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1.
考虑资源置信度的跨企业项目鲁棒性调度算法   总被引:1,自引:1,他引:0  
徐汉川  徐晓飞 《自动化学报》2013,39(12):2176-2185
资源不确定性高和调度鲁棒性要求高是跨企业项目调度问题的重要特征,本文采用资源置信度度量资源的不确定性,建立了考虑资源置信度约束的跨企业项目鲁棒性优化调度模型,设计了路径重连求解算法.算法以路径重连机制搜索解空间,以嵌入 的启发式时间缓冲插入算法快速生成鲁棒性调度,并可通过局部增强搜索算法进一步优化调度的鲁棒性.本文应用项目调度标准问题 库PSPLIB中大量问题实例进行了仿真实验,同两个当前具有代表性的鲁棒性项目调度算法进行了比较,实验结果表明了文中算法的有 效性与优势.  相似文献   

2.
针对柔性生产环境下的车间调度问题,在考虑遗传算法早熟收敛问题和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法结合起来,提出了基于遗传和禁忌搜索的混合动态优化调度算法,并用实例对该算法进行了仿真研究。结果表明,此算法有很好收敛精度,是可行的,并且能够在扰动发生后提供新的调度计划,与传统的调度算法相比较,体现了明显的优越性。  相似文献   

3.
为了保证轧制调度计划的可行性,提高排程的效率,根据热轧生产模式和轧制计划的特点,利用车辆路径问题模型来建模轧制调度问题,并用一种基于离散人工免疫算法的混合优化算法来解决这一问题。该方法利用离散人工免疫算法的全局搜索能力来寻找全局最优解,利用模拟退火方法来避免陷入局部最优.对某钢厂实际生产数据仿真结果表明,所提出的模型和算法对于求解热轧调度问题具有可行性和高效性。  相似文献   

4.
通过举例说明在关键链项目计划中插入输入缓冲后,可能出现资源冲突和紧前关系冲突。从局部和全局两个方面采用分支定界法对项目计划进行重排来解决冲突问题。通过举例说明基于分支定界法的计划重排算法是有效和可行的。通过模拟仿真,从三个不同层次分析项目活动任务的不确定性对项目完工率和项目惩罚成本的影响。结果显示,保留原始关键链的基于分支定界法的全局性关键链计划重排方法较之其他方法要好,不确定性更低,项目的完工率更高,项目的惩罚成本更低。  相似文献   

5.
汽车装配线生产计划与调度的集成优化方法   总被引:1,自引:0,他引:1  
为提高汽车装配线的生产效率,优化资源配置,研究了汽车装配线生产计划和调度的集成优化问题,给出了该问题的混合整数规划模型.利用分枝定界算法和单纯型法求得问题的粗生产计划.通过将模拟退火算法和快速调度仿真相结合,探讨了一种新的启发式算法.然后基于已求得的粗生产计划,针对三种不同寻优组合论述了该算法的实现.将该算法应用于实际算例,仿真结果表明该算法对求解此类问题有着很好的效果.  相似文献   

6.
对于无线传感器网络(WSN)移动基站的调度问题,提出了一种基于线性规划方法的移动单基站调度算法。首先,通过对移动单基站调度问题的形式化描述,对该问题在时间域中进行了数学建模,并使用重建模技术,将问题从时间域转化到空间域以降低求解复杂度,然后基于线性规划理论建立了一个多项式时间复杂度的最优算法。模拟仿真实验验证了该算法的有效性,实验数据表明该移动基站调度算法能有效地延长无线传感器网络的网络生命周期。  相似文献   

7.
对具有不确定时间参数的复杂产品开发项目调度问题,提出一种有效的模糊优化调度算法-基于预测的模糊BoP项目调度算法.首先,用模糊数表示不确定的时间参数,并构造相应的模糊数运算方法,对适合于确定性调度问题的BoP算法进行扩展,使其能处理模糊性时间参数.其次,修正了BoP算法中子项目调度方法,提高了算法的调度性能,降低了计算复杂度.大量的数值仿真实验表明,与基于启发式规则的调度算法相比,模糊BoP算法更适合于具有不确定时间参数的复杂产品开发项目调度问题.  相似文献   

8.
刘涛  刘民  张龙  路深  张亚斌 《控制工程》2005,12(2):104-106
研究了施工项目进度调度问题,提出了一种基于启发式规则和遗传算法的综合智能优化算法,并在施工项目调度问题的描述、带资源约束的施工项目调度问题的分解方法、遗传算法的编码、交叉、变异方法和解码方法等方面进行了研究。不同规模的数值计算结果表明,该算法在解决复杂工程施工项目调度问题上具有良好的性能,并能较好地适用于带时序、资源约束的施工项目调度问题。  相似文献   

9.
在CAN总线组成的网络中,随着传输的信息的增多,固定优先级算法已经不能满足系统在信息传输实时性的需求。文章在分析CAN总线信息模型和常用信息调度算法的基础上,提出了一种能有效提高系统性能的混合调度算法,并引入基于利用率的可调度分析方法对该算法进行了理论上的可调度分析。最后,进行了基于LPC2129的仿真平台进行了仿真实验,结果显示了该算法的有效性。  相似文献   

10.
基于混沌粒子群算法的项目调度干扰问题研究   总被引:1,自引:0,他引:1  
针对资源受限项目调度问题中的干扰情况进行了界定, 面向几种干扰问题建立了相应的资源受限项目调度干扰模型和混沌粒子群求解算法, 对项目网络图干扰、任务干扰和资源干扰三种干扰问题进行仿真计算, 验证了算法和模型的有效性, 为决策者在干扰事件发生后及时对原最优调度计划作出调整给出了方向。  相似文献   

11.
当前市场环境快速变化且竞争愈发激烈,通过有效控制不确定性以避免进度延迟是项目管理者极为重视的方面.关键链缓冲管理提供了一种应对项目进度风险的有效方法,针对现有缓冲监控机制存在的忽视活动层面不确定信息以及项目动态执行特征的不足,提出一种基于统计过程控制的两阶段统计缓冲监控方法(SBMA).第1阶段通过在活动层面设置管理者认为可以接受的活动工期波动范围,生成项目层面基于缓冲消耗指标的容忍阈值,建立统计缓冲控制图;第2阶段依据假设检验理论,当缓冲消耗指标超出缓冲控制图中监控阈值时即触发相应行动.模拟实验结果表明,相对于已有研究中的其他3种缓冲监控方法,SBMA可以更准确及时地给出项目整体进度预警信息,并且可以更有效地追溯至活动层面识别出活动工期是否发生异常波动,从而验证所提出方法的实用性和优越性.  相似文献   

12.
The scheduling of software development projects is a central, non-trivial and costly task for software companies. This task is not exempt of erroneous decisions caused by human limitations inherent to project managers. In this paper, we propose a knowledge-based evolutionary approach with the aim of assisting to project managers at the early stage of scheduling software projects. Given a software project to be scheduled, the approach automatically designs feasible schedules for the project, and evaluates each designed schedule according to an optimization objective that is priority for managers at the mentioned stage. Our objective is to assign the most effective set of employees to each project activity. For this reason, the evaluation of designed schedules in our approach is developed based on available knowledge about the competence of the employees involved in each schedule. This knowledge arises from historical information about the participation of the employees in already executed projects. In order to evaluate the performance of our evolutionary approach, we present computational experiments developed over eight different sets of problem instances. The obtained results are promising since this approach has reached an optimal level of effectivity on seven of the eight mentioned sets, and a high level of effectivity on the remaining set.  相似文献   

13.
This paper describes new heuristic reactive project scheduling procedures that may be used to repair resource-constrained project baseline schedules that suffer from multiple activity duration disruptions during project execution. The objective is to minimize the deviations between the baseline schedule and the schedule that is actually realized.We discuss computational results obtained with priority-rule based schedule generation schemes, a sampling approach and a weighted-earliness tardiness heuristic on a set of randomly generated project instances.  相似文献   

14.
The problem of simultaneous LQG control and scheduling of a Networked Control System (NCS) with constant network induced delays at input and output and bandwidth limitations is investigated. Delays are considered at plant as well as controller side. Sufficient conditions for controllability, stabilizability, reconstructibility and detectability of the underlying networked control system are drawn. The proposed conditions extend previous works on structural properties of NCS by capturing both plant and controller side delays together with bandwidth limitations. A framework for computing the optimal LQG controller for the NCS with a fixed scheduling is provided. The proposed modeling approach facilitates use of LQG as well as other control methods for NCSs with delays and bandwidth limitations. In order to optimize performance, a semi-online scheduling procedure is proposed based on an offline look up table. The look up table assigns an optimal schedule with associated optimal LQG controller to initial conditions. The proposed scheme improves previous results by online deployment of schedule and LQG control with stability guarantees and very low computational overhead. A simulation example with communication delays, packet losses and bandwidth limitations in both sensor and actuator sides is included. Static optimal periodic communication sequence, Optimal Pointer Placement (OPP) approach proposed in previous works, a random access scheduling method representing contention based access policies and the proposed method are simulated and compared.   相似文献   

15.
We address a recently introduced static dataflow model: the Static Dataflow with Access Patterns (SDF-AP) model. For this model we present (1) a generalization of an existing regular periodic scheduling scheme to regular 1-periodic scheduling for flexibility to achieve a smaller schedule period and additional room for optimization on communication storage; (2) a method based on Integer Linear Programming (ILP) to minimize communication buffers under periodic scheduling and user-specified throughput constraints. Experimental results on a set of test cases show that buffer sizes using this approach can be reduced dramatically when compared to the traditional SDF models. The optimal sizing result may serve as an important criterion to evaluate and fine-tune any heuristics-based buffer sizing approach for the SDF-AP model of computation.  相似文献   

16.
A genetic algorithm for multiprocessor scheduling   总被引:6,自引:0,他引:6  
The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can be minimized. This scheduling problem is known to be NP-hard, and methods based on heuristic search have been proposed to obtain optimal and suboptimal solutions. Genetic algorithms have recently received much attention as a class of robust stochastic search algorithms for various optimization problems. In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. The representation of the search node is based on the order of the tasks being executed in each individual processor. The genetic operator proposed is based on the precedence relations between the tasks in the task graph. Simulation results comparing the proposed genetic algorithm, the list scheduling algorithm, and the optimal schedule using random task graphs, and a robot inverse dynamics computational task graph are presented  相似文献   

17.
Manufacturing job shop scheduling is a notoriously difficult problem that lends itself to various approaches - from optimal algorithms to suboptimal heuristics. We combined popular heuristic job shop-scheduling approaches with emerging AI techniques to create a dynamic and responsive scheduler. We fashioned our job shop scheduler's architecture around recent holonic manufacturing systems architectures and implemented our system using multiagent systems. Our scheduling approach is based on evolutionary algorithms but differs from common approaches by evolving the scheduler rather than the schedule. A holonic, multiagent systems approach to manufacturing job shop scheduling evolves the schedule creation rules rather than the schedule itself. The authors test their approach using a benchmark agent-based scheduling problem and compare performance results with other heuristic-scheduling approaches.  相似文献   

18.
A bi-objective optimisation using a compromise programming approach is proposed for installation scheduling of an offshore wind farm. As the installation cost and the completion period of the installation are important aspects in the construction of an offshore wind farm, the proposed method is used to deal with those conflicting objectives. We develop a mathematical model using integer linear programming (ILP) to determine the optimal installation schedule considering several constraints such as weather condition and the availability of vessels. We suggest two approaches to deal with the multi-objective installation scheduling problem, namely compromise programming with exact method and with metaheuristic techniques. In the exact method the problem is solved by CPLEX whereas in the metaheuristic approach we propose Variable Neighbourhood Search (VNS) and Simulated Annealing (SA). Moreover, greedy algorithms and a local search for solving the scheduling problem are introduced. Two generated datasets are used for testing our approaches. The computational experiments show that the proposed metaheuristic approaches produce interesting results as the optimal solution for some cases is obtained.  相似文献   

19.
This paper applies interval number theory to production scheduling for its advantage in uncertainty modeling. A job shop scheduling problem with interval processing time is first described and then a population-based neighborhood search (PNS) is presented to optimize the interval makespan of the problem. In PNS, an ordered operation-based representation is used and a decoding procedure is constructed by using operations of interval numbers, in which there are no approximate treatments. It is proved that the possible actual makespan of each schedule are contained in its interval makespan. A swap operation and binary tournament selection are applied to update the population. PNS is finally tested by using some instances and computational results show that PNS can provide better results than some methods from the literature.  相似文献   

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