一类DEDS最优调度算法的改进

进一步研究一类 DEDS最优调度问题 ,用极大代数方法得到了目标函数所有参数的简化公式 ,从而完善并改进了最优调度算法。该结果可用于含存储器的串行生产线。

基于任务复制的调度算法

任务调度是并行分布式计算系统中最具挑战性的NP完全问题之一。基于任务复制的调度是一种有效的调度方法。在通信开销较小的情况下,现已有许多算法能产生最优调度。但其最优条件要么比较苛刻,要么比较复杂。因此,针对这些算法存在的问题,提出一个新的基于任务复制的聚集调度(TDCS)算法,不仅其最优条件简单、宽松,而且该算法具有更小的时间复杂度O(log),其中,和分别表示任务集中任务的个数和最大入度。

调度Fork-Join任务图的贪心算法

任务调度算法的目标是把组成并行程序的一组任务分配到多个处理器以使得程序的完成时间最短,这是一个NP完全问题。虽然许多算法在任务满足某些条件时能产生最优调度,但大多都忽略了节省处理器个数和最小化程序总的完成时间等问题。Fork-Join结构是一种并行处理的基本结构。因此,专门针对Fork-Join任务图,提出了一个能产生最优调度的新的贪心调度算法,该算法具有高的加速比和总体效率,时间复杂度为2,其中,表示任务集中任务的个数。实验结果表明,相比其它算法,该算法具有较短的调度长度、较短的完成时间,使用的处理器数较少。

并行分布计算中的任务调度问题(一)

<正>1引言 任务调度是并行分布计算中最具有挑战性的问题之一,其1般形式和几种受限形式是NP完全问题~[1][2]。要取得多项式时间复杂度的最优调度算法。

GLOBAL CONVERGENCE OF A CLASS OF OPTIMALLY CONDITIONED SSVM METHODS

This paper explores the convergence of a class of optimally conditioned self scaling variable metric (OCSSVM) methods for unconstrained optimization. We show that this class of methods with Wolfe line search are globally convergent for general convex functions.

A hybrid differential evolution algorithm for meta-task scheduling in grids

Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the grid.This paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or near-optimal schedule within reasonable time.The encoding scheme and the adaptation ofclassical differential evolution algorithm for dealing with discrete variables are discussed.A simple but ef-fective local search is incorporated into differential evolution to stress exploitation.The performance of theproposed HDE algorithm is showed by being compared with a genetic algorithm(GA)on a known staticbenchmark for the problem.Experimental results indicate that the proposed algorithm has better perfor-mance than GA in terms of both solution quality and computational time,and thus it can be used to de-sign efficient dynamic schedulers in batch mode for real grid systems.

Multi-objective optimization for draft scheduling of hot strip mill

A multi-objective optimization model for draft scheduling of hot strip mill was presented, rolling power minimizing, rolling force ratio distribution and good strip shape as the objective functions. A multi-objective differential evolution algorithm based on decomposition （MODE/D）. The two-objective and three-objective optimization experiments were performed respectively to demonstrate the optimal solutions of trade-off. The simulation results show that MODE/D can obtain a good Pareto-optimal front, which suggests a series of alternative solutions to draft scheduling. The extreme Pareto solutions are found feasible and the centres of the Pareto fronts give a good compromise. The conflict exists between each two ones of three objectives. The final optimal solution is selected from the Pareto-optimal front by the importance of objectives, and it can achieve a better performance in all objective dimensions than the empirical solutions. Finally, the practical application cases confirm the feasibility of the multi-objective approach, and the optimal solutions can gain a better rolling stability than the empirical solutions, and strip flatness decreases from （0± 63） IU to （0±45） IU in industrial production.

An Optimal Algorithm for a Class of Parallel Machines Scheduling Problem

This paper considers the parallel machines scheduling problem where jobs are subject to different release times. A constructive heuristic is first proposed to solve the problem in a modest amount of computer time. In general, the quality of the solutions provided by heuristics degrades with the increase of the probiem’s scale. Combined the global search ability of genetic algorithm, this paper proposed a hybrid heuristic to improve the quality of solutions further. The computational results show that the hybrid heuristic combines the advantages of heuristic and genetic algorithm effectively and can provide very good solutions to some large problems in a reasonable amount of computer time.

Batching Scheduling in a Two-Level Supply Chain with Earliness and Tardiness Penalties

This paper studies the batch sizing scheduling problem with earliness and tardiness penalties which is closely related to a two-level supply chain problem.In the problem,there are K customer orders,where each customer order consisting of some unit length jobs has a due date.The jobs are processed in a common machine and then delivered to their customers in batches,where the size of each batch has upper and lower bounds and each batch may incur a fixed setup cost which can also be considered a fixed delivery cost.The goal is to find a schedule which minimizes the sum of the earliness and tardiness costs and the setup costs incurred by creating a new batch.The authors first present some structural properties of the optimal schedules for single-order problem with an additional assumption(a):The jobs are consecutively processed from time zero.Based on these properties,the authors give a polynomial-time algorithm for single-order problem with Assumption(a).Then the authors give dynamic programming algorithms for some special cases of multiple-order problem with Assumption(a).At last,the authors present some structural properties of the optimal schedules for single-order problem without Assumption(a) and give a polynomial-time algorithm for it.