水面舰艇发射鱼雷初期弹道优化研究

为了给水面舰艇发射鱼雷提供一定的理论可行性依据,以保证鱼雷的有效使用,减少试验次数和损失。利用动量定理和动量矩定理建立水面舰艇发射鱼雷初期弹道的数学模型。用Runge-Kutta法对鱼雷初期弹道进行仿真,并根据仿真结果分析了各种因素对鱼雷初期弹道的影响,从而得出水面舰艇发射鱼雷时各参数的最优值。     

Doubly Stochastic Sequential Assignment Problem

This article introduces the Doubly Stochastic Sequential Assignment Problem (DSSAP), an extension of the Sequential Stochastic Assignment Problem (SSAP), where sequentially arriving tasks are assigned to workers with random success rates. A given number of tasks arrive sequentially, each with a random value coming from a known distribution. On a task arrival, it must be assigned to one of the available workers, each with a random success rate coming from a known distribution. Optimal assignment policies are proposed for DSSAP under various assumptions on the random success rates. The optimal assignment algorithm for the general case of DSSAP, where workers have distinct success rate distribution, has an exponential running time. An approximation algorithm that achieves a fraction of the maximum total expected reward in a polynomial time is proposed. The results are illustrated by several numerical experiments. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 124–137, 2016     

Optimal staffing in nonstationary service centers with constraints

This paper considers optimal staffing in service centers. We construct models for profit and cost centers using dynamic rate queues. To allow for practical optimal controls, we approximate the queueing process using a Gaussian random variable with equal mean and variance. We then appeal to the Pontryagin's maximum principle to derive a closed form square root staffing (SRS) rule for optimal staffing. Unlike most traditional SRS formulas, the main parameter in our formula is not the probability of delay but rather a cost‐to‐benefit ratio that depends on the shadow price. We show that the delay experienced by customers can be interpreted in terms of this ratio. Throughout the article, we provide theoretical support of our analysis and conduct extensive numerical experiments to reinforce our findings. To this end, various scenarios are considered to evaluate the change in the staffing levels as the cost‐to‐benefit ratio changes. We also assess the change in the service grade and the effects of a service‐level agreement constraint. Our analysis indicates that the variation in the ratio of customer abandonment over service rate particularly influences staffing levels and can lead to drastically different policies between profit and cost service centers. Our main contribution is the introduction of new analysis and managerial insights into the nonstationary optimal staffing of service centers, especially when the objective is to maximize profitability. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 615–630, 2017     

Capacity expansion and cost efficiency improvement in the warehouse problem

The warehouse problem with deterministic production cost, selling prices, and demand was introduced in the 1950s and there is a renewed interest recently due to its applications in energy storage and arbitrage. In this paper, we consider two extensions of the warehouse problem and develop efficient computational algorithms for finding their optimal solutions. First, we consider a model where the firm can invest in capacity expansion projects for the warehouse while simultaneously making production and sales decisions in each period. We show that this problem can be solved with a computational complexity that is linear in the product of the length of the planning horizon and the number of capacity expansion projects. We then consider a problem in which the firm can invest to improve production cost efficiency while simultaneously making production and sales decisions in each period. The resulting optimization problem is non‐convex with integer decision variables. We show that, under some mild conditions on the cost data, the problem can be solved in linear computational time. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 367–373, 2016     

相控阵雷达LPI搜索方法研究

相控阵雷达可通过合理配置工作参数优化其性能。针对相控阵雷达在搜索目标时易受敌方电子侦察设备威胁的现实,研究了在保持搜索能力的同时,通过合理配置参数降低截获概率的问题。分析了影响相控阵雷达搜索状态LPI(Low Probability of Intercept)性能的主要因素,建立了截获概率模型与搜索能力模型,给出了3种参数调整策略。仿真试验验证了模型的有效性,并比较了3种策略的优劣。     

三关节机器人的神经网络补偿自适应控制器设计

三关节机器人广泛用于工业生产、轮式或履带式排爆机器人,为了补偿由于机器人结构参数、作业环境干扰等不确定性因素造成的机器人动力学模型的不确定性,将机器人动力学模型分解为名义模型和误差模型两部分,其误差模型采用RBF神经网络进行补偿,得到其估计信息,神经网络的输出权值根据Lyapunov稳定性理论采用自适应算法进行调整。所设计的神经网络补偿自适应控制器解决了不确定性机器人动力学系统控制器设计的不确定性问题,同时,通过定义Lyapunov函数,证明了控制器能渐近、稳定地跟踪期望轨迹。机器人的3个关节在控制器的作用下,约在5 s时达到期望轨迹,神经网络约在5 s时逼近机器人动力学模型的误差模型,实验结果表明了机器人关节对期望轨迹具有良好的轨迹跟踪性能。     

空中多飞行器飞行轨迹优化数值仿真

针对空中多飞行器在复杂环境中飞行轨迹的多目标最优问题,分析了多飞行器飞行过程中各种可视和不可视约束条件。基于在回避威胁区前提下燃料消耗最少、飞行时间最短的综合性能指标,采用“多方法组合”思路,提出了改进动态规划法和多点边值法组合算法,并进行了仿真验证,大量C＋＋数值飞行仿真结果表明该算法能够在考虑外界复杂环境和飞行器各种约束条件下快速规划出空中多飞行器的最优飞行轨迹,该组合算法具有一定的实用性和创新性。     

演化排列优化拉丁超立方设计方法

拉丁超立方设计是最常用的计算机试验设计方法之一,针对现有拉丁超立方设计方法采样一次性且难以兼顾设计的空间均匀性和计算效率的问题,提出了一种演化排列拉丁超立方试验设计方法。通过对小样本设计的演化、排列信息继承和扩充等操作,以较小的计算量实现了样本的扩充与优化。此外,所提方法可以兼顾现有样本和新增采样点之间的关系,实现样本的序列扩充,这在实际近似建模过程中十分方便。通过多组数值试验,验证了本文方法在空间均匀性和计算效率等方面的优越性。     

Selecting review periods for a coordinated multi‐item inventory model with staggered deliveries

Consider an N‐item, periodic review, infinite‐horizon, undiscounted, inventory model with stochastic demands, proportional holding and shortage costs, and full backlogging. For 1 ≤ j ≤ N, orders for item j can arrive in every period, and the cost of receiving them is negligible (as in a JIT setting). Every T_j periods, one reviews the current stock level of item j and decides on deliveries for each of the next T_j periods, thus incurring an item‐by‐item fixed cost k_j. There is also a joint fixed cost whenever any item is reviewed. The problem is to find review periods T₁, T₂, …, T_N and an ordering policy satisfying the average cost criterion. The current article builds on earlier results for the single‐item case. We prove an optimal policy exists, give conditions where it has a simple form, and develop a branch and bound algorithm for its computation. We also provide two heuristic policies with O(N) computational requirements. Computational experiments indicate that the branch and bound algorithm can handle normal demand problems with N ≤ 10 and that both heuristics do well for a wide variety of problems with N ranging from 2 to 200; moreover, the performance of our heuristics seems insensitive to N. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48:430–449, 2001     

一类对固定目标的集群搜寻对策问题及求解

利用集群搜索对策的理论与方法 ,建立了集群对固定目标的一类搜索对策模型 ,给出了集群的ε -最优搜寻策略 ,并考虑了其在搜索过程中的应用