有交货期的Job-shop调度问题的改进的倒排算法

在提出的以“加工效率”为主要参数,并引入“虚工序”、“虚机器”求解有交货期的单件车间调度问题的逆序算法的基础上,进一步改进了该算法,并在VBAForProject中实现了该算法.经分析实例验证,该算法具有一定的优度,并且在工程中是可行的。     

解特殊工艺约束拖后调度问题的并行遗传算法

非等同拖后调度问题作为家纺企业的车间调度问题重要组成部分,有着独特的特点,一方面生产设备非等同,另一方面受特殊工艺的约束。针对该问题的特点,设计了一个基于向量编码的遗传算法。此算法编码方法简单,能有效地反映实际调度方案,并能保证满足约束条件,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。     

求解动态车辆调度问题的混合禁忌搜索算法

对带时间窗的动态车辆调度问题进行分析,引入虚拟点和时间轴概念,建立基于时间轴的动态车辆调度模型,并提出基于C-W节约法和禁忌搜索的混合禁忌搜索算法进行求解.算法中使用动态方法构造候选解和动态禁忌长度的选取策略来提高算法的收敛速度,最后通过测试实例验证了该混合算法解决动态车辆调度问题的有效性和可行性.     

Heuristic Method for Job Shop Scheduling Problem with Combination Processing

A job shop scheduling problem with a combination processing in complex production environment is proposed. Based on the defining of non-elastic combination processing relativity and virtual process,the problem can be simplified and transformed to a traditional one. On the basis of the dispatching rules select engine and considered factors of complex production environment,a heuristic method is designed. The algorithm has been applied to a mould enterprise in Shenzhen for half a year. The practice showed tha...     

工序间存在零等待约束的复杂产品调度研究

针对实际装配生产中工序之间存在零等待约束的复杂产品的调度问题, 提出了一种把存在零等待约束的工序虚拟成一个工序的方法. 该方法在提出复杂产品、标准工序、虚拟工序、零等待和扩展加工工艺树的概念基础上, 对扩展加工工艺树中的标准工序采用拟关键路径法和最佳适应调度的车间调度算法进行调度, 对虚拟工序采用移动交换算法在相应设备上分离调度, 将存在零等待约束的调度问题转化为存在虚拟工序的无零等待约束的调度问题. 实例表明, 所提出的调度算法能够较好地解决具有实际意义的工序间存在零等待约束的复杂产品的调度问题, 且易于实现.     

Edge computing-based real-time scheduling for digital twin flexible job shop with variable time window

Production scheduling is the central link between enterprise production and operation management and is also the key to realising efficient, high-quality and sustainable production. However, in real-world manufacturing, the frequent occurrence of abnormal disturbance leads to the deviation of scheduling, which affects the accuracy and reliability of scheduling execution. The traditional dynamic scheduling methods (TDSMs) cannot solve this problem effectively. This paper presents a real-time digital twin flexible job shop scheduling (R-DTFJSS) method with edge computing to address the issue. Firstly, an overall framework of R-DTFJSS is proposed to realise real-time scheduling (RS) through real-time interaction between physical workshop (PW) and virtual workshop (VW). Secondly, the implementation process of R-DTFJSS is designed to realise real-time operation allocation. Then, to obtain the optimal RS result, an improved Hungarian algorithm (IHA) is adopted. Finally, a case simulation from an industrial case of a cooperative enterprise is described and analysed to verify the effectiveness of the proposed R-DTFJSS method. The results show that compared with the TDSMs, the R-DTFJSS method can effectively deal with unexpected and frequent abnormal disturbances in the production process.     

基于多智能体的虚拟企业群协同生产调度模式研究

为了实现虚拟企业群的协同生产调度, 提高企业群整体运作水平, 建立了基于多智能体的虚拟企业群制造企业生产调度模型, 分析了生产计划调度及信息交互作用。在企业协同层, 提出了基于多目标和价格协商的供应商选择过程, 确定了合作伙伴的初选过程方法, 企业层采用了基于EDD+SCR规则组合确定企业任务分配, 并为所有订单任务确定交货期, 各车间层则根据企业层的生产计划进行车间生产调度安排。最后, 运用Swarm仿真软件平台对案例进行仿真分析, 验证了提出的生产调度模式可以有效提高各企业的车间负载率, 并使车间负载率在短时间内达到稳定状态, 从而合理有效地利用企业资源。     

Scheduling parallel Kalman filters with quantized deadlines

In this paper we explore the problem of scheduling parallel processes of Kalman filters to meet individual estimation error requirements. It is assumed that at each time-step measurements of only one process are received. We define real-time deadlines of transmissions and convert the problem into arranging sequence of tasks with corresponding deadlines. To reduce computations, cycles of transmissions are calculated and virtual processes are introduced into scheduling. A sliding window method is then designed to adjust the processes against real-time disturbances in applications. Compared with algorithms proposed in Lin and Wang (2013), the proposed algorithm is able to schedule a feasible sequence adaptively within a short scheduling window and requires little computation.     

能耗感知下云资源三支粒度调度策略研究

云资源调度是云数据中心的一种重要节能方式。然而,实际云平台中,受单一物理机资源限制,存在虚拟机资源竞争和利用率低的问题。对此,通过分析虚拟机负载相似性及资源占有度问题,提出一种基于三支决策的能耗感知虚拟机迁移策略。首先,在虚拟机迁移过程中,设计云资源的三支划分策略,并使用K-means算法在划分区域选择待迁移的虚拟机序列;其次,依据虚拟机与物理机的负载相似度,获取虚拟机放置顺序;最后,依托CloudSimPlus云仿真平台验证了所提方法的有效性。实验结果表明,所提方法能够有效降低云能耗,实现资源充分利用。     

制造执行系统的生产管理控制研究

车间调度是制造执行系统生产管理控制的核心功能,因此制造执行系统必须实现车间的优化调度。文章分析了制造执行系统的生产管理控制方法,提出了制造执行系统的车间调度方法。一个已在某航天制造企业使用的制造执行系统软件证实了制造执行系统的生产管理控制方法的正确性。     

考虑缓冲区的自动生产单元的无死锁调度策略

在制造系统中,必须防止死锁的发生.本文提出了一种在制造系统(带有有限缓冲区)中搜索最优的无死锁调度的算法.为此首先介绍了死锁问题及其图论表示方法,然后在遗传算法的基础上,运用图论算法来保证无死锁的调度结果.为了保证遗传算法生成的调度策略能够满足所要求的约束,运用图论方法选择无死锁个体,或添加缓冲区,从而在基本保证了系统的主要性能指标的同时,得到系统可行的无死锁调度结果.最后给出了一个运用此方法解决死锁问题的实例.     

Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms

In contrast to traditional job-shop scheduling problems, various complex constraints must be considered in distributed manufacturing environments; therefore, developing a novel scheduling solution is necessary. This paper proposes a hybrid genetic algorithm (HGA) for solving the distributed and flexible job-shop scheduling problem (DFJSP). Compared with previous studies on HGAs, the HGA approach proposed in this study uses the Taguchi method to optimize the parameters of a genetic algorithm (GA). Furthermore, a novel encoding mechanism is proposed to solve invalid job assignments, where a GA is employed to solve complex flexible job-shop scheduling problems (FJSPs). In addition, various crossover and mutation operators are adopted for increasing the probability of finding the optimal solution and diversity of chromosomes and for refining a makespan solution. To evaluate the performance of the proposed approach, three classic DFJSP benchmarks and three virtual DFJSPs were adapted from classical FJSP benchmarks. The experimental results indicate that the proposed approach is considerably robust, outperforming previous algorithms after 50 runs.     

面向应用的虚拟桌面网格架构

针对桌面网格中出现的应用部署难、作业结果差异大、系统可扩展性差等问题,基于虚拟化技术提出一种桌面网格架构。利用虚化技术的隔离性和封装性,通过面向应用的作业调度策略以及有限生命周期的虚拟机控制方式,实现一个易于扩展且易于部署的桌面网格架构。分析及测试结果表明,该架构适用于大规模应用场景,架构中所采用的面向应用的调度策略以及虚拟机控制方式是有效可行的。     

基于Hopfield神经网络的作业车间生产调度方法

该文提出了基于Hopfield神经网络的作业车间生产调度的新方法.文中给出了作业车 间生产调度问题(JSP)的约束条件及其换位矩阵表示,提出了新的包括所有约束条件的计算能 量函数表达式,得到相应的作业车间调度问题的Hopfield神经网络结构与权值解析表达式,并 提出相应的Hopfield神经网络作业车间调度方法.为了避免Hopfield神经网络容易收敛到局部 极小,从而产生非法调度解的缺点,将模拟退火算法应用于Hopfield神经网络求解,使Hopfield 神经网络收敛到计算能量函数的最小值0,从而保证神经网络输出是一个可行调度方案.该文 改进了已有文献中提出的作业调度问题的Hopfield神经网络方法,与已有算法相比,能够保证 神经网络稳态输出为可行的作业车间调度方案.     

Selection of partners in virtual enterprise paradigm

Agility is the competitive advantage in the global manufacturing environment. It is believed that the agility can be realized by dynamically reconfigurable virtual enterprise. However, the configuration of virtual enterprises is a challenge to us. In selecting the partners for the reconfiguration of virtual enterprise, many factors should be taken into consideration. However, the manufacturing cost and time to market are the most important factors. In this paper, in considering the completion time as a constraint we model the partner selection problem by an integer programming formulation to minimize the manufacturing cost. The formulation is then transformed into a graph-theoretical formulation and a 2-phase algorithm is developed to solve the problem. In the first phase, a polynomial bounded algorithm is proposed to find the earliest completion time, so we obtained a feasible solution. In phase 2, we improve the solution by exchanging the candidate partners in keeping the solution in the feasible region. Although we may not find the optimal solution for the problem by the improvement algorithm, the objective is reduced iteration by iteration. Thus, the algorithm is efficient and can be applicable to practical problems. An illustrative example is presented to show the application of the proposed algorithm.     

A decomposition approach to non-preemptive real-time scheduling

Consider the problem of scheduling a set ofn tasks on a uniprocessor such that a feasible schedule that satisfies each task's time constraints is generated. Traditionally, researchers have looked at all the tasks as a group and applied heuristic or enumeration search to it. We propose a new approach called thedecomposition scheduling where tasks are decomposed into a sequence of subsets. The subsets are scheduled independently, in the order of the sequence. It is proved that a feasible schedule can be generated as long as one exists for the tasks. In addition, the overall scheduling cost is reduced to the sum of the scheduling costs of the tasks in each subset.Simulation experiments were conducted to analyze the performance of decomposition scheduling approach. The results show that in many cases decomposition scheduling performs better than the traditional branch-and-bound algorithms in terms of scheduling cost, and heuristic algorithms in terms of percentage of finding feasible schedules over randomly-generated task sets.     

A hybrid method for hydroelectric generation scheduling using an artificial neural network

This paper presents a decomposition method for finding an optimal operating policy of interconnected hydroelectric power plants using an artificial neural network. The coupling constraints on reservoir storage at the end of the planning horizon are relaxed using coordinating multipliers that result in interval wise decomposition of the overall problem. Resulting subproblems are solved sequentially, which reduces the complexity of the problem. Each subproblem is solved using a two-phase neural network approach. An efficient heuristic algorithm is developed to find the feasible solution. A case study considering scheduling of the Bhakra-Beas reservoir system is also presented in this paper. The new method demonstrates the potential of achieving an improved performance.     

A bilevel decomposition algorithm for simultaneous production scheduling and conflict-free routing for automated guided vehicles

We address a bilevel decomposition algorithm for solving the simultaneous scheduling and conflict-free routing problems for automated guided vehicles. The overall objective is to minimize the total weighted tardiness of the set of jobs related to these tasks. A mixed integer formulation is decomposed into two levels: the upper level master problem of task assignment and scheduling; and the lower level routing subproblem. The master problem is solved by using Lagrangian relaxation and a lower bound is obtained. Either the solution turns out to be feasible for the lower level or a feasible solution for the problem is constructed, and an upper bound is obtained. If the convergence is not satisfied, cuts are generated to exclude previous feasible solutions before solving the master problem again. Two types of cuts are proposed to reduce the duality gap. The effectiveness of the proposed method is investigated from computational experiments.     

Parallel desynchronized block matching: A feasible scheduling algorithm for the input-buffered wavelength-routed switch

The input-buffered wavelength-routed (IBWR) switch is a promising switching architecture for slotted optical packet switching (OPS) networks. The benefits of the IBWR fabric are a better scalability and lower hardware cost, when compared to output buffered OPS proposals. A previous work characterized the scheduling problem of this architecture as a type of matching problem in bipartite graphs. This characterization establishes an interesting relation between the IBWR scheduling and the scheduling of electronic virtual output queuing switches. In this paper, this relation is further explored, for the design of feasible IBWR scheduling algorithms, in terms of hardware implementation and execution time. As a result, the parallel desynchronized block matching (PDBM) algorithm is proposed. The evaluation results presented reveal that IBWR switch performance using the PDBM algorithm is close to the performance bound given by OPS output buffered architectures. The performance gap is especially small for dense wavelength division multiplexing (DWDM) architectures.     

云计算中基于NSGA Ⅱ的虚拟资源调度算法

云计算环境下将物理资源抽象为同一的虚拟资源,如何将虚拟资源调度到物理资源上是云计算中一个基本且复杂的问题.对虚拟资源的调度进行建模并证明其难解性,将该模型的求解转化以系统负载均衡为优化目标的多目标优化问题,提出采用改进的基于非支配排序的遗传算法(NSGA Ⅱ)来求解该问题.与针对具体环境的调度算法相比,抽象的模型更能代表典型的云计算环境中的虚拟资源调度问题.对提出模型进行了仿真,实验结果表明了该模型的有效性和NSGA Ⅱ算法求解该问题的可行性,同时对比随机算法、静态算法和排序匹配调度算法,NSGA Ⅱ算法优于其他算法.