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.     

Non‐greedy heuristics and augmented neural networks for the open‐shop scheduling problem

In this paper we propose some non‐greedy heuristics and develop an Augmented‐Neural‐Network (AugNN) formulation for solving the classical open‐shop scheduling problem (OSSP). AugNN is a neural network based meta‐heuristic approach that allows integration of domain‐specific knowledge. The OSSP is framed as a neural network with multiple layers of jobs and machines. Input, output and activation functions are designed to enforce the problem constraints and embed known heuristics to generate a good feasible solution fast. Suitable learning strategies are applied to obtain better neighborhood solutions iteratively. The new heuristics and the AugNN formulation are tested on several benchmark problem instances in the literature and on some new problem instances generated in this study. The results are very competitive with other meta‐heuristic approaches, both in terms of solution quality and computational times. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

A logistics scheduling model: Inventory cost reduction by batching

Logistics scheduling refers to the problems where the decisions of job scheduling and transportation are integrated in a single framework. In this paper, we discuss a logistics scheduling model where the raw material is delivered to the shop in batches. By making the batching and scheduling decisions simultaneously, the total inventory and batch setup cost can be reduced. We study different models on this issue, present complexity analysis and optimal algorithms, and conduct computational experiments. Some managerial insights are observed. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

临近空间飞行器等离子体鞘套的太赫兹波穿透特性

临近空间高超声速飞行器再入过程中会产生等离子体鞘套,干扰电磁波对飞行器的探测.针对这一问题,对典型临近空间飞行器模型进行建模,模拟其再入过程中不同飞行工况下的流场分布.基于流场分布对等离子体参数分布进行建模,利用散射矩阵方法从理论上对太赫兹波在等离子体鞘套中的传输特性进行计算.计算结果表明高频太赫兹波能够有效穿透等离子...     

红外烟幕面积的摄像测试方法

从分析多波段红外烟幕性能和它在热像仪上的成像规则入手,阐述了应用热成像测试方法的基本思想、硬件配置与工作流程,详细介绍了热成像测试方法中有关烟幕面积计算的数学方法以及在本测试方法中使用的去除噪声和图像增强的图像处理过程,最后对整个热成像测试方法作了简单的分析。     

实时磁盘调度算法研究

总结分析了现阶段流行的实时磁盘调度算法,针对磁盘的存储特性,在对任务请求的磁盘访问时间精确预估的基础上,提出了一种新的调度算法-DSJIT(diskjustintime).该算法通过对请求文件在磁盘上分布的预分析得出处理请求所需时间的一个精确预估值,然后根据此预估值再对任务队列进行调度调整,它在保证应用程序对磁盘工作实时要求的同时,尽可能地提高磁盘的吞吐量.最后通过模拟试验,验证了算法的有效性.     

基于概念模型的仿真模型调度决策法

仿真模型调度决策是实现仿真结果合理、可信、可用的重要环节。界定了模型调度决策问题域,引入了模型调度序列表示决策结果,提出了基于UML(unified modeling language)概念模型的模型调度决策法,分析了防空作战仿真模型调度决策示例,为解决舰艇作战仿真模型的调度决策提供可行办法,对复杂军事问题仿真模型调度决策具有一定的理论意义。     

Scheduling with centralized and decentralized batching policies in concurrent open shops

This article considers batch scheduling with centralized and decentralized decisions. The context of our study is concurrent open shop scheduling where the jobs are to be processed on a set of independent dedicated machines, which process designated operations of the jobs in batches. The batching policy across the machines can be centralized or decentralized. We study such scheduling problems with the objectives of minimizing the maximum lateness, weighted number of tardy jobs, and total weighted completion time, when the job sequence is determined in advance. We present polynomial time dynamic programming algorithms for some cases of these problems and pseudo‐polynomial time algorithms for some problems that are NP‐hard in the ordinary sense. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 58: 17–27, 2011     

多光谱共孔径成像自动聚焦系统研究

针对自主研制的可见光波段与近红外激光波段共孔径复合成像平台中2个波段不能同时清晰成像的问题,在激光探测成像光路中设计了自动聚焦模块。不同于以往移动镜片组的方式,该自动聚焦系统针对实际问题,采用了移动像面的方式。通过实验选取了适合评价激光反射成像的聚焦函数,并且设计了合理的聚焦窗口。提出了＂三段式＂搜索策略,减少了电机反转次数,提高了搜索效率和精度。     

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.