基于云模型的网络计划建模方法

项目建设过程中的不确定性因素对预测项目完工时间有较大影响,项目中的不确定性主要是随机性和模糊性.将云模型以及云运算引入到网络计划建模方法中,提出基于云模型的网络计划建模方法,可以将随机性和模糊性结合起来处理;这种基于云模型的网络计划建模方法,比传统的方法更真实地反映了项目的不确定性,能够提供更多有价值的信息.使用基于云模型的网络计划建模方法来计算整个项目完工的时间,充分考虑了项目建设过程中的不确定性,更加符合工程实际.     

基于关键链的软件项目进度风险管理

文中讨论基于关键链技术的软件项目进度风险管理方法。基于软件过程工作分解结构,预测各项工作在理想工作条件下的工期,考虑人力资源的约束与冲突,建立项目的关键链。通过对各项工作的风险分析,为关键链、非关键链分别设置项目缓冲、输入缓冲,通过对缓冲区的监控来进行风险的控制和管理。     

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。     

基于蒙特卡洛仿真的项目完工成本概率预测

在项目完工成本概率预测问题的研究中,传统挣值分析法中对项目完工成本的预测没有考虑到项目成本的不确定性,预测结果均为单一值.为解决上述问题,根据挣值分析法原理和项目各活动成本分布的特征,结合项目实施过程中的活动成本与进度绩效信息,给出活动完工成本的三点估计,即最乐观值、最可能值和最悲观值,并提出基于蒙特卡洛仿真的项目完工成本概率预测方法.上述方法综合考虑了项目成本的不确性,给出了预测结果的区间范围和概率分布.案例分析表明改进方法的预测结果符合项目成本的随机性特征,且预测精确度较高.     

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。     

基于前摄策略的项目调度优化方法研究*

为了解决不确定性因素对项目调度造成的扰动,在综合考虑项目资源紧张度、网络图结构复杂度等因素影响的前提下,对关键链和非关键链分别添加适当时间缓冲,减小了不确定性因素带来的扰动,提高了项目调度的健壮性。在此基础上提出以在制品水平和净成本最低为目标的项目调度优化方法;最后通过大量仿真验证,结果表明该方法优于文献中的项目调度方法。     

基于活动工期风险和资源约束风险的缓冲大小计算方法

提出一种不确定情况下考虑活动工期风险和多资源约束风险的缓冲大小计算方法. 首先, 运用贝叶斯网络技术分析关键风险因素, 评估其造成的活动工期风险; 其次, 通过资源流网络方法衡量资源约束风险, 进而提出合理的缓冲配置方法以构建稳定的关键链调度计划. 通过算例分析和比较研究, 验证了所提出方法兼具有效性和实用性, 能够在保证较高按时完工率的同时, 有效缩短项目工期并保持进度计划稳定.

     

基于关键链技术的项目管理软件研究*

关键链技术（CCM）的出现给项目管理软件研究带来新的挑战。当今国际主流项目管理软件多采纳关键路径法（CPM）,伴随项目管理的深入和发展,这类软件难以适应实际需要。从理论和应用角度分析比较了基于关键路径和关键链的项目管理软件,指出关键链项目管理方法（CCPM）引入主流项目管理软件中的必然趋势。     

关键链技术研究与基于关键链的项目管理系统

关键链(Critical Chain)是Eli Goldratt博士提出的一种基于约束理论(Theory of Constraints)的项目管理方法。自1997年被提出以来,基于关键链的项目管理技术(CCPM)受到日益广泛的关注。通过介绍关键链技术的原理和运用,实现关键链技术的核心问题——多资源约束下的进度编排问题(MRCPSP,Multi-Resources Constrained Project Scheduling Problem)和用来实现MRCPSP的启发式算法,阐述了一个基于关键链的项目管理系统的设计与实现。     

软计算求解分布式多工厂单件制造业的提前/拖期生产计划问题

描述了分布式多工厂单件制造企业准时化生产计划问题, 以实现最小化提前/拖期惩罚费用、生产成本、产品运输费用之和为目标建立了0-1规划数学模型; 设计了基于模糊规则量化的方法求解模糊决策, 并将模糊决策嵌入到遗传算法中的软计算方法求解模型, 使得算法具有比分枝定界法更快速的寻找优解的能力以及更广泛的适应范围. 结果表明了该模型和算法的有效性和应用潜力.     

On the identical parallel-machine rescheduling with job rework disruption

This paper investigates an issue of rescheduling on identical parallel machines where the original jobs have already been scheduled to minimize the total completion time, when a single set of jobs to be reworked re-arrives and creates a job rework disruption. Two conflicting rescheduling criteria are considered: the total completion time, as the measure of scheduling cost (efficiency); and the number of jobs assigned to different machines in the original schedule and newly generated schedule, as the measure of disruption cost (stability). Further, the rescheduling problem is defined as a bi-criteria scheduling problem. Two polynomial time algorithms are proposed to lexicographically optimize the two criteria. Besides, the set of all efficient schedules with respect to the two criteria can be also generated in polynomial time.     

基于损益云模型的制造车间重调度决策方法

针对制造车间重调度触发机制问题,建立了制造车间重调度损益函数,揭示了生产车间重调度过程损失及增益的变化规律.引入云理论测度重调度损益的不确定性,使用逆向云算法计算重调度增益云和损失云的数字特征,根据云形态预测重调度损益变化趋势.提出一种基于损益云模型的重调度决策方法以判断是否需要重调度,并利用最佳损益比甄选预调度方案以兼顾生产系统的稳定性和有效性.最后,通过实例验证了该方法的合理性和实用性.     

Structure and Simulation Evaluation of an Integrated Real-Time Rescheduling System for Railway Networks

A critical problem faced by railways is how to increase capacity without investing heavily in infrastructure and impacting on schedule reliability. One way of increasing capacity is to reduce the buffer time added to timetables. Buffer time is used to reduce the impact of train delays on overall network reliability. While reducing buffer times can increase capacity, it also means that small delays to a single train can propagate quickly through the system causing knock-on delays to trains impacted by the delayed train. The Swiss Federal Railways (SBB) and Swiss Federal Institute of Technology (ETH) are researching a new approach for real-time train rescheduling that could enable buffer times to be reduced without impacting schedule reliability. This approach is based on the idea that if trains can be efficiently rescheduled to address delays, then less buffer time is needed to maintain the same level of system schedule reliability. The proposed approach combines a rescheduling algorithm with very accurate train operations (using a driver-machine interface). This paper describes the proposed approach, some system characteristics that improve its efficiency, and results of a microscopic simulation completed to help show the effectiveness of this new approach. The results demonstrate that the proposed integrated real-time rescheduling system enables capacity to be increased and may reduce knock-on delays. The results also clearly showed the importance of accurate train operations on the rescheduling system’s effectiveness.     

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.     

Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops

In actual manufacturing processes, some unexpected disturbances, called as recessive disturbances (e.g., job set-up time variation and arrival time deviation), would gradually make the original production schedule obsolete. It is hard for production managers to perceive their presences. Thus, the impact of recessive disturbances can not be eliminated by rescheduling in time. On account of this, a rescheduling decision mechanism for recessive disturbances in RFID-driven job shops is proposed in this article, and a manifold learning method, which reduces the response time of manufacturing system, is applied in the mechanism to preprocess manufacturing data. The rescheduling decision mechanism is expected to answer the questions of whether to reschedule, when to reschedule, and which rescheduling method to be used. Firstly, RFID devices acquire the actual process completion time of all work in process (WIPs) at every WIP machining process completion time. Secondly, recessive disturbances are quantified to time accumulation error (TAE) which represents the difference between actual process completion time and planned process completion time. Lastly, according to the TAE and production managers’ experience, the rescheduling decision mechanism selects a proper rescheduling method to update or repair the original production schedule. The realization algorithms of rescheduling decision mechanism includes: (1) supervised locally linear embedding. (2) General regression neural network. (3) Least square-support vector Machine. Finally, a numerical experiment is used to demonstrate the implementation procedures of the rescheduling decision mechanism.     

An effective Lagrangian relaxation approach for rescheduling a steelmaking-continuous casting process

This paper studies a steelmaking-continuous casting (SCC) rescheduling problem with machine breakdown and processing time variations. Two objectives are considered in this study: the efficiency objective and the stability objective. The former refers to the total weighted completion time and total sojourn time, whereas the latter refers to the number of operations processed on different machines in the initial and revised schedules. We develop a time-index formulation and an effective Lagrangian relaxation (LR) approach with machine capacity relaxation to address the rescheduling problem. The LR approach decomposes the relaxed problem into batch-level subproblems with variable processing times. A polynomial two-stage dynamic programming algorithm is proposed to solve the batch-level subproblems. An efficient subgradient algorithm with global convergence is presented to solve the corresponding Lagrangian dual (LD) problem. Computational experiments based on practical production data show that the proposed approach not only produces a high quality schedule within an acceptable time but also performs much better than a practical SCC rescheduling method from a large iron and steel enterprise in China.     

云制造环境下的动态调度

在云制造环境下, 制造资源和制造能力以服务的形式封装起来, 不同的任务通过云端汇集到云平台并通过合适的调度给每个任务分配相应的服务. 由于任务在执行的过程中的不确定性, 会在某个时刻遇到突发状况从而导致对余下任务的重调度问题. 因此, 针对该问题, 考虑到云制造环境下任务的复杂性和多样性会导致在合理的时间段内很难找到最优解, 以所有任务的最大完成时间为优化目标, 提出了一种以改进的遗传算法与邻域搜索技术相结合的元启发式算法, 旨在解决云制造环境下由于任务和资源服务等的不确定性导致的重调度问题. 实验结果表明,本文所提出的算法能够很好地解决动态调度过程中的重调度问题, 并可以快速地获取最优解.     

A control oriented model for manual-pick warehouses

Nowadays the low cost of wireless communications allows the real time monitoring of the state of manual-pick warehouse systems making possible the real time control of these systems. At this aim, an approach based on a two-level model is presented in this paper. At the lower level, Petri nets are used to build online a model representing the active missions state and to detect conflicts among resources. At the upper level, logical expressions are used to add constraints for a single mission or item. The model is proved to be effective for online monitoring, scheduling and rescheduling of warehouse activities.     

An anticipative scheduling approach with controllable processing times

In practice, machine schedules are usually subject to disruptions which have to be repaired by reactive scheduling decisions. The most popular predictive approach in project management and machine scheduling literature is to leave idle times (time buffers) in schedules in coping with disruptions, i.e. the resources will be under-utilized. Therefore, preparing initial schedules by considering possible disruption times along with rescheduling objectives is critical for the performance of rescheduling decisions. In this paper, we show that if the processing times are controllable then an anticipative approach can be used to form an initial schedule so that the limited capacity of the production resources are utilized more effectively. To illustrate the anticipative scheduling idea, we consider a non-identical parallel machining environment, where processing times can be controlled at a certain compression cost. When there is a disruption during the execution of the initial schedule, a match-up time strategy is utilized such that a repaired schedule has to catch-up initial schedule at some point in future. This requires changing machine–job assignments and processing times for the rest of the schedule which implies increased manufacturing costs. We show that making anticipative job sequencing decisions, based on failure and repair time distributions and flexibility of jobs, one can repair schedules by incurring less manufacturing cost. Our computational results show that the match-up time strategy is very sensitive to initial schedule and the proposed anticipative scheduling algorithm can be very helpful to reduce rescheduling costs.