Machine scheduling with pickup and delivery

The coordination of production, supply, and distribution is an important issue in logistics and operations management. This paper develops and analyzes a single‐machine scheduling model that incorporates the scheduling of jobs and the pickup and delivery arrangements of the materials and finished jobs. In this model, there is a capacitated pickup and delivery vehicle that travels between the machine and the storage area, and the objective is to minimize the makespan of the schedule. The problem is strongly NP‐hard in general but is solvable in polynomial time when the job processing sequence is predetermined. An efficient heuristic is developed for the general problem. The effectiveness of the heuristic is studied both analytically and computationally. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

A model to predict the residual life of aircraft engines based upon oil analysis data

This paper reports on a study using the available oil monitoring information, such as the data obtained using the Spectrometric Oil Analysis Programme (SOAP), to predict the residual life of a set of aircraft engines. The relationship between oil monitoring information and the residual life is established using the concept of the proportional residual, which states that the predicted residual life may be proportional to the wear increment measured by the oil analysis programmes. Assuming such a relationship between wear and the residual life exists, we formulated a recursive prediction model for the item's residual life given measured oil monitoring information to date. A set of censored life data of 30 aircraft engines (right censored due to preventive overhaul) along with the history of their monitored metal concentration information are available to us. The metal concentration information includes many variables, such as Fe, Cu, Al, etc.; not all of them are useful, and some of them may be correlated. The principal component analysis (PCA) has been adopted to reduce the dimension of the original data set and to produce a new set of uncorrelated variables, which we shall use in the prediction model. The procedure associated with estimating model parameters is discussed. The model is fitted to the actual SOAP data from the aircraft engines, and the goodness‐of‐fit test has been carried out. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

汽车维修的环境影响和绿色维修

资产维修是社会持续发展的重要技术途径,同时,维修又要消耗资源产生废弃物,对环境造成危害。汽车维修是最庞大的维修行业。以某运输团军用汽车年维修产生的废弃物的统计分析为基础,论述了推行绿色维修的重要性和紧迫性,并针对目前的维修废弃物处理方式及其危害性,从管理及回收处置方面提出了相应的改进意见。     

故障检查间隔期的简化计算方法

建立了定期故障检查策略下系统的可用度模型,在满足系统可用度要求的条件下,可以使用该模型确定故障检查工作的间隔期。为了简化故障检查间隔期的计算,给出了故障时间服从指数分布时,故障检查间隔期的上、下限计算公式及应用示例。     

待修弹药信息管理与决策支持系统开发

在已建立的维修决策理论模型的基础上,分析了待修弹药信息管理与决策支持系统的组成和所要实现的功能,并深入研究了系统包含的数据库、功能模块、知识库和决策过程的开发与实现。     

通用雷达装备虚拟维修训练系统设计

针对目前装备模拟训练系统研制所存在的问题,以雷达装备为研究对象,开发了一种通用虚拟维修训练系统。该系统运用高层体系结构和组件技术,采用总线—插件的分布式软件体系结构,实现了系统开放互联的要求,有效降低了开发成本,提高了系统的通用性。     

经验模态分析法在雷达信号脉内分析中的应用

雷达脉内特征分析是信号分选领域一个重要手段,当脉内信号有多个雷达信号交叠在一起时,很难分析出其各自的调制方式。经验模态分解(EMD)在分析非平稳混合信号时效果显著,但其存在2个明显弊端。针对端点效应问题,首先采用小波分解将信号分解成各分量,随后对除噪声外的各分量利用ARMA模型对信号进行预测,接着对预测后的各分量进行小波重构,从而消除了端点效应,针对虚假分量的问题,结合雷达信号的特点改进了其终止条件,提高了EMD分解的性能。最后,对EMD分解后的各分量进行时频分析,得出雷达脉内信号的调制特征。仿真验证了算法的有效性。     

基于能力的维修装备体系需求分析问题研究

分析了基于能力的维修装备体系需求分析的基本内涵,对基于能力的维修装备体系需求分析过程进行了形式化描述,提出了把分析过程划分为维修装备体系能力需求分析、装备维修活动需求分析、维修装备体系需求分析等3个阶段的思路及步骤,并对各个步骤之间的关联进行了阐述。     

一种基于改进的ROUSTIDA算法的数据补齐方法

针对ROUSTIDA数据补齐方法存在的问题,提出了一种改进的ROUSTIDA数据补齐方法.该方法首先将条件属性与决策属性区分对待,优先填补决策属性值,避免了不一致决策表的产生;然后,在决策属性相同的前提下填补条件属性缺失值,扩展了原算法的使用范围;最后,通过一个实例说明改进算法补齐后的决策表的确能够获得更集中的决策规则...     

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.