A simulated annealing approach to integrated production scheduling

This paper describes an approach to manufacturing planning that seeks to integrate both process planning and scheduling. We show that separating these two related tasks, as is the common practice, can impose constraints that substantially reduce the quality of the final schedule. These constraints arise from premature decisions regarding operation sequence and allocation of manufacturing resources. Having formulated an integrated process planning and scheduling problem, we describe a solution technique based on simulated annealing. We compare this approach with others reported in the literature, considering both their generality and performance. In particular, we perform a detailed empirical comparison between simulated annealing and the popular technique of dispatching rules. Our results, achieved with two distinct sets of example problems, show that simulated annealing can produce solutions of significantly higher quality than those achieved through a published dispatching rule approach.     

A unified view on planning,scheduling and dispatching for a factory

In a smart factory, the decisions of planning, scheduling and dispatching are made based on the real time information through Internet-of-Things. To ensure the organization objective can be carried out through different levels faithfully, planning, scheduling and dispatching should be considered through a cyber physical production system in an integrated manner. To support the implementation of a smart factory, their definitions have to be given through a unified view. The distinction between planning and scheduling is given based on microeconomics and queueing theory. The distinction between scheduling and dispatching is analyzed from the viewpoint of computational complexity and hierarchical decomposition. Based on the elasticity of price and capacity, planning can be separated into demand planning or capacity planning. Scheduling period is the time horizon where price and average production costs are insensitive to the production rate. The critical roles of the master production schedule and throughput targets in job scheduling have been explained through the concept of hierarchical decomposition. Dispatching is the last layer of scheduling in the hierarchical decomposition.     

战场环境中多无人机动态任务调度

采用了基于合同网的分布式规划方法,研究了战场环境中多无人机动态任务调度问题,并建立了数学模型,提出了分布式的任务调度体系结构,设计了一种基于代价变换的概率路标图路径规划算法,该算法能够在任务调度阶段,快速预估无人机执行不同任务的飞行航路,扩展了合同网协议,可在一次拍卖中“并发”进行多次交易,提高了任务调度的效率。通过多种合同类型的综合,解决了复杂战场态势下的任务调度问题。     

The influence of alternate process planning in job shop scheduling

The purpose of this research is to investigate the relationships between the alternate process planning and the scheduling performance regarding to three different criteria; they are mean tardiness, mean work-in-process, and mean machine utilization. In this paper, three factors which are the percentage of alternate process planning, the scheduling algorithm, and dispatching rule are considered relative to those three criteria. The result of this research will show how the percentage of alternate process planning influences those criteria and what the percentage of alternate process planning should be done efficiently and economically.     

一种开放混合实时系统的开放自适应调度算法

开放计算环境下的实时与非实时任务不确定并发,以及多种实时约束混合的复杂约束系统,即开放混合实时系统的需求越来越广泛.通过引入接收控制、调度服务器、自适应调节机制,提出一种开放环境下的自适应实时系统调度架构--OARtS(open adaptive real-time scheduling).它能适应开放计算环境的不确定性,有控制地接受实时任务运行;可根据系统空闲计算带宽变化,自适应地调节任务的实时等级,使得系统运行在最优的实时性能上;对于软实时任务,可根据其计算带宽需求变化,自适应地调节其计算带宽分配,以适应任务执行时间时变引起的实时不确定性.     

A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters

In this paper, a heuristic dynamic scheduling scheme for parallel real-time jobs executing on a heterogeneous cluster is presented. In our system model, parallel real-time jobs, which are modeled by directed acyclic graphs, arrive at a heterogeneous cluster following a Poisson process. A job is said to be feasible if all its tasks meet their respective deadlines. The scheduling algorithm proposed in this paper takes reliability measures into account, thereby enhancing the reliability of heterogeneous clusters without any additional hardware cost. To make scheduling results more realistic and precise, we incorporate scheduling and dispatching times into the proposed scheduling approach. An admission control mechanism is in place so that parallel real-time jobs whose deadlines cannot be guaranteed are rejected by the system. For experimental performance study, we have considered a real world application as well as synthetic workloads. Simulation results show that compared with existing scheduling algorithms in the literature, our scheduling algorithm reduces reliability cost by up to 71.4% (with an average of 63.7%) while improving schedulability over a spectrum of workload and system parameters. Furthermore, results suggest that shortening scheduling times leads to a higher guarantee ratio. Hence, if parallel scheduling algorithms are applied to shorten scheduling times, the performance of heterogeneous clusters will be further enhanced.     

Dynamic Dispatching of Cyclic Real-Time Tasks with Relative Timing Constraints

In some hard real-time systems, relative timing constraints may be imposed on task executions, in addition to the release time and deadline constraints. Relative timing constraints such as separation or relative deadline constraints may be given between start or finish times of tasks (Gerber et al., 1995; Han and Lin, 1989; Han et al., 1992; Han and Lin, 1992; Han et al., 1996).One approach in real-time scheduling is to find a total order on a set of N tasks in a scheduling window, and cyclically use this order at run time to execute tasks. However, in the presence of relative timing constraints, if the task execution times are nondeterministic with defined lower and upper bounds, it is not always possible to statically assign task start times at pre-runtime for a given task ordering (Gerber et al., 1995).We develop a technique called dynamic cyclic dispatching as an extension of a parametric dispatching mechanism in (Gerber et al., 1995). An ordered set of N tasks is assumed to be given in a scheduling window and this schedule(ordering) is cyclically repeated at runtime in consecutive scheduling windows. Relative timing constraints between tasks may be defined across scheduling window boundaries as well as within one scheduling window. A task set is defined to be dispatchable if there exists any way in which the tasks can be dispatched with all their timing constraints satisfied. An off-line algorithm is presented to check the dispatchability of a task set and to obtain parametric lower and upper bound functions for task start times if the task set is dispatchable. These parametric bound functions are evaluated at runtime to obtain a valid time interval during which a task can be started. The complexity of this off-line component is shown to be O(n ² N ³) where n is the number of tasks in a scheduling window that have relative timing constraints with tasks in the next scheduling window. An online algorithm can evaluate these bounds in O(N) time.Unlike static approaches which assign fixed start times to tasks in the scheduling window, our approach allows us to flexibly manage the slack times at runtime without sacrificing the dispatchability of tasks. Also, a wider class of relative timing constraints can be imposed to the task set compared to the traditional approaches.     

嵌入式并行系统中基于任务优化的调度算法

在嵌入式并行计算系统中,任务调度是决定系统性能的关键。多任务调度中,启发式调度法是一种设计简单且性能良好的调度方法。目前的调度算法大多是基于任务复制的,没有充分考虑前驱任务与其后继任务间的相关性。该文提出了一种基于相关任务优化(DTO)的调度算法,通过分析已用处理机的负载和空闲时间,尽量减少系统的调度长度和处理机数目。算法分析结果表明,DTO算法在性能上优于其他算法,对嵌入式并行计算系统中的多任务调度是一个较好的选择。     

Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling

Process planning and scheduling are two key sub-functions in the manufacturing system. Traditionally, process planning and scheduling were regarded as the separate tasks to perform sequentially. Recently, a significant trend is to integrate process planning and scheduling more tightly to achieve greater performance and higher productivity of the manufacturing system. Because of the complementarity of process planning and scheduling, and the multiple objectives requirement from the real-world production, this research focuses on the multi-objective integrated process planning and scheduling (IPPS) problem. In this research, the Nash equilibrium in game theory based approach has been used to deal with the multiple objectives. And a hybrid algorithm has been developed to optimize the IPPS problem. Experimental studies have been used to test the performance of the proposed approach. The results show that the developed approach is a promising and very effective method on the research of the multi-objective IPPS problem.     

A distributed multi-agent production planning and scheduling framework for mobile robots

Inspired by the new achievements in mobile robotics having as a result mobile robots able to execute different production tasks, we consider a factory producing a set of distinct products via or with the additional help of mobile robots. This particularly flexible layout requires the definition and the solution of a complex planning and scheduling problem. In order to minimize production costs, dynamic determination of the number of robots for each production task and the individual robot allocation are needed. We propose a solution in terms of a two-level decentralized Multi-Agent System (MAS) framework: at the first, production planning level, agents are tasks which compete for robots (resources at this level); at the second, scheduling level, agents are robots which reallocate themselves among different tasks to satisfy the requests coming from the first level. An iterative auction based negotiation protocol is used at the first level while the second level solves a Multi-Robot Task Allocation (MRTA) problem through a distributed version of the Hungarian Method. A comparison of the results with a centralized approach is presented.     

模糊动态抢占调度算法

针对不确定任务特征，提出应用模糊理论进行动态抢占调度，用语言模糊集来描述任务的不确定特征和不同的优先级等级，利用最大隶属度原理确定任务的优先级等级，采用优先调度高优先级等级任务的调度策略提高重要任务的调度成功率，实现具有不确定任务特征的抢占调度，与传统的EDF和LSF算法相比较，仿真表明，所提算法能够提高重要任务的调度成功率，并降低重要任务的截止期错失率；同时，任务间的平均切换次数大大小于LSF的平均切换次数，而与EDF保持相当，该方法可应用于计算机控制系统的控制任务调度，并借鉴于其它具有不确定任务特征或具有有限优先级等级的实时调度问题研究中。     

一种实时异构系统的集成动态调度算法

提出了一种实时异构系统的集成动态调度算法.该算法通过一个新的任务分配策略以及软实时任务的服务质量QoS(quality of service)降级策略,不仅以统一方式完成了对实时异构系统中硬、软实时任务的集成动态调度,而且提高了算法的调度成功率.同时,还进行了大量的模拟研究.这些模拟以传统的近视算法为基准,将其应用在实时异构系统集成动态调度时的调度成功率与新算法进行比较,模拟结果表明,在多种任务参数取值下,新算法的调度成功率均高于传统的近视算法.     

计算机控制中的模糊调度设计

研究了具有模糊截止期的多控制任务的实时调度问题,提出了奉献度的概念和最大奉献优先（LDF）的调度策略．为了减小因任务间频繁切换造成的系统开销。提出了基于抢占阈值的最大奉献优先（TLDF）调度策略．最后,通过仿真比较了LDF和TLDF两种调度策略,实现了具有模糊截止期的控制任务调度,在减少并均衡控制性能损失的同时提高系统计算资源的使用率．     

一种云工作流任务调度能效优化算法

工作流任务执行时带来的高能耗不仅会增加云资源提供方的经济成本,而且会降低云系统的可靠性。为了满足截止时间的同时,降低工作流执行能耗,提出一种工作流能效调度算法CWEES。算法将能效优化调度划分为三个阶段：初始任务映射、处理器资源合并和任务松驰。初始任务映射旨在通过任务自底向上分级排序得到任务调度初始序列,处理器资源合并旨在通过重用松驰时间合并相对低效率的处理器,降低资源使用数量,任务松驰旨在为每个任务重新选择带有合适电压/频率等级的最优目标资源,在不违背任务顺序和截止时间约束前提下降低工作流执行总能耗。通过随机工作任务模型对算法的性能进行了仿真实验分析。结果表明,CWEES算法不仅资源利用率更高,而且可以在满足截止时间约束下降低工作流执行能耗,实现执行效率与能耗的均衡。     

The anatomy study of high performance task scheduling algorithm for Grid computing system

Large-scale computation is frequently limited to the performance of computer hardware or associated cost. However, as the development of information and network technologies thrives, idle computers all over the world can be utilized and organized to enhance overall computation performance; that is, Grid environments that facilitate distributed computation. Hence, the dispatching and scheduling of tasks should be considered as an important issue. Previous studies have demonstrated Grid environments that are composed of idled computers around the globe and are categorized as a type of Heterogeneous Computing (HC). However, scheduling heuristics currently applied to HC focus on the search of minimum makespan, instead of the reduction of cost. In addition, relevant studies usually presume that HC is based on high-speed bandwidth and the communication time is ignored. Further, in response to the call for user-pay policy, as a user dispatches a job to a Grid environment for computation, each execution task would be charged. It is difficult to estimate a job will be dispatched to which and how many computers; it is impossible to predetermine scheduling heuristic which is proposed in previous studies will result in the optimal makespan, and mention actual cost and risk. Therefore, this study proposes ATCS-MCT (Apparent Tardiness Cost Setups-Minimum Completion Time) scheduling algorithm that composes of execution time, weight, due date, and communication time factors to testify that the ATCS-MCT scheduling algorithm not only achieves better makespan than Min–min scheduling heuristics do but also reduces costs.     

蓄热地板分散式电采暖负荷协同优化调度系统设计

传统的调度系统在调度蓄热地板分散式电采暖负荷时,调度范围很小,导致采暖效果很差。为了解决这一问题,设计了一种新的蓄热地板分散式电采暖负荷协同优化调度系统,对系统的硬件和软件分别进行设计。硬件部分重点设计了采暖器、控制器和处理器,采暖器选用型号为TSDA-523的新型集热采暖器,提高采暖效果;控制器采用了LPI控制器,能够实现连续控制;处理器选用了型号为AN176的中央集成处理器,在短时间内能够处理大量电采暖负荷。软件由电采暖负荷量确定、电采暖负荷量处理、电采暖负荷量调度三部分组成。为了检测系统效果,与传统系统进行对比,结果表明,系统能够完成大范围调度,有效提高采暖效果。     

基于物联网大数据节能调度框架研究

当今社会,物联网的快速发展离不开数据的支撑,数据的持久化存储与检索离不开物联网的边缘服务器,边缘服务器相当于物联网的数据服务中心,同时也是云环境重要组成部分,因此数据中心的能耗问题成为核心问题。现今在边缘侧常用的大数据资源调度框架为YARN,该框架并不能对能耗进行有效控制,针对此问题,提出一种基于YARN的物联网大数据节能调度框架,该框架是在YARN基础上进行优化,增加三个关键性的功能模块,分别对系统任务进行分析、计算、调度、分配,实现了细粒化管理。通过模拟实验验证该框架能在保证系统任务性能的同时减少能量损耗。     

开放式实时嵌入式系统自适应调度模型研究

开放式实时嵌入式系统中多类型实时任务并存和资源受限的情况给实时调度机制带来了新的需求和挑战。通过引入准入控制、资源管理、调度服务器、自适应调节机制等,提出了一个形式化的自适应调度模型。它能适应开放计算环境的不确定性,有控制地接受不同类型任务的运行;可根据系统资源和任务需求的最新变换情况计算带宽变化,自适应地调节任务的优先等级,使得系统运行在最优的实时性能上;该模型在某航空机载系统设计中得到了实际应用,同其它类似系统相比,该模型的应用提高了系统的调度性和系统稳定性。     

实时异构系统的动态调度算法研究

实时多处理器系统是解决复杂时应用的有效手段,目前对实时多处理器调度算法的研究却大多集中在同构系统上,对实时异构系统的调度则研究得比较少,提出了一种新的实时异构系统的动态调度算法,该算法采用了集中式的调度方案,同时,引入了一个新的任务分配策略,从而通过提高任务可行性而提高了算的调度成功率,此外,为了评估该算法的性能,还进行了大量的模拟研究,由于近视算法经简单修改便可以应用到实时异构系统的动态调度中,因此,在模拟研究中,以近视算法作为基准,将其应用于实时异构系统动态调度时的性能与新算法进行了比较,模拟结果显示,在多种任务参数的取值下,新算法的调度成功率均高于近视算法。     

Task mapper and application-aware virtual machine scheduler oriented for parallel computing

We design a task mapper TPCM for assigning tasks to virtual machines, and an application-aware virtual machine scheduler TPCS oriented for parallel computing to achieve a high performance in virtual computing systems. To solve the problem of mapping tasks to virtual machines, a virtual machine mapping algorithm (VMMA) in TPCM is presented to achieve load balance in a cluster. Based on such mapping results, TPCS is constructed including three components: a middleware supporting an application-driven scheduling, a device driver in the guest OS kernel, and a virtual machine scheduling algorithm. These components are implemented in the user space, guest OS, and the CPU virtualization subsystem of the Xen hypervisor, respectively. In TPCS, the progress statuses of tasks are transmitted to the underlying kernel from the user space, thus enabling virtual machine scheduling policy to schedule based on the progress of tasks. This policy aims to exchange completion time of tasks for resource utilization. Experimental results show that TPCM can mine the parallelism among tasks to implement the mapping from tasks to virtual machines based on the relations among subtasks. The TPCS scheduler can complete the tasks in a shorter time than can Credit and other schedulers, because it uses task progress to ensure that the tasks in virtual machines complete simultaneously, thereby reducing the time spent in pending, synchronization, communication, and switching. Therefore, parallel tasks can collaborate with each other to achieve higher resource utilization and lower overheads. We conclude that the TPCS scheduler can overcome the shortcomings of present algorithms in perceiving the progress of tasks, making it better than schedulers currently used in parallel computing.