一类复杂生产调度问题的基于符号演绎的调度方法

针对带有时问不确定件的复杂生产过程调度问题,提出一种基于符号演绎的调度方法.首先将时间的不确定性信息看作符号型数据,并提出一种用于处理这些符号型数据的基于不确定区间的符号演绎方法;然后将此符号演绎方法与遗传算法相结合,提出一种预排调度计划与实时调度规则相结合的调度方法来求解上述复杂生产调度问题.实验表明,将基于符号演绎的调度方法用于求解带有时间不确定性的复杂生产过程调度问题,能够取得较好的调度效果.     

Converging to periodic schedules for cyclic scheduling problems with resources and deadlines

Cyclic scheduling has been widely studied because of the importance of applications in manufacturing systems and in computer science. For this class of problems, a finite set of tasks with precedence relations and resource constraints must be executed repetitively while maximizing the throughput. Many applications also require that execution schedules be periodic i.e. the execution of each task is repeated with a fixed global period w.The present paper develops a new method to build periodic schedules with cumulative resource constraints, periodic release dates and deadlines. The main idea is to fix the period w, to unwind the cyclic scheduling problem for some number of iterations, and to add precedence relations so that the minimum time lag between two successive executions of any task equals w. Then, using any usual (not cyclic) scheduling algorithm to compute task starting times for the unwound problem, we prove that either the method converges to a periodic schedule of period w or it fails to compute a schedule. A non-polynomial upper bound on the number of iterations to unwind in order to guarantee that cyclic precedence relations and resource constraints are fulfilled is also provided. This method is successfully applied to a real-life problem, namely the software pipelining of inner loops on an embedded VLIW processor core by using a Graham list scheduling algorithm.     

染缸排产建模及滑动时间窗启发式调度算法

针对染缸排产问题约束复杂、任务规模大、排产效率要求高的特点,为了提高问题模型和算法在实际场景中的适用性,建立了染缸排产增量调度模型,提出了滑动时间窗启发式调度（STWS）算法。该算法以最小化延误代价、洗缸成本、染缸切换成本为优化目标,使用启发式调度规则,按照优先级顺序调度产品;对于每个产品的调度,先用动态拼缸算法和拆缸算法进行批次划分,然后调用批次最佳排序算法调度批次。使用某染纱企业车间实际生产数据仿真调度,所提算法可在10 s内完成月度计划的调度。相对于人工排产方式,所提算法提高了排产效率,显著优化了三个目标,在增量调度中洗缸成本和染缸切换成本也有明显优化。实验结果表明所提算法具有很好的调度能力。     

Loop Shifting for Loop Compaction

The idea of decomposed software pipelining is to decouple the software pipelining problem into a cyclic scheduling problem without resource constraints and an acyclic scheduling problem with resource constraints. In terms of loop transformation and code motion, the technique can be formulated as a combination of loop shifting and loop compaction. Loop shifting amounts to moving statements between iterations thereby changing some loop independent dependences into loop carried dependences and vice versa. Then, loop compaction schedules the body of the loop considering only loop independent dependences, but taking into account the details of the target architecture. In this paper, we show how loop shifting can be optimized so as to minimize both the length of the critical path and the number of dependences for loop compaction. The first problem is well-known and can be solved by an algorithm due to Leiserson and Saxe. We show that the second optimization (and the combination with the first one) is also polynomially solvable with a fast graph algorithm, variant of minimum-cost flow algorithms. Finally, we analyze the improvements obtained on loop compaction by experiments on random graphs.     

性能随时间衰减的锅炉蒸汽系统循环调度

针对性能随时间衰减的锅炉蒸汽系统的循环调度问题进行了研究.首先建立了描述该问题的混合整数非线性模型;然后提出了确定各锅炉循环运行状态的时间分段策略以简化问题的求解;最后应用列队竞争算法对该混合整数非线性规划问题进行优化计算.采用某锅炉蒸汽系统循环调度的实例对所提出的方法进行了验证,计算结果表明循环调度优化能够获得比人工随机安排更优的调度方案,节能效果十分明显.     

基于离线利用率分析的混合关键级任务调度优化研究

现主流的混合关键级调度算法在系统高关键级状态下时主要通过抛弃低关键级任务来保证高关键级任务的执行,进而保证系统的正确性。此方法常常导致低关键级任务无法执行但系统资源却过剩的问题发生,故基于该问题提出复合型SDU（schedule depend on utilization）调度算法。该方法根据任务集对系统资源需求情况的不同进行利用率区间的划分,通过对各个区间实际使用情况的分析,设计相应的子算法进行调度,并提出了SDU算法对应的可调度性判据。仿真实验结果表明,相较于混合关键级任务调度领域主流的EDF-VD（earliest deadline first-virtual deadline）算法,所提SDU算法可将系统对任务集的调度率提升30%,并在相同情况下将系统对低关键级任务的执行率提升165%,证明了该算法可以极大地提高系统资源使用率,并保证系统服务完整性。     

A new ILS algorithm for parallel machine scheduling problems

This paper addresses the non-preemptive scheduling problem of scheduling jobs on identical parallel machines to minimize the maximum completion time or makespan. The problem has been proved to be NP-hard in the strong sense. The NP-hardness of the problem motivates us to develop a new methodology to obtain near-optimal solutions. We formulate the problem as an integer programming and then propose a new iterated local search (ILS) algorithm based on a variable number of cyclic exchanges to solve it. The properties of the solutions are derived and the results are used to improve the computational efficiency of our algorithm. Computational experiments show that the cyclic exchange neighborhood embedded in an iterated local search framework is effective for solving the scheduling problems with up to 1000 jobs and 40 machines within a reasonable amount of computation time. Received: April 2005 / Accepted: January 2006     

基于任务图的多处理器负载均衡调度算法

针对共享存储多处理机系统中各处理机负载不均衡的问题,提出一种新的任务调度算法--多重波前法.在任务图划分的基础上,采用分层调度方式对原波前法进行改进,通过对任务序列进行多重遍历和重组以降低各处理器的分配误差,利用循环调度算法提高任务调度结果的精度,并给出该算法的并行实现.实验结果证明,该算法具有较低的任务分配误差和较高...     

基于自适应蚁群算法的作业车间模糊调度研究

在研究不确定生产调度问题的基础上,针对具有模糊加工时间和模糊交货期的调度问题给出了作业车间模糊调度模型,用三角模糊数表示模糊加工时间,梯形模糊数表示模糊交货期,以交货期平均满意度最大作为调度目标.针对模糊调度问题对基本蚁群算法作了改进,并给出了新的状态转移规则,同时采用自适应信息素更新策略使算法能快速跳出局部收敛,进行仿真结果验证了自适应蚁群算法求解作业车间模糊调度的有效性.     

同构Hadoop集群环境下改进的延迟调度算法

在Hadoop框架下计算资源和数据资源可以在不同物理位置的特点产生本地化问题。延迟调度算法的产生旨在解决本地化问题, 此算法根据任务待处理数据的物理位置作为作业的计算节点, 调度任务至目标节点。但是可能出现同一作业中若干任务集中运行在某一计算节点, 导致作业达不到理想的并行效果。针对原有的延迟调度算法, 提出延迟一容量调度算法, 允许部分任务选择非本地化节点作为原延迟调度算法中任务的目标计算节点, 以提高作业的响应时间与增加作业的并行程度。最后通过实验对比分析, 改进后的算法在执行效率和并行效果明显优于原延迟调度算法。     

一种新的异构网格任务调度算法

提出了一种基于免疫计算的异构网格任务调度算法。设计了异构网格独立任务调度问题的数学模型,给出了免疫调度算法的框架、基于实数编码的克隆变异算子和浓度抑制算子,并在仿真环境下进行了实验。实验结果表明,算法能有效地解决异构网格任务调度问题,具有较好的应用价值。     

基于Hadoop的车辆调度算法优化及应用

随着互联网技术的快速发展,各行各业所产生的信息数据也在以指数级的速度增长.传统的车辆调度算法已经不能够很好地解决车辆调度问题中出现的实时性,大规模等问题.因此,本文构建了一种基于Hadoop的动态车辆调度并行智能优化算法.该算法以传统遗传算法为基础,通过改善遗传算法全局优化能力弱和收敛于局部次优解的问题,并利用Hadoop平台的并行计算机制对传统遗传算法进行改进,使其能够有效应对大规模、快速响应的车辆调度.数值计算结果表明：基于Hadoop的车辆调度算法能够有效提升传统调度算法的优化性能,在处理大规模车辆调度问题时具有良好的加速比.     

复杂负载下数据缓冲区自适应调度方法仿真

传统数据缓冲区调度方法调度时间长、调度结果误差大且不能够完全应对复杂负载问题。因此提出了复杂负载下数据缓冲区自适应调度方法,通过构建模拟数据缓冲区来定义调整的方向,在缓冲数据中,利用操控行为和代替方法之间进行相互不变性推测,获取数据缓冲错失函数;通过引用能力制约条件,将时间分成一些零碎的小片段,利用数据缓冲错失,获取时间约束模型;引入时间约束模型,需要依据时间顺序对事件进行调顺序,结合根据模拟自适应算法所得到的数据,使用雷达资源约束条件能够精准快速地衡量各种数据波束所要求的指令,获取自适应调度模型,为某一个调度间隔选取出最完善的自适应调度方法。通过仿真结果表明:上述方法能够完全应对复杂负载情况的问题,且数据缓冲区自适应调度时间短、调度结果误差小。     

基于细菌觅食优化算法的城市轨道交通调度优化

为了合理高效地制定城市轨道交通调度方案,实现客流与车次的优化配置,提出了一种基于细菌觅食优化算法的城市轨道交通调度优化策略。兼顾乘客与运营企业双方利益,以发车间隔为决策变量,乘客平均候车时间最短和发车次数最少为优化目标,建立调度优化模型,并对细菌觅食优化算法求解该调度模型的过程进行分析。结合某城市轨道交通一号线实际运营数据进行仿真实验,并与其他算法的优化结果进行对比分析,实验表明该算法和模型能有效解决城市轨道交通调度优化问题。     

Complexity of cyclic scheduling problems: A state-of-the-art survey

In this survey we review the current complexity status of basic cyclic scheduling models. We start with the formulations of three fundamental cyclic scheduling problems, namely the cyclic jobshop, cyclic flowshop, and cyclic project scheduling problems. We present state-of-the-art results on the computational complexity of the problems, paying special attention to recent results on the unsolvability (NP-hardness) of various cyclic problems arising from the scheduling of robotic cells.     

Data‐aware task scheduling on heterogeneous hybrid memory multiprocessor systems

In this paper, we propose a method about task scheduling and data assignment on heterogeneous hybrid memory multiprocessor systems for real‐time applications. In a heterogeneous hybrid memory multiprocessor system, an important problem is how to schedule real‐time application tasks to processors and assign data to hybrid memories. The hybrid memory consists of dynamic random access memory and solid state drives when considering the performance of solid state drives into the scheduling policy. To solve this problem, we propose two heuristic algorithms called improvement greedy algorithm and the data assignment according to the task scheduling algorithm, which generate a near‐optimal solution for real‐time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm, which is commonly used to solve heterogeneous task scheduling problem. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance and demonstrate that considering data allocation in task scheduling is significant for saving energy. We conduct experiments on two heterogeneous multiprocessor systems. Copyright © 2016 John Wiley & Sons, Ltd.     

Scheduling Space–Ground Communications for the Air Force Satellite Control Network

We present the first coupled formal and empirical analysis of the Satellite Range Scheduling application. We structure our study as a progression; we start by studying a simplified version of the problem in which only one resource is present. We show that the simplified version of the problem is equivalent to a well-known machine scheduling problem and use this result to prove that Satellite Range Scheduling is NP-complete. We also show that for the one-resource version of the problem, algorithms from the machine scheduling domain outperform a genetic algorithm previously identified as one of the best algorithms for Satellite Range Scheduling. Next, we investigate if these performance results generalize for the problem with multiple resources. We exploit two sources of data: actual request data from the U.S. Air Force Satellite Control Network (AFSCN) circa 1992 and data created by our problem generator, which is designed to produce problems similar to the ones currently solved by AFSCN. Three main results emerge from our empirical study of algorithm performance for multiple-resource problems. First, the performance results obtained for the single-resource version of the problem do not generalize: the algorithms from the machine scheduling domain perform poorly for the multiple-resource problems. Second, a simple heuristic is shown to perform well on the old problems from 1992; however it fails to scale to larger, more complex generated problems. Finally, a genetic algorithm is found to yield the best overall performance on the larger, more difficult problems produced by our generator.     

On scheduling transactions in a grid processing system considering load through Ant Colony Optimization

Load balanced transaction scheduling problem is an important issue in distributed computing environments including grid system. This problem is known to be NP-hard and can be solved by using heuristic as well as any meta-heuristic method. We ponder over the problem of the load balanced transaction scheduling in a grid processing system by using an Ant Colony Optimization for load balancing. The problem that we consider is to achieve good execution characteristics for a given set of transactions that has to be completed within their given deadline. We propose a transaction processing algorithm based on Ant Colony Optimization (ACO) for load balanced transaction scheduling. We modify two meta-heuristic along with ACO and three heuristic scheduling algorithms for the purpose of comparison with our proposed algorithm. The results of the comparison show that the proposed algorithm provides better results for the load balanced transaction scheduling in the grid processing system.     

云环境下基于多目标的多科学工作流调度算法

针对现有云环境下的多科学工作流调度算法中存在的未考虑安全调度问题,提出多科学工作流安全-时间约束费用优化算法MSW-SDCOA（Multi-Scientific Workflows Security-Deadline constraint Cost OptimizationAlgorithm）.首先MSW-SDCOA基于数据依赖关系压缩科学工作流,减少任务节点数从而节省了调度开销;并通过改进HEFT（Heterogeneous Earliest-Finish-Time）算法形成调度序列,以实现全局多目标优化调度;最后,通过优化ACO（Ant Colony Optimization）中信息素更新策略和启发式信息,进一步改善费用优化效果.仿真实验表明,MSW-SDCOA算法在费用优化效果上比MW-DBS算法提高了约14%.     

The periodicity and robustness in a single-track train scheduling problem

The most important operating problem in any railway industry is to produce robust train timetables with minimum delays. The train scheduling problem is defined as an application of job shop scheduling which is considered to be one of the most interesting research topics. This paper deals with scheduling different types of trains in a single railway track. The authors have focused on the robust and periodic aspects of produced timetables. This paper is also concerned with some applicable constraints, such as the acceleration and deceleration times, station capacity and headway constraints. The periodic timetable for railways is modeled based on the periodic event scheduling problem (PESP). Furthermore, a fuzzy approach is used to reach a tradeoff among the total train delays, the robustness of schedules, and the time interval between departures of trains from the same origins. To solve large-scale problems, a meta-heuristic algorithm based on simulated annealing (SA) is utilized and validated using some numerical examples on a periodic robust train scheduling problem. Finally, a robustness measure is defined in order to assure the effectiveness of the proposed SA to find robust solutions.