling is one of the key elements in any distributed-memory machine (DMM), and an efficient algorithm can help reduce the interprocessor communication time. As optimalscheduling of tasks to DMMs is a strong NP-hard problem, many heuristic algorithms have been introduced in the literature. This paper presents a Task Duplication based scheduling (TDS) algorithm which can schedule directed acyclic graphs (DAGs) with a complexity of O(IVI2), where IVI is the number of tasks in the DAG. This algorithm generates an optimal schedule for a class of DAGs which satisfy a simple cost relationship. The performance of the algorithm has been observed by its application to some practical DAGs, and by comparing it with other existing scheduling schemes in terms of the schedule length and algorithm complexity.
optimal online schedulingalgorithms are known for sporadic task systems scheduled upon a single processor. Additionally, optimal online schedulingalgorithms are also known for restricted subclasses of sporadic task ...
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optimal online schedulingalgorithms are known for sporadic task systems scheduled upon a single processor. Additionally, optimal online schedulingalgorithms are also known for restricted subclasses of sporadic task systems upon an identical multiprocessor platform. The research reported in this article addresses the question of existence of optimal online multiprocessor schedulingalgorithms for general sporadic task systems. Our main result is a proof of the impossibility of optimal online scheduling for sporadic task systems upon a system comprised of two or more processors. The result is shown by finding a sporadic task system that is feasible on a multiprocessor platform that cannot be correctly scheduled by any possible online, deterministic scheduling algorithm. Since the sporadic task model is a subclass of many more general real-time task models, the nonexistence of optimal scheduling algorithms for the sporadic task systems implies nonexistence for any model which generalizes the sporadic task model.
scheduling periodic real-time tasks upon uniform multiprocessors is studied in this paper. We propose an optimalscheduling algorithm on uniform multiprocessors, named A-S algorithm, which achieves on-line scheduling ...
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ISBN:
(纸本)9781509062188
scheduling periodic real-time tasks upon uniform multiprocessors is studied in this paper. We propose an optimalscheduling algorithm on uniform multiprocessors, named A-S algorithm, which achieves on-line scheduling as PCG[5] algorithm. PCG assigns task with largest local remaining time to fastest idle processor when rescheduling, which incurs great context switches between consecutive rescheduling. A-S keeps as many processors' assignments unchanged as possible at rescheduling points through a greed algorithm. The resulting A-S schedule can reduce preemptions and migrations dramatically (90.8% and 87.5% at most, respectively) when compared to those of PCG schedules.
With the increasing scale and complexity of the power system, the traditional large power grid can no longer meet the requirements for power quality and reliability. In order to solve these problems, distributed power...
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Sequencing to minimize mean finishing time (or mean time in system) is not only desirable to the user, but it also tends to minimize at each point in time the storage required to hold incomplete tasks. In this paper a...
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Sequencing to minimize mean finishing time (or mean time in system) is not only desirable to the user, but it also tends to minimize at each point in time the storage required to hold incomplete tasks. In this paper a deterministic model of independent tasks is introduced and new results are derived which extend and generalize the algorithms known for minimizing mean finishing time. In addition to presenting and analyzing new algorithms it is shown that the most general mean-finishing-time problem for independent tasks is polynomial complete, and hence unlikely to admit of a non-enumerative solution.
We consider the complexity of determining whether a set of periodic, real-time tasks can be scheduled on m ⩾ 1 identical processors with respect to fixed-priority scheduling. It is shown that the problem is NP-hard in...
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We consider the complexity of determining whether a set of periodic, real-time tasks can be scheduled on m ⩾ 1 identical processors with respect to fixed-priority scheduling. It is shown that the problem is NP-hard in all but one special case. The complexity of optimal fixed-priority scheduling algorithm is also discussed.
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