仿真计算机和仿真软件发展

介绍仿真机和仿真软件的发展,分析了银河仿真机系统性能,探讨了仿真机的未来发展特征。     

基于圆孔夫琅和费衍射的CCD像素间距标定

提出了一种基于圆孔夫琅和费衍射原理的像素间距标定方法,利用研制的测试设备对图像采集系统的像素间距进行了测试,实现了CCD摄像系统像素间距的标定。     

基于遗传算法的观察所优化配置

文章研究了三维地形中的观察所优化配置问题,结合观察所实际情况,给出了利用遗传算法进行观察所优化的计算方法,并编制了计算机应用程序.计算机模拟实验表明,文中采用的算法能综合考虑多种因素,所得结果合理、准确,并且具有较高的效率.     

具有不确定信息的作战方案优选的群决策方法

根据群决策的特点,按照集议决策的习惯程序,提出了一种充分发挥集议决策优势的群决策方法。并基于区间分析对潜艇布雷方案优选中的不确定性进行处理,最后利用了区间数多属性决策的TOPSIS法行为集结的最后结果（决策矩阵）进行数学集结,较好地做到了决策在定性和定量上的综合集成。     

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.     

Approximation of multiclass queueing networks with highly variable arrivals under deterministic routing

In this paper, we present the heavy‐traffic bottleneck phenomenon under multiclass deterministic routing and discuss how it can be addressed by decomposition approximation. Examples show that Bitran and Tirupati's method and Whitt's enhancements for deterministic routing may not properly account for this phenomenon. We propose refinements to these methods based on Whitt's variability functions. Results of numerical experiments on simple networks and semiconductor manufacturing process show significant improvement in the approximation of expected waiting time at bottleneck stations. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

基于Markov链的分布式仿真系统最佳检查点间隔研究

基于HLA的分布式仿真系统作为一类特殊的分布式系统,其容错一般基于回卷恢复实现,在回卷恢复中检查点间隔的不同会对系统性能产生很大的影响。分析了分布式仿真容错与一般分布式系统容错的异同,根据不同仿真进程对仿真结果的重要程度对其进行了分类,定义并利用Markov链分析了采用回卷恢复时分布式仿真系统的可用度,得到了系统最大可用度对应的检查点间隔的求解等式,通过一组试验数据验证了该最佳检查点间隔求解等式的正确性。     

火炮动态后坐装置有限元分析与优化

活塞缸是火炮动态后坐装置中的关键部件,其结构强度设计是整个实验装置强度设计的核心。利用I-DEAS软件,在对其进行实体建模和有限元网格划分的基础上,计算了活塞缸整体的应力应变值,得到了活塞缸工作时的应力应变图,并对其结构中应力状况恶劣的部位进行了结构优化。