多核处理器数控系统的全局调度算法

提出了面向多核处理器系统的限制抢占调度算法,通过在任务的末尾设置高优先级(抢占阈值)的限制抢占区,减少任务运行过程中被抢占的次数,提高调度效率。建立了限制抢占调度的可调度性判定条件和限制抢占区设计方法。仿真结果表明,限制抢占调度的调度性能较抢占调度和延迟抢占调度的调度性能有明显提高。     

Mathematical model and parallel genetic algorithm for hybrid flexible flowshop lot streaming problem

Lot streaming is the technique of splitting a given job into sublots to allow the overlapping of successive operations in multi-stage manufacturing systems thereby reducing production makespan. Several research articles appeared in literature to solve this problem and most of these studies are limited to pure flowshop environments where there is only a single machine in each stage. On the other hand, because of the applicability of hybrid flowshops in different manufacturing settings, the scheduling of these types of shops is also extensively studied by several authors. However, the issue of lot streaming in hybrid flowshop environment is not well studied. In this paper, we aim to contribute in bridging the gap between the research efforts in flowshop lot streaming and hybrid flowshop scheduling. We propose a mathematical model and a genetic algorithm for the lot streaming problem of several jobs in multi-stage flowshops where at each stage there are unrelated parallel machines. The jobs may skip some of the stages, and therefore, the considered system is a complex generalized flowshop. The proposed genetic algorithm is executed on both sequential and parallel computing platforms. Numerical examples showed that the parallel implementation greatly improved the computational performance of the developed heuristic.     

六足仿生步行机器人系统节能技术的研究

针对采用电池供电的六足仿生步行机器人其工作时间受限的情况,提出了将动态电源管理、实时任务调度和运动策略规划等方法,综合运用于其控制系统,且更为全面地考虑了机器人系统的能耗等级.这种方法对于降低机器人的系统能耗起到了实质性的作用,其整体思路与技术途径可为降低其它类似的多足步行机器人的系统能耗,提供参考与借鉴.     

多蒸馏塔炼油系统的可调度性分析

在炼油企业中,排产计划都是按以往经验来制定的.在调度初期,通常合理的库存范围就能保证系统稳定不间断地运行.但是即使在已知的初始状态下,系统的可调度性也不是明确的.所以在实际中只有不断地修改计划才能满足运作中的各种约束.这些不确定性直接导致厂区油罐内初始库存量大、资金占用率高及油罐的可用性差等问题,影响到厂区调度的柔性.通过分析蒸馏塔炼油系统在不同油罐数的情况下调度的可行性问题,得出可调度性条件:油罐初始库存关系及油罐需求边界.如此,依照这些条件就可以判断实际中蒸馏塔炼油系统初始状态的可调度性.     

机器人制造单元的建模与任务调度策略

机器人资源的合理分配和调度是提高制造单元柔性的关键。本文针对一类机器人制造单元的最小周期调度问题，应用极大代数方法建立了单元系统的调度模型，提出了基于禁忌搜索的启发式调度策略，并给出了初始可行解和搜索邻域的构造方法；最后，通过具体的运算实例，验证了所提出方法具有较高的效率，能够解决较大规模的最小周期调度问题，具有广泛的适用性。     

基于混合教学优化算法的多车间协作综合调度

目前在处理加工属性类似的多车间协作综合调度问题时，几乎都是采用调度规则，对产品加工工艺结构的依赖度过高，降低了算法对同一类问题不同实例的适应性，并且对大型实例的求解结果普遍欠佳，为此提出了一种混合教学优化算法。该算法在基本教学优化算法的基础上加入采用变异操作模拟的自学习阶段，提高其局部搜索能力，并且在教学、互学和自学三个阶段均按照模拟退火算法中Metropolis准则计算的概率，随机接受学生群体中某一个较差个体作为新个体，进一步提高算法跳出局部最优解的能力；教学、互学和自学三个阶段设计的变换操作均考虑综合调度问题中各虚拟工序之间的顺序约束关系，保证生成的解均是可行解。通过测试以往该类问题实例，得到的结果验证了所提算法的可行性和有效性。     

A New Cyclic Scheduling Algorithm for Flexible Manufacturing Systems

Flexible manufacturing system control is an NP-hard problem. A cyclic approach has been demonstrated to be adequate for an infinite scheduling problem because of maximal throughput reachability. However, it is not the only optimization criterion in general. In this article we consider the minimization of the work in process (WIP) as an economical and productivity factor. We propose a new cyclic scheduling algorithm giving the maximal throughput (a hard constraint) while minimizing WIP. This algorithm is based on progressive operations placing. A controlled beam search approach has been developed to determine at each step the schedule of the next operations. After presenting the main principles of the algorithm, we compare our approach to several most known cyclic scheduling algorithms using a significant existing example from the literature.     

优先约束的混合实时任务动态调度算法

为了对较大规模问题在短时间内给出满意的解并调度随机到达的非周期任务,提出了一种动态混合调度算法。在第一个截止期限错失时刻,通过分析系统的运行情况,推导出具有优先约束的实时任务可调度的充分条件。在系统运行前,判定周期任务的可调度性,预分配足够的处理机。当非周期任务到达系统时,在保证周期任务可调度的前提下重新给周期任务和非周期任务分配处理机。该算法具有多项式时间复杂性,适合解决较大规模的多处理机调度问题。实验结果表明,该算法有效提高了处理机的利用率和非周期任务的接收率。     

A genetic algorithm for energy-efficiency in job-shop scheduling

Many real-world scheduling problems are solved to obtain optimal solutions in term of processing time, cost, and quality as optimization objectives. Currently, energy-efficiency is also taken into consideration in these problems. However, this problem is NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the Job-shop Scheduling Problem in which machines can consume different amounts of energy to process tasks at different rates (speed scaling). This problem represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The evaluation section shows that a powerful commercial tool for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.     

A hybrid algorithm based on particle swarm optimization and simulated annealing for a periodic job shop scheduling problem

Generating schedules such that all operations are repeated every constant period of time is as important as generating schedules with minimum delays in all cases where a known discipline is desired or obligated by stakeholders. In this paper, a periodic job shop scheduling problem (PJSSP) based on the periodic event scheduling problem (PESP) is presented, which deviates from the cyclic scheduling. The PESP schedules a number of recurring events as such that each pair of event fulfills certain constraints during a given fixed time period. To solve such a hard PJSS problem, we propose a hybrid algorithm, namely PSO-SA, based on particle swarm optimization (PSO) and simulated annealing (SA) algorithms. To evaluate this proposed PSO-SA, we carry out some randomly constructed instances by which the related results are compared with the proposed SA and PSO algorithms as well as a branch-and-bound algorithm. In addition, we compare the results with a hybrid algorithm embedded with electromagnetic-like mechanism and SA. Moreover, three lower bounds (LBs) are studied, and the gap between the found LBs and the best found solutions are reported. The outcomes prove that the proposed hybrid algorithm is an efficient and effective tool to solve the PJSSP.     

多用户MIMO无线通信系统经典调度算法研究

该文研究多用户MIMO下行通信系统的调度问题。下行链路的调度是为了在各个用户之间进行公平的资源分配,并通过获取多用户分集增益来提高系统的容量。因此,用户之间的公平性和系统吞吐量成为衡量一个调度算法性能的重要指标。     

FMS适应调度知识稳定性分析

针对柔性制造系统适应调度知识获取问题,提出了调度知识稳定性的概念,建立了调度知识稳定性判定的必要条件,并用于指导调度知识获取。仿真试验结果表明,根据稳定性判据选择调度知识能使调度系统获得较好的适应性。     

Sensor scheduling for relay-assisted wireless control systems with limited power resources

In this paper, we investigate the transmission scheduling problem for wireless control systems (WCSs) with limited power resources. Different from the existing works, for a discrete-time linear process, we consider a more practical WCS, where a relay is introduced into the framework for remote transmission and control. To achieve the best control performance of the system, we propose a global optimal offline scheduling algorithm. Then, based on ACK-feedback framework, two different online scheduling schemes are further designed respectively under the given power resources. Theoretically, we prove the superiority of online schedule to the offline one under the same energy budget. Simulations are conducted to demonstrate and verify the effectiveness of the proposed algorithms.     

软数控系统混合任务两级调度策略

针对软数控混合任务调度,提出一种两级结构调度策略：对实时周期性任务采用时间触发,实时突发性任务则采用事件触发。建立混合任务模型并定义相关概念,给出调度策略的架构,并对调度算法、任务可调度性及时标间隔进行深入研究。将提出的两级调度策略应用到基于Windows CE.NET的软数控系统中,分析软数控系统结构和任务划分。最后,对调度策略进行试验验证,结果表明该方法能保证混合任务的确定性,从而提高软数控系统的性能。     

A drastic hybrid heuristic algorithm to approach to JIT policy considering controllable processing times

Job scheduling has always been a challenging task in modern manufacturing and the most real life scheduling problems which involves multi-criteria, multi-machine environments. In this research, the single-machine scheduling problem is studied in which job processing times are controllable, namely, they may vary within a specified interval. The goal of this research is to minimize total tardiness and earliness on a single machine, simultaneously. In this context, we first propose a mathematical model for the considered problem and then a net benefit compression–net benefit expansion heuristic is presented for obtaining the set of amounts of compression and expansion of jobs processing times in a given sequence. Two meta-heuristic approaches are then employed to solve medium-to-large-sized problems as local search methods. Thereafter, we apply a hybrid method based on our heuristic as well as these two meta-heuristics in order to obtain solutions with higher quality within lesser computational time. The addressed problem is NP-hard since the single machine total tardiness problem is already NP-hard. The computational results show that our proposed heuristics can effectively solve such Just-In-Time problem with a high-quality solution.     

基于对象模型的不确定环境下服务性机器人系统模型研究

提出了一个基于对象模型的服务性机器人系统模型 ,描述了模型的实现方法 ,并介绍了在动态不确定环境下实现任务规划的一种方法 ,这种任务规划方法模仿了人任务规划方式 ,选择最有可能成功执行的任务执行 ,当环境发生意外变化或目标被改变时 ,它能够作出相应的反应 ,改变任务选择 ,同时这种任务规划方法对实现人机接口特别有用。     

A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS

A carefully designed and efficiently managed material handling system plays an important role in planning and operation of a flexible manufacturing system. Most of the researchers have addressed machine and vehicle scheduling as two independent problems and most of the research has been emphasized only on single objective optimization. Multiobjective problems in scheduling with conflicting objectives are more complex and combinatorial in nature and hardly have a unique solution. This paper addresses multiobjective scheduling problems in a flexible manufacturing environment using evolutionary algorithms. In this paper the authors made an attempt to consider simultaneously the machine and vehicle scheduling aspects in an FMS and addressed the combined problem for the minimization of makespan, mean flow time and mean tardiness objectives.     

A dispatching rule-based hybrid genetic algorithm focusing on non-delay schedules for the job shop scheduling problem

Job shop scheduling is an important decision process in contemporary manufacturing systems. In this paper, we aim at the job shop scheduling problem in which the total weighted tardiness must be minimized. This objective function is relevant for the make-to-order production mode with an emphasis on customer satisfaction. In order to save the computational time, we focus on the set of non-delay schedules and use a genetic algorithm to optimize the set of dispatching rules used for schedule construction. Another advantage of this strategy is that it can be readily applied in a dynamic scheduling environment which must be investigated with simulation. Considering that the rules selected for scheduling previous operations have a direct impact on the optimal rules for scheduling subsequent operations, Bayesian networks are utilized to model the distribution of high-quality solutions in the population and to produce the new generation of individuals. In addition, some selected individuals are further improved by a special local search module based on systematic perturbations to the operation processing times. The superiority of the proposed approach is especially remarkable when the size of the scheduling problem is large.     

面向成本的车间调度优化模型研究

简述了典型调度问题,给出了调度优化的成本目标函数,包括加工成本、设备等待的静态成本以及工件作为在制品的存储成本3个子目标,然后提出了兼顾总加工时间和总成本两个指标的车间调度优化依据。最后,应用所提出的模型对标准调度问题FT06的调度方案进行了评估优化,证明提出的方法是可行的。     

Discrete lot sizing and scheduling problem under batch processing constraints in the semiconductor manufacturing

Highly advanced technology products are manufactured on the series of equipment having various characteristics and requirements. The deposition process of organic light emitting diode, for example, is performed in chambers for a long manufacturing time, while various product devices belonging to the same family can be processed in sequence before the preventive maintenance schedule or the refill of chemical gas required. The deposition process plays as a bottleneck, and its productivity of the schedule is critical to the upstream low-temperature poly silicon process and the downstream encapsulation and module processes as well. The batch family scheduling problem is formulated using the mixed-integer programming (MIP) in consideration of the family setups, lot sizing, supplementary mask tools requirements, and material exhaustion requirements. It is shown that the planning and scheduling decisions can be made simultaneously in an integrated model, and that it can be implemented in the actual manufacturing line. Through the optimized model analysis, the capacity can be enhanced by 20～30 % without losing the throughput and demand satisfaction as well.