离散和声求解带启动时间批量流水线调度问题

分别在有等待和无等待的情况下,深入分析了带有启动时间的批量调度问题,以最小化最大完成时间为目标,提出了两种离散和声搜索算法。针对算法本质连续而问题离散的矛盾,对和声搜索算法进行改进。首先提出了基于工序的编码方式,采用inver-over和重组两种离散算子产生候选解的进化机制;并利用改进的NEH(NawazEnscore-Ham)方法进行初始化,产生的高质量和多样化的初始种群有效地指导了算法的进化方向,提高收敛速度;最后将一种简单而有效的局部邻域搜索方法嵌入到和声搜索算法中以增强其局部搜索能力。仿真实验和比较结果表明了所提算法的有效性。     

离散和声求解带启动时间批量流水线调度问题

分别在有等待和无等待的情况下,深入分析了带有启动时间的批量调度问题,以最小化最大完成时间为目标,提出了两种离散和声搜索算法。针对算法本质连续而问题离散的矛盾,对和声搜索算法进行改进。首先提出了基于工序的编码方式,采用inver-over和重组两种离散算子产生候选解的进化机制;并利用改进的NEH(Nawaz-Enscore-Ham)方法进行初始化,产生的高质量和多样化的初始种群有效地指导了算法的进化方向,提高收敛速度;最后将一种简单而有效的局部邻域搜索方法嵌入到和声搜索算法中以增强其局部搜索能力。仿真实验和比较结果表明了所提算法的有效性。     

求解考虑机器调整时间的并行机分批优化调度问题

基于目前车间调度问题是以单个或整批进行生产加工的并行机调度模型已不再符合实际工况下的车间生产。提出以最小化最大完工时间为优化目标,对遗传差分进化混合算法,灰狼差分进化混合算法进行了比较。为提高加工工件进行分批及分批之后子批的分配与排序效率,该问题是对不同规模的经典并行机调度问题进行求解并展示两种算法的求解,证明了灰狼差分进化混合算法在寻优性能上优于遗传差分进化混合算法,不仅具有更好的解的稳定性,而且具有更高的寻优精度。     

改进布谷鸟搜索算法求解批量流水线调度问题

针对以最大完工时间为目标的批量流水线调度问题,提出一种改进的布谷鸟搜索算法．该算法采用排序规则的编码方式,将连续个体值的布谷鸟搜索算法直接应用于离散的调度问题．其次,在布谷鸟搜索算法的基础上,一个简单而有效的局部搜索用于批量流水线调度问题的探索．仿真实验表明所提出算法的可行性和有效性．     

求解批量流水线调度问题的和声算法

针对以最大完工时间和总流经时间为目标的批量流水线调度问题,提出了改进的和声调度算法。该算法采用基于最大位置值(LPV)规则的编码方式,使具有连续性质的和声算法应用于求解调度问题;提出新的初始化方法,应用了多种群进化的思想更新和声库,并结合和声算法和模拟退火算法各自的特点,给出了两种混合调度算法。仿真实验表明所提算法的可行性和有效性。     

极小化最大完工时间的单机连续型批调度问题

从钢铁工业中加热炉对管坯的加热过程,提出一种新的连续型批处理机调度问题,与传统批处理机调度问题的批进批出方式不同,其主要特征为批中工件的进入、处理和离开都连续进行,批B_i的处理时间与该批的大小|B_i|、批中工件T_j的处理时间p_j及机器的容量C都有关,表示为p^{(i)}=\dmax_{T_j\in B_i}\{p_j\}(1+\displaystyle\frac{|B_i|-1}{C}).对于极小化最大完工时间问题,给出了一个复杂性为O(n^2)的动态规划算法,并证明了这个算法的最优性.     

特殊工艺约束下最小完工时间调度问题的新并行遗传算法

针对在特殊工艺约束下非等同并行机最小完工时间调度问题,设计了一个基于向量组编码的新的遗传算法。此算法的编码方法简单,能有效地反映实际调度方案,并能保证交叉和变异后染色体满足约束条件,收敛速度快。同时为更好地适应调度实时性和解决大型企业此类问题的需要,在基于遗传算法自然并行性特点的基础上,实现了主从式控制网络模式下并行遗传算法。仿真结果表明,此算法是有效的,优于普通的遗传算法,具有较高的并行性。     

云计算中最小化任务完工时间的多资源调度算法

云计算中Hadoop平台上默认调度方式FIFO是以公平性为目标,然而考虑单一因素会使资源利用率低下以及任务完成时间过长。在公平性和完成时间的权衡中,运行时间指标更为重要。据此,建立云计算下多资源和应用程序任务以及调度的数学模型和其目标函数,运用归约方法和具有强大计算能力的工具MINI SAT SOLVER去求解问题。仿真实验结果表明,在不同的资源供给条件下,基于MINI SAT SOLVER的次优算法比YARN(Yet Another Resource Negotiator)中默认的调度算法FIFO缩短了任务的完工时间,优化比率最高可以达到30%。     

多机器总完成时间和makespan近似最优的开放式车间调度方法

研究了多机器开放式车间调度问题,采用离散事件系统调度使makespan最小化和总完成时间最小.给出了在确定处理机器的条件下,不同批次的作业总完成时间最优的排序定理,以及选择机器处理作业的指标优化定理,利用给出的若干定理建立了总完成时间最优的调度方法.作者利用加权总完成时间最优算法来近似求解makespan最小化和总完成时间最优的调度问题.作者也利用论文的理论结果给出了一个三机器开放式车间情况的实际算例.     

最小化多MapReduce任务总完工时间的分析模型及其应用

随着大规模的MapReduce集群广泛地用于大数据处理,特别是当有多个任务需要使用同一个Hadoop集群时,一个关键问题是如何最大限度地减少集群的工作时间,提高MapReduce作业的服务效率。可将多个MapReduce作业当做一个调度任务建模,观察发现多个任务的总完工时间和任务的执行顺序有密切关系。 研究目标是设计作业调度系统分析模型,最小化一批MapReduce作业的总完工时间。提出一个更好的调度策略和实现方法, 使整个调度系统符合经典Johnson算法的条件, 从而可使用经典Johnson算法在线性时间内获取总完工时间的最优解。同时,针对需要使用两个或多个资源池进行平衡的问题, 提出了一种线性时间解决方案, 优于已知的近似模拟方案。该理论模型可应用于提高系统响应速度、节能和负载均衡等方面, 对应的应用实例提供了证实。     

最小化完成时间和加惩罚值和的批调度问题

考虑如下单机并行批调度问题:给定一些工件,每个工件有给定的处理时间以及惩罚值(可以拒绝处理某些工件,惩罚值为拒绝处理工件所付出的代价).给定一个可同时处理多个工件的批处理器.同时处理的工件形成一个批.同一批处理的工件具有相同的开始时间和结束时间,即开始时间加上这一批中所有工件的最大给定处理时间.判断如何选择要处理的工件,给这些工件分批以及给批排序使得目标函数值最小.对目标函数是被处理工件的完成时间之和加上被拒绝工件的惩罚值之和的情况,通过给出一个动态规划算法,证明当批容量为常量时问题是多项式时间可解的.     

Maximizing Weighted number of Just-in-Time Jobs on Unrelated Parallel Machines

We are concerned with problems of scheduling jobs non-preemptively with the objective to maximize the weighted number of jobs that are completed exactly at their due dates. It has been shown that the problems for single machine and identical parallel machines are polynomial time solvable. The purpose of this paper is to establish the complexity status of the problem for unrelated parallel machine, which was left open. First, we present a polynomial time algorithm for solving the problem when the number of machine is fixed. Second, we show that when the number of machine is a part of input, the problem becomes NP-hard in the strong sense.     

Minimizing Mean Completion Time in a Batch Processing System

We consider batch processing jobs to minimize the mean completion time. A batch processing machine can handle up to $B$ jobs simultaneously. Each job is represented by an arrival time and a processing time. Jobs processed in a batch have the same completion time, i.e., their common starting time plus the processing time of their longest job. For batch processing, non-preemptive scheduling is usually required and we discuss this case. The batch processing problem reduces to the ordinary uniprocessor system scheduling problem if $B=1$. We focus on the other extreme case $B=+\infty$. Even for this seemingly simple extreme case, we are able to show that the problem is NP-hard for the weighted version. In addition, we establish a polynomial time algorithm for a special case when there are only a constant number of job processing times. Finally, we give a polynomial time approximation scheme for the general case.     

求解差异机器平行批调度的双目标协同蚁群算法

利用用户的偏好信息, 提出一种基于蚁群的双目标协同优化算法(Bi-objective synergy ant colony optimization algorithm based on Pareto domination, PDACO)并用于求解平行批处理机调度问题. 考虑在一组差异容量并带有不同加工功率的平行批处理机器上, 加工带有不同到达时间、尺寸和加工时间的一组工件, 以同时最小化最大完工时间和总能耗. 偏好向量的引入虽然可以提高算法的收敛性, 但会降低解的多样性. 为了弥补这一缺陷, 在本文所提算法中, 利用两个子蚁群分别沿着不同方向, 迭代地进行独立和联合搜索. 最后, 通过大量的仿真实验验证了本文提出算法的有效性.     

分布式估计算法在考虑差异工件的并行批处理机调度中的应用

论文考虑包含差异工件的并行批处理机调度问题，优化目标是最小化制造跨度.在不违背机器容量的限制下，所有工件需要被分成不同的批次，然后被安排在机器上进行加工.首先根据问题提出一个混合整数规划模型，并提出一个下界；采用FF-LPT规则实现对工件的分批和排序；然后提出基于4种更新机制的分布式估计算法（EDA）来对问题求解.最后通过实验对各类规模不同的算例进行仿真，并将结果和模拟退火算法（SA）、遗传算法（GA）作对比，验证了算法的有效性.     

Approximation Schemes for Scheduling on Uniformly Related and Identical Parallel Machines

We give a polynomial approximation scheme for the problem of scheduling on uniformly related parallel machines for a large class of objective functions that depend only on the machine completion times, including minimizing the l_p norm of the vector of completion times. This generalizes and simplifies many previous results in this area.     

An Experimental Study of Algorithms for Weighted Completion Time Scheduling

We consider the total weighted completion time scheduling problem for parallel identical machines and precedence constraints, P| prec|\sum w _i C _i . This important and broad class of problems is known to be NP-hard, even for restricted special cases, and the best known approximation algorithms have worst-case performance that is far from optimal. However, little is known about the experimental behavior of algorithms for the general problem. This paper represents the first attempt to describe and evaluate comprehensively a range of weighted completion time scheduling algorithms. We first describe a family of combinatorial scheduling algorithms that optimally solve the single-machine problem, and show that they can be used to achieve good performance for the multiple-machine problem. These algorithms are efficient and find schedules that are on average within 1.5\percent of optimal over a large synthetic benchmark consisting of trees, chains, and instances with no precedence constraints. We then present several ways to create feasible schedules from nonintegral solutions to a new linear programming relaxation for the multiple-machine problem. The best of these linear programming-based approaches finds schedules that are within 0.2\percent of optimal over our benchmark. Finally, we describe how the scheduling phase in profile-based program compilation can be expressed as a weighted completion time scheduling problem and apply our algorithms to a set of instances extracted from the SPECint95 compiler benchmark. For these instances with arbitrary precedence constraints, the best linear programming-based approach finds optimal solutions in 78\percent of cases. Our results demonstrate that careful experimentation can help lead the way to high quality algorithms, even for difficult optimization problems. Received October 30, 1998; revised March 28, 2001.     

Minimizing Total Weighted Tardiness in a Generalized Job Shop

We consider a generalization of the classical job shop scheduling problem with release times, positive end–start time lags, and a general precedence graph. As objective we consider the total weighted tardiness. We use a tabu search algorithm to search for the order in which the operations are processed on each machine. Given a sequence of operations on each machine, we determine optimal starting times by solving a maximum cost flow problem. This solution is used to determine the neighborhood for our tabu search algorithm. All sequences in our neighborhood are obtained by swapping certain pairs of adjacent operations. We show that only swaps that possess a certain property can improve the current solution; if no such swap is available in the neighborhood, then the current solution is globally optimal. In the computational results we compare our method with other procedures proposed in literature. Our tabu search algorithm seems to be effective both with respect to time and solution quality. The research was carried out at the Technische Universiteit Eindhoven and the Universiteit Utrecht with support of Baan and the Future and Emerging Technologies programme of the EU under contract number IST-1999-14186 (ALCOM-FT).     

差异工件并行批调度问题中遗传算法研究

本文考虑了遗传算法在包含差异工件的并行批处理机调度中的应用问题.工件具有不同的尺寸和到达时间.首先基于问题假设提出了一个数学规划模型,并采用BF、ERT-LPT实现工件的分批排序调度.然后考虑到这是一个NP-Hard问题,设计了新的选择、交叉、变异操作并结合遗传算法进行求解.最后通过仿真实验对比,验证了算法的有效性.     

Minimizing Total Completion Time Subject to Job Release Dates and Preemption Penalties

Extensive research has been devoted to preemptive scheduling. However, little attention has been paid to problems where a certain time penalty must be incurred if preemption is allowed. In this paper, we consider the single-machine scheduling problem of minimizing the total completion time subject to job release dates and preemption penalties, where each time a job is started, whether initially or after being preempted, a job-independent setup must take place. The problem is proved to be strongly NP-hard even if the setup time is one unit. We also study a natural extension of a greedy algorithm, which is optimal in the absence of preemption penalty. It is proved that the algorithm has a worst-case performance bound of 25/16, even when the maximum completion time, i.e., makespan, criterion is considered simultaneously.