飞行器低空突防威胁建模与航迹优化算法

提出了一种飞行器在给定地形和威胁分布的威胁场进行低空突防的航迹优化算法。该方法首先对地形的高程栅格数据进行了图形简化预处理,将各种威胁叠加到地形图上,构成一个包含各种威胁的特定威胁场,再对其建立合理的有向图数学建模,用Dijkstra最短路算法进行航迹优化。航迹优化的过程中考虑了飞行器的过载限制,使优化航迹能够较好地满足飞行器各项性能指标及任务规划的要求,仿真结果显示,该算法简单快速,能很好地进行地形、威胁、障碍物的回避。     

Design optimization for a launching system with novel structure

A launching system with novel structure using optimization method is investigated to improve the muzzle velocity of guns in this article. This system has two tandem chambers of which the front one is ignited first and the other is ignited after a while. The launching process of this novel system is modelled and a series of different schemes are simulated, to discover the interior ballistic performance of this novel launching system. In order to obtain the optimal loading conditions, an optimization model combined with the combustion model is established. The optimal schemes can improve the muzzle velocity by 20.6% without changing the parameters of barrel. It means that this novel launch system could improve the interior ballistics performance significantly and it still has considerable potential to be ameliorated.     

空陆攻防博弈的动态武器目标分配

大规模作战具有高动态、非完全信息和不确定性,在分析归纳目前解决动态武器目标分配问题的一系列方法的基础上,尝试构建基于双方动态博弈的攻防对抗综合数学模型,并利用纳什均衡和帕累托最优算法进行分阶段求解。结果表明,该数学模型和博弈论方法结合能够有效解决武器目标动态分配问题。     

A worst‐case formulation for constrained ranking and selection with input uncertainty

In this research, we consider robust simulation optimization with stochastic constraints. In particular, we focus on the ranking and selection problem in which the computing time is sufficient to evaluate all the designs (solutions) under consideration. Given a fixed simulation budget, we aim at maximizing the probability of correct selection (PCS) for the best feasible design, where the objective and constraint measures are assessed by their worst‐case performances. To simplify the complexity of PCS, we develop an approximated probability measure and derive the asymptotic optimality condition (optimality condition as the simulation budget goes to infinity) of the resulting problem. A sequential selection procedure is then designed within the optimal computing budget allocation framework. The high efficiency of the proposed procedure is tested via a number of numerical examples. In addition, we provide some useful insights into the efficiency of a budget allocation procedure.     

Excess procurement strategies by a dominant buyer under constrained supply

Supply chains are often characterized by the presence of a dominant buyer purchasing from a supplier with limited capacity. We study such a situation where a single supplier sells capacity to an established and more powerful buyer and also to a relatively less powerful buyer. The more powerful buyer enjoys the first right to book her capacity requirements at supplier's end, and then the common supplier fulfills the requirement of the less powerful buyer. We find that when the supplier's capacity is either too low (below the lower threshold) or too high (above the higher threshold), there is no excess procurement as compared to the case when supplier has infinite capacity. When the supplier's capacity is between these two thresholds, the more powerful buyer purchases an excess amount in comparison to the infinite capacity case.     

Shortest path interdiction problem with arc improvement recourse: A multiobjective approach

We consider the shortest path interdiction problem involving two agents, a leader and a follower, playing a Stackelberg game. The leader seeks to maximize the follower's minimum costs by interdicting certain arcs, thus increasing the travel time of those arcs. The follower may improve the network after the interdiction by lowering the costs of some arcs, subject to a cardinality budget restriction on arc improvements. The leader and the follower are both aware of all problem data, with the exception that the leader is unaware of the follower's improvement budget. The effectiveness of an interdiction action is given by the length of a shortest path after arc costs are adjusted by both the interdiction and improvement. We propose a multiobjective optimization model for this problem, with each objective corresponding to a different possible improvement budget value. We provide mathematical optimization techniques to generate a complete set of strategies that are Pareto‐optimal. Additionally, for the special case of series‐parallel graphs, we provide a dynamic‐programming algorithm for generating all Pareto‐optimal solutions.     

采用遗传算法的电控增压器电磁阀优化设计

运用Ansoft Maxwell软件建立电磁阀的动态响应特性仿真模型,并通过试验验证模型的准确性。利用模型集成ISIGHT软件的方法,对影响电磁阀动态响应特性的参数进行灵敏度分析,得出各个参数对动态响应特性的Pareto图。基于遗传算法对电磁阀进行多参数优化设计,获取最佳参数组合,并利用电磁阀动态响应特性试验台架,开展参数优化前后电磁阀动态响应特性的对比试验研究。试验结果表明：相比于原参数方案,参数优化后的电磁阀动态响应特性得到大幅度提升,其开启响应时间缩短了59.1%,关闭响应时间缩短了47.2%。     

复杂网络影响力极大化快速评估算法

分析复杂网络中影响力极大化问题,设计一种新的启发式算法框架。针对信息传递中节点的交互方式进行分析,给出节点在任意时刻处于信息接收态的概率。通过期望计算得到种子节点集传播影响力的近似估计,实现集群影响力快速计算,进而得到基于序列采样的影响力极大化快速评估算法。特别地,对于六个来自不同领域的真实网络上的影响力极大化问题进行了研究,仿真结果表明：该方法能够高效识别网络中具有重要传播影响力的节点集,在三种常见度量准则下的表现均明显优于三种影响力极大化问题基准算法。     

舰炮身管的模态分析与多目标优化

舰炮的炮口扰动是影响舰炮射击精度的重要因素。为了减少舰炮的炮口扰动,优化身管结构,建立了柔性身管的ABAQUS有限元模型,将有限元模型的模态计算值与模态试验值进行比较,发现建立的柔性身管有限元模型是合理可行的。提出了混沌量子粒子群算法与动力学联合优化的方法,进行身管和炮口的多目标优化。优化结果表明,优化后炮口中心的线速度、角速度和角位移与优化前相比显著减小,身管质量有所降低,结构更加合理,该优化方法有效可行,为下一步全炮总体优化设计提供了一定的借鉴。     

Assortment optimization with position effects under the nested logit model

We study the assortment optimization problem with position effects under the nested logit model, whose goal is to find the revenue-maximizing subset of products as well as their corresponding display positions. In this joint assortment-position optimization problem, the choices of products are affected by not only their qualities and prices but also the positions where they are displayed. Despite determining the assortment and their corresponding display positions sequentially, we propose to solve this problem in an integrated way to obtain the optimal solution. We formulate this problem as a nonlinear binary integer programming model and develop a dynamic programming based solution approach to obtain the optimal assortment-position assignments. We carry out extensive numerical experiments to evaluate the benefit of our integrated approach. The most important insight we discover is that it is not necessarily better to put the most attractive products in the best position. Moreover, we show that compared to the sequential approaches, our approach can improve revenue by 10.38% on average, which suggests that firms should take into consideration position effects when making assortment decisions. Finally, we discuss results related to two extensions of this problem, that is, the special case when positions are preassigned to nests, and the joint assortment-position-price optimization problem.