生产批量计划问题的干扰管理研究

以成组单元生产批量计划问题为原型,研究了在生产计划执行过程中受到机器故障干扰的调整方案.运用干扰管理方法,在扰动判定和度量的基础上,建立了考虑计划偏离度和总成本最小化的成组单元生产批量计划干扰管理模型,并设计了求解该模型的知识进化算法.当干扰发生时,系统能快速响应因机器故障造成的生产能力不足问题,重新调整生产计划.通过仿真实验,与全局重调度方案结果进行对比,验证了本干扰事件处理方法的可行性和有效性.     

微粒群优化算法在流水线干扰管理调度中的应用

微粒群优化算法(Particle Swarm Optimization,PSO)是起源于鸟群和鱼群群体运动行为的研究,是在蚁群算法提出之后的又一种新的进化计算技术,具有典型的群体智能特性。本文构建了干扰为工件到达的流水车间调度干扰管理模型,其经典目标函数为最大完工时间和干扰目标函数为干扰时间差相混合。本文运用微粒群优化算法求解流水线干扰管理调度问题,给出了计算实例并进行了详细分析,并对干扰管理问题和重调度问题进行了测试分析,得出了有参考意义的结果。     

生产调度干扰管理模型构建及智能算法研究

在现代企业生产经营过程中,生产调度的作用日益突出。它是生产管理领域内的关键生产环节。干扰管理是近些年来学者提出的一种新的处理生产过程中突发事件的思想和方法,已在供应链、物流以及某些特定领域的调度方面有所应用。流水车间调度问题(Flow-shopScheduling Problem,FSP)是一类复杂且极有代表性的流水线生产调度问题的简化模型,它无论是在离散制造工业还是在流程工业中都具有广泛的应用,具有一定的代表性。构建了流水车间调度问题以及干扰为工件到达的流水车间调度干扰管理模型,其经典目标函数为最大完工时间和干扰目标函数为干扰时间差相混合。     

顾客时间窗和维修需求变动下车辆维修的干扰管理优化

针对车辆维修中因顾客时间窗和维修需求量变动所带来的干扰恢复问题,从干扰管理的角度,提出整车维修的扰动度量方法、扰动恢复策略及干扰管理优化模型。并通过数值算例,对比了干扰管理优化结果、按原维修流程执行结果、及重新分配维修计划结果等三种不同的干扰处理方式,结果表明,1）相对于按原流程计划执行结果,干扰管理优化的结果在顾客服务时间偏离上减少32-14%、成本节约14-89%;2）相对于重新分配维修任务的结果,干扰管理优化结果虽然在成本费用上略有增高,但在顾客服务时间偏离上减少28-53%、维修流程偏离上减少66-67%。     

谐波干扰作用下柔性结构振动的增益调度H∞控制

针对柔性结构受到随系统运行工况的变化而改变频率的谐波干扰作用下的振动控制问题，提出了一种新的增益调度H∞控制器设计方案，首先，对谐波干扰信号建模，并将该模型与系统结构模型结合形成广义模型，然后，选取二次性能指标将谐波干扰作用下振动抑制问题转换为标准的H∞控制器设计问题，最后，采用增益调度技术对典型谐波频率所设计H∞控制器的输出进行模糊加权插值，从而获得随谐波干扰频率变化的控制器输出，通过与LQC和一般的H∞控制器进行数值的仿真比较，验证了方法的有效性。     

基于OFDM的大气激光通信湍流抑制关键技术研究

文章在分析无线激光通信(FSO)存在两种主要的大气信道问题的基础上,针对激光大气信道问题尤其是在复杂湍流环境下的频率选择性衰落问题和多径效应问题,提出了基于正交频分复用(OFDM)的湍流效应抑制方法,构建了FSO-OFDM系统,研究了该系统的基带模型以及信号的多载波调制与解调方法。分析了无线激光通信中存在复杂湍流环境下的大气信道问题,讨论了大气湍流影响下的平面波激光通信系统模型,建立了大气湍流影响下对数正态湍流通道的高斯光束空间光通信系统模型,推导了光波强度的概率密度函数,研究了利用信噪比概率密度函数分析各种大气湍流效应对系统性能影响的方法;设计了无线光通信系统的OFDM多载波调制方案,构建了FSO-OFDM系统基带模式模型,并基于该模型研究了其信号的调制与解调原理。最后,采用MATLAB编程实现FSO-OFDM系统,对多径干扰下的FSO通信系统进行仿真实验,进行了不同保护间隔下的误码率特性实验,验证了FSO-OFDM系统具有很强的抗多径干扰和频谱选择性衰落能力以及良好的BER性能,可有效解决码间干扰大、链路不可靠的问题,具有非常广泛的应用前景和使用价值。     

基于近场测试进行辐射干扰机理诊断的方法研究

为诊断被测电路的辐射干扰机理,通过对电偶极子和磁偶极子模型的近场电磁辐射进行分析,提出了通过测试近场波阻抗进行电路辐射干扰机理诊断的方法,实验表明基于近场测试进行辐射干扰机理判断是一种简单易行的方法,得到的结果与电波暗室测试结果一致。     

基于模型参考自适应的球形机器人运动控制

针对球形机器人动力学模型不准确、参数不确定的问题,设计了一种基于李雅普诺夫(Lyapunov)方法的模型参考自适应控制(MRAC)算法。首先,搭建了基于全向轮驱动的球形机器人实验平台,并利用拉格朗日方程进行动力学建模。然后,为解决球形机器人运动过程中参数易受干扰的问题,采用MRAC进行运动控制,其中自适应控制率利用李雅普诺夫方法进行设计。最后,通过在线仿真和实测实验,结果表明MRAC具有良好的控制效果,在运动参数受到干扰时仍有较好的控制能力。     

基于时频关系的复合人为干扰自适应预测

分析了军事无线通信系统复合人为干扰的构成,建立了复合人为干扰模型,提出了基于时频关系的复合人为干扰自适应预测方法。该方法通过分析干扰信号时间和频率的关系,采用分类搜索算法将复合干扰解析成基本的干扰分量,然后基于各基本干扰分量进行干扰趋势的自适应预测,最后对预测的结果进行准确性检验,并用检验标识符实时调整解析和预测分量,从而完成干扰的自适应预测。理论分析和仿真结果表明,对于有一定复杂度的复合人为干扰,运用上述自适应预测方法能够较为准确地对干扰趋势进行预测。     

基于混合裁剪失衡数据增强与SwinNet网络的滚动轴承故障诊断

针对在滚动轴承故障诊断领域中存在的故障样本较少,健康样本丰富所导致的故障类别失衡问题以及环境中存在噪声与人为噪声标签干扰等问题,提出了一种基于混合裁剪失衡数据增强与SwinNet网络相结合的故障诊断模型(fault diagnosis model combining mixed-cutout imbalance data augmentation and SwinNet, SwinNet-MCIDA)。首先,借鉴图像分类数据增强方法,利用混合裁剪失衡数据增强算法对失衡类别的数据进行裁剪、混合处理生成新的故障样本来增加样本量,构造出增强数据集,然后对增强数据集进行小波变换转换成时频图像,将所得图像输入到卷积神经网络与Swin Transformer编码器相结合的SwinNet网络模型中,进行特征提取和故障分类,从而实现滚动轴承故障的高效诊断。试验结果表明,该文所提出的SwinNet-MCIDA故障诊断方法不仅可以很好地解决滚动轴承故障诊断领域故障类别失衡问题,而且也可以很好地应对故障数据中存在环境噪声问题与人为噪声标签干扰问题。     

基于仿真的长距离引水隧洞施工全过程进度实时控制与可视化分析研究

长距离引水隧洞常具有埋深大、洞线长、洞径大和工程地质条件极其复杂等特点,各工序工程量大,相互间影响巨大,因此,在实际施工过程中内外环境和约束条件可能发生变化,使原定的施工进度计划与实际施工进程不可避免地产生偏差。如果这种偏差不及时纠正,将会越来越大,以致原计划进度起不到指导实际施工的作用。文章利用计算机仿真技术、控制论思想和虚拟现实技术,提出了基于仿真的长距离引水隧洞施工全过程进度实时控制与可视化分析方法,对施工进度的实时控制提供了可行性研究和科学依据。     

Real-time disruption management in a two-stage production and inventory system

This paper presents a general disruption management approach for a two-stage production and inventory control system. A penalty cost for deviations of the new plan from the original plan is incorporated and the concept of a disruption recovery time window is introduced. We define two classes of problems: one with fixed setup epochs and another with flexible setup epochs. With linear or quadratic penalty functions for production/ordering quantity change and fixed setup epochs, the best recovery plan is obtained by solving a quadratic mathematical programming problem. With convex penalty functions for quantity changes and flexible setup epochs, it is shown that the second stage orders have identical order quantities within each production cycle. Therefore, in a lot-for-lot system, the ordering and production quantities for both stages are the same. As a special case, we consider disruption recovery problems with short time windows spanning one or two production cycles. We also discuss solution procedures for both major and minor disruption problems and give an extension for the case of multiple retailers. Throughout the paper managerial insights are presented that indicate how a company should respond to various types of disruptions during its operations.     

基于DSM的项目成组管理模式的研究

为克服传统的项目进度计划方法在基于多种约束条件的多项目管理环境下表现出的不足,引入设计结构矩阵(DSM)建立了基于DSM的项目成组管理模式,该模式能够将多项目自身信息、资源使用信息、各个作业之间的信息约束关系和资源约束关系等充分反映在一个矩阵中,从而便于进行多项目管理.用1个实例说明了该模型的实用性.     

Production rescheduling for machine breakdown at a job shop

Rescheduling is a procedure to repair a production plan affected by unexpected disruptions. It is important because a production schedule released to a shop floor is subject to unexpected disruptions. This paper develops two novel heuristic rescheduling procedures, AWI-J and AWI-O, to minimise mean tardiness of jobs in a job shop. The procedures are based on an active schedule-generation procedure and Wilkerson–Irwin algorithm, which is well known for minimising mean tardiness of a schedule. The performances of the new procedures are compared with those of the Affected Operations Rescheduling (AOR) procedure, which is popular in the rescheduling literature. Efficiency measures such as mean tardiness, mean flowtime, and makespan, and a stability measure, deviation of new operation start times from the original start times, are used for comparison. Test results show that AWI-J improves efficiency over AOR, and AWI-O improves efficiency and stability as well.     

An improved robust buffer allocation method for the project scheduling problem

Unpredictable uncertainties cause delays and additional costs for projects. Often, when using traditional approaches, the optimizing procedure of the baseline project plan fails and leads to delays. In this study, a two-stage multi-objective buffer allocation approach is applied for robust project scheduling. In the first stage, some decisions are made on buffer sizes and allocation to the project activities. A set of Pareto-optimal robust schedules is designed using the meta-heuristic non-dominated sorting genetic algorithm (NSGA-II) based on the decisions made in the buffer allocation step. In the second stage, the Pareto solutions are evaluated in terms of the deviation from the initial start time and due dates. The proposed approach was implemented on a real dam construction project. The outcomes indicated that the obtained buffered schedule reduces the cost of disruptions by 17.7% compared with the baseline plan, with an increase of about 0.3% in the project completion time.     

Reducing tardy jobs by integrating process planning and scheduling functions

This paper presents a dynamic approach to reduce tardy jobs through the integration of process planning and scheduling in a batch-manufacturing environment. The developed method aims at re-generating a schedule with fewer tardy jobs, step by step, by exploring the process plan solution space of the tardy jobs. The integrated system comprises a process planning module, a scheduling module, and an integrator module. The process planning module employs an optimisation approach in which the entire plan solution space is first generated and a search algorithm is then used to find the optimal plan, while the scheduling module is based on commonly used heuristics. Based on the job tardiness information of the generated schedule, the integrator module automatically issues a modification order to the process plan solution space of the tardy jobs. The process planning and scheduling modules are then re-run to generate a new plan/schedule solution. Through this iterative process, a satisfactory schedule can be gradually achieved. The uniqueness of this approach is characterised by the flexibility of the process planning strategy, which makes full use of the plan solution space intuitively to achieve a satisfactory schedule. Several examples are presented to confirm the efficacy and the effectiveness of the developed integration system.     

A portfolio approach to supply chain disruption management

A new, computationally efficient portfolio approach to supplier selection in the presence of supply chain disruption risks is proposed, where the selection of supply portfolios for parts is combined with production scheduling of finished products. Unlike most of reported research on the supply chain risk management which focuses on the risk mitigation decisions taken prior to a disruption, the proposed portfolio approach combines decisions made before, during and after the disruption. The two decision-making approaches are considered: an integrated approach with the perfect information about the future disruption scenarios, and a hierarchical approach with no such information available. In the integrated approach, which accounts for all potential disruption scenarios, the primary supply portfolio that will hedge against all scenarios is determined along with the recovery supply portfolio and production schedule for each scenario. In the hierarchical approach, first the primary supply portfolio is determined, and then, when a primary supplier is hit by a disruption, the recovery supply portfolio is selected. For the integrated and the hierarchical decision-making, mixed integer programming models are developed with the two risk-neutral conflicting objectives that account for both time and cost of recovery: minimising expected cost or maximising expected service level. The findings indicate that for both objectives, the integrated decision-making selects a more diversified primary supply portfolio than the hierarchical approach and when all primary suppliers are shutdown by disruption, a single sourcing recovery portfolio is usually selected.     

Collection channel and production decisions in a closed-loop supply chain with remanufacturing cost disruption

Enterprise risk management (ERM) focuses on managing functions of an entire business, with special attention to supply chain uncertainty. Managing supply chain through effective ERM will mitigate firm exposure to risk in operations and ensure the success of the business. We study collection channel and production decisions in a closed-loop supply chain (CLSC) with one dominant manufacturer and one retailer, from the perspective of both firm profit and system robustness. A robust system is desired as only minimal changes in production plan are necessary when facing disruption, and is valuable for enterprise risk reduction. We find that without disruption the indirect (retailer) collection channel achieves higher profit than that of the direct (manufacturer) channel, which is also true under some disruption cases. However, the direct channel is more robust when facing disruption, and generates more profits for manufacturers when the positive disruption is large. We also find that the revenue-sharing contracts are effective in coordinating members of the CLSC. Finally, we conduct numerical studies to validate the models and derive managerial insights.