基于关键链的资源受限项目调度新方法

针对资源受限项目调度问题（RCPSPs）的实际需求建立了多目标优化调度模型,综合运用现有研究成果,设计了基于关键链的项目调度方法。该方法首先采用基于优先规则的启发式算法生成工期最小的近优项目计划,再在该计划中嵌入输入缓冲和项目缓冲,保证项目计划在非确定环境下的稳定执行。论文引用RCPSPs的标准问题库PSPLIB中大量案例对算法进行了的仿真试验,结果表明本文方法较传统项目调度方法有很大改进,论文最后对仿真结果进行了深入讨论,并指出了未来的研究方向。     

一种动态并行I／O调度新策略的研究

并行I／O已经成为提高分布式计算系统性能的一种重要方式，论文在研究集群计算系统并行I／O数据调度策略的基础上，提出了一种应用于分布式计算系统中的二次调度自主维护负载平衡的动态I／O调度算法（DIO_TSMB），实验结果表明了算法的有效性，最后分析了并行I／O数据调度的发展趋势。     

基于改进型批决策与调度建模的生产计划调度系统的研究

热轧型钢生产工艺复杂,其生产中极易出现由于计划调度安排不当而产生的交货期延误、库存超负荷等问题。针对以上问题研究设计了MES生产计划调度系统,改进了批决策调度策略用于数学建模,利用自适应遗传算法求解生产调度计划。以此为基础,为某热轧企业设计实现了生产计划调度系统,并通过真实的热轧型钢订单、原料、设备等数据,对模型改进前后的计划编制方法进行模拟与比较,验证了利用该改进型批决策与调度模型编制的热轧型钢生产调度计划可节省生产时间、降低设备调度时间,以此来指导热轧型钢的生产可切实减少交货延误和减少库存占用率,并提高企业利润率。     

一种推拉结合的生产计划与调度模型*

通过分析混合流程企业生产计划管理的实际需求特点,提出生产工艺流程图和一种推拉结合的生产计划与调度模型.模型以订单、库存和预投为综合需求起点,以拉式补货的方式制订有限产能的主生产计划,依据工艺流程图分解生成可行的流程作业计划和离散作业计划,以及车间级物料需求计划等.在生产执行中则结合工序流转卡等方式进行推式生产,以保证生...     

汽车装配线生产计划与调度的集成优化方法

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.     

柔性制造单元一种多层计划调度方法

柔性制造单元的计划与调度问题已有许多的讨论,但大都针对具体的系统以及对任务做某些假设,本文提出一种多层计划和调度的方法并对每层的功能进行讨论,这种方法的结构可应用于各类单元.同时提出三种机器资源的分配方法.文中给出了仿真结果表明此方法是比较有效的.     

一种炼钢连铸调度计划的综合模糊评价方法

炼钢连铸生产调度是冶金企业生产管理的核心内容,在分析和研究了冶金企业炼钢连铸调度计划的评价指标、评价因素和表示方法等问题的基础上,提出了调度计划评价的三级模糊综合评价体系。仿真结果表明,该方法可以公平合理地获得评价结果,在实际应用中也切实可行。采用模糊综合评价方法,进行现场实际数据模拟计算,获得指导编制调度计划的满意结果。     

一种需求变更驱动的软件项目人力资源再调度方法

需求开发和维护是软件生产的基础,是软件开发的重要部分。需求变更是软件项目中最常见、影响最大的风险之一,会对软件项目的成本、进度和软件产品的质量产生不同程度的影响。提出一种需求变更驱动的软件项目人力资源再调度方法。该方法通过建立需求变更模型来分析需求变更对软件项目计划的影响,并在此基础上使用人力资源调度算法来获得满足时间、成本双重约束的人力资源分配方案,为软件项目管理人员提供决策参考,从而提高软件企业的风险管理能力和资源利用率。最后,通过一个示例说明了该方法的可行性和有效性。     

一种新的混合生产调度问题的优化求解

研究了一种新的生产调度问题的优化问题,针对这种新的调度模式,设计了一种两层遗传算法进行优化求解。算法采用了上下两层共同进化,上层遗传算法优化产品生产过程中每类产品对应每台设备所生产的物料类型的分配,下层遗传退火算法优化了所有产品子批量的一个排序。在算法的求解过程中,引入了针对该问题的一个新的批量加工时间计算方法来求得最大完成时间指标的值。最后通过系统仿真论证了算法以及批量完工时间计算的有效性。     

一种分布式安全评估通信与调度模型

抽象并形式化分布式通信与调度基本概念,给出任务T与sensor的关系分析,指出调度存在4种约束。为提高控制中心性能和最小化任务分发与执行时间,提出基于任务复制的动态指定通信与调度模型。给出模型中Cost、行政村、DS选举、选票和通信规则的设计及其规则。时间复杂度分析与实验验证,该模型对分布式安全评估通信与调度的运行效率有很明显的效果,是一种比较高效的通信与调度模型。     

Applying fuzzy method for measuring criticality in project network

Program evaluation and review technique (PERT) is widely used as a tool for managing large-scale projects. In the traditional PERT model, the durations of activities (tasks) are either represented as crisp numbers or drawn from the beta distribution to estimate the task durations such as pessimistic, most likely and optimistic times. However, the operation time for each activity is usually difficult to define and estimate precisely in a real situation. The aim of this paper is to present an analytical method for measuring the criticality in a project network with fuzzy activity times. Triangular fuzzy numbers are used to express the operation times for all activities in a project network. A new model that combines fuzzy set theory with the PERT technique is proposed to determine the critical degrees of activities (tasks) and paths. In the proposed model, a possibility index is defined to identify the likelihood of meeting a specified required time for a project network. At the end of the paper, an example is presented to compare with those obtained using the proposed method as well as other methods. The comparisons reveal that the method proposed in this paper is more effective in determining the activity criticalities and finding the critical path.     

基于混沌粒子群算法的关键链项目进度管理研究*

应用混沌粒子群算法解决关键链项目管理的优化调度问题,并利用了混沌运动遍历性,随机性等特点,对传统粒子群算法进行改进,摆脱了粒子群算法后期易陷入局部极值点的缺点,并保持了前期搜索的快速性。通过实例证明,在求解该类问题上,混沌粒子群算法比基本粒子群算法更具有优势。     

Time-constrained project scheduling

We propose a new approach for scheduling with strict deadlines and apply this approach to the Time-Constrained Project Scheduling Problem (TCPSP). To be able to meet these deadlines, it is possible to work in overtime or hire additional capacity in regular time or overtime. For this problem, we develop a two stage heuristic. The key of the approach lies in the first stage in which we construct partial schedules. In these partial schedules, jobs may be scheduled for a shorter duration than required. The second stage uses an ILP formulation of the problem to turn a partial schedule into a feasible schedule, and to perform a neighborhood search. The developed heuristic is quite flexible and, therefore, suitable for practice. We present experimental results on modified RCPSP benchmark instances. The two stage heuristic solves many instances to optimality, and if we substantially decrease the deadline, the rise in cost is only small.     

An efficient hybrid algorithm for resource-constrained project scheduling

We propose an efficient hybrid algorithm, known as ACOSS, for solving resource-constrained project scheduling problems (RCPSP) in real-time. The ACOSS algorithm combines a local search strategy, ant colony optimization (ACO), and a scatter search (SS) in an iterative process. In this process, ACO first searches the solution space and generates activity lists to provide the initial population for the SS algorithm. Then, the SS algorithm builds a reference set from the pheromone trails of the ACO, and improves these to obtain better solutions. Thereafter, the ACO uses the improved solutions to update the pheromone set. Finally in this iteration, the ACO searches the solution set using the new pheromone trails after the SS has terminated. In ACOSS, ACO and the SS share the solution space for efficient exchange of the solution set. The ACOSS algorithm is compared with state-of-the-art algorithms using a set of standard problems available in the literature. The experimental results validate the efficiency of the proposed algorithm.     

An intelligent search method for project scheduling problems

We develop an approach for implementing a real time admissible heuristic search algorithm for solving project scheduling problems with resource constraints. This algorithm is characterized by the complete heuristic learning process: state selection, heuristic learning, and search path review. The implementation approach is based on the network structure and the activity status of a project; which consists of definition of states, state transition operator, heuristic estimation, and state transition cost. The performance analysis with a benchmark problem shows that, the accumulation of heuristic learning during the search process leads to the re-scheduling of more promising activities, and finds an optimal schedule efficiently.     

Event-based MILP models for resource-constrained project scheduling problems

In this paper we make a comparative study of several mixed integer linear programming (MILP) formulations for resource-constrained project scheduling problems (RCPSPs).     

Multi-objective optimization model for multi-project scheduling on critical chain

In this paper, a multi-project scheduling in critical chain problem is addressed. This problem considers the influence of uncertainty factors and different objectives to achieve completion rate on time of the whole projects. This paper introduces a multi-objective optimization model for multi-project scheduling on critical chain, which takes into consideration multi-objective, such as overall duration, financing costs and whole robustness. The proposed model can be used to generate alternative schedules based on the relative magnitude and importance of different objectives. To respond to this need, a cloud genetic algorithm is proposed. This algorithm using randomness and stability of Normal Cloud Model, cloud genetic algorithm was designed to generate priority of multi-project scheduling activities and obtain plan of multi-project scheduling on critical chain. The performance comparison shows that the cloud genetic algorithm significantly outperforms the previous multi-objective algorithm.     

关键链项目调度方法研究评述

客观地评述了关键链方法的优缺点。首先,简要阐述了关键链与关键路径法/计划评审技术及资源约束型项目调度问题的异同点;然后,从关键链调度优化模型、缓冲机制和关键链的应用3方面梳理了已有的研究成果,相应地,从两个方面总结出目前关键链方法中的欠缺;最后,从多目标优化、动态关键链、与鲁棒性结合和与其他理论融合4个角度指出了关键链方法将来的研究趋势。评述工作将会对关键链的理论研究和实践应用提供一些思维启发。     

基于分支定界法的关键链项目计划重排*

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。