Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty

We propose a novel simulation‐based approach for solving two‐stage stochastic programs with recourse and endogenous (decision dependent) uncertainty. The proposed augmented nested sampling approach recasts the stochastic optimization problem as a simulation problem by treating the decision variables as random. The optimal decision is obtained via the mode of the augmented probability model. We illustrate our methodology on a newsvendor problem with stock‐dependent uncertain demand both in single and multi‐item (news‐stand) cases. We provide performance comparisons with Markov chain Monte Carlo and traditional Monte Carlo simulation‐based optimization schemes. Finally, we conclude with directions for future research.     

Exact algorithms and bounds for the dynamic assignment interdiction problem

We study a multi‐stage dynamic assignment interdiction (DAI) game in which two agents, a user and an attacker, compete in the underlying bipartite assignment graph. The user wishes to assign a set of tasks at the minimum cost, and the attacker seeks to interdict a subset of arcs to maximize the user's objective. The user assigns exactly one task per stage, and the assignment costs and interdiction impacts vary across stages. Before any stage commences in the game, the attacker can interdict arcs subject to a cardinality constraint. An interdicted arc can still be used by the user, but at an increased assignment cost. The goal is to find an optimal sequence of assignments, coupled with the attacker's optimal interdiction strategy. We prove that this problem is strongly NP‐hard, even when the attacker can interdict only one arc. We propose an exact exponential‐state dynamic‐programming algorithm for this problem as well as lower and upper bounds on the optimal objective function value. Our bounds are based on classical interdiction and robust optimization models, and on variations of the DAI game. We examine the efficiency of our algorithms and the quality of our bounds on a set of randomly generated instances. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 373–387, 2017     

舰艇防空反导自适应干扰HDP算法分析与仿真

为干扰来袭的多波次、多方向反舰导弹,提高舰艇防空反导能力,分析了自适应干扰HDP算法模型,并通过实例进行仿真。通过对结果进行分析,验证了启发式动态规划(HDP)算法在自适应电子干扰策略最优组合的生成过程中的时效性、预测精度以及适用性,为该算法的后续研究提供支持。     

Determining crane areas for balancing workload among interfering and noninterfering cranes

This article treats the problem of subdividing an area for storing containers such that the workload is evenly shared among the cranes operating the resulting subareas. We consider two crane sets: while noncrossing constraints between cranes of the same set need to be observed, cranes of different sets do not interfere. Such a problem setting is, for instance, relevant for scheduling the (un‐)loading of vessels by parallel quay cranes operating on opposing berths or in container yards with cross‐over cranes. We formalize the resulting optimization problem, prove computational complexity, and present exact and heuristic solution procedures. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

一种新的装备配置优化模型

传统的装备配置优化问题一般采用确定性的数学规划方法解决,难以满足高技术条件下现代战争中需要对大量随机现象和模糊现象进行精确定量分析的实际要求。在分析了高技术条件下装备配置问题一般特性的基础上,根据机会约束规划和模糊机会约束规划的思想,提出了一种新的建立装备配置优化模型的思路和方法,并给出了基于随机模拟的遗传算法的实值算例,算例结果与实际情况基本相符,反映了模型的科学性与实用性。     

多边形障碍环境中避障路径的遗传算法

研究了平面障碍环境中避障的遗传算法,重点介绍了障碍环境的模型构造和用遗传算法求解该模型的方法设计.采用了与已有遗传算法不同的染色体编码方式和不同的遗传算子设计,在初始群体产生时尽量避免了引入无效基因,提高了染色体进化的效率,使得该算法具有很好的寻优能力,实验结果也表明了该算法的有效性.     

基于动态规划的猎雷具识别路径优化算法

路径长度、海流方向给猎雷具航渡、操控带来的时间消耗,是影响目标识别效率的主要因素。单纯运用动态规划算法只能解决猎雷具最短识别路径的问题,而无法顾全猎雷具在操控方面的时间损耗,从而在提升作战效率上得不偿失。本文基于动态规划算法,优化了识别路径的解算模型,并在模型解算前,提出了目标位置的预处理条件,简化了模型的解算步骤;在模型解算后,提出了识别路径的修正方法,完善了模型的解算结果。     

Modeling and analyzing multiple station baggage screening security system performance

In the aftermath of the tragic events of 11 September 2001, numerous changes have been made to aviation security policy and operations throughout the nation's airports. The allocation and utilization of checked baggage screening devices is a critical component in aviation security systems. This paper formulates problems that model multiple sets of flights originating from multiple stations (e.g., airports, terminals), where the objective is to optimize a baggage screening performance measure subject to a finite amount of resources. These measures include uncovered flight segments (UFS) and uncovered passenger segments (UPS). Three types of multiple station security problems are identified and their computational complexity is established. The problems are illustrated on two examples that use data extracted from the Official Airline Guide. The examples indicate that the problems can provide widely varying solutions based on the type of performance measure used and the restrictions imposed by the security device allocations. Moreover, the examples suggest that the allocations based on the UFS measure also provide reasonable solutions with respect to the UPS measure; however, the reverse may not be the case. This suggests that the UFS measure may provide more robust screening device allocations. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

基于多目标优化的液体火箭发动机减损与延寿控制

着重分析了减损与延寿控制律的综合过程,提出其基于多目标优化方法的理论基础。在建立某型液体火箭发动机系统动力学模型、关键部件之涡轮叶片的结构分析模型及其材料损伤模型的基础上,分析了应用非线性规划法求解减损与延寿控制律的过程。对发动机起动过程实施减损与延寿控制,结果表明在系统性能略微损失的情况下,可以较大幅度地减小涡轮叶片的损伤,从而达到延长发动机工作寿命的目的。     

Crane scheduling with spatial constraints

In this work, we examine port crane scheduling with spatial and separation constraints. Although common to most port operations, these constraints have not been previously studied. We assume that cranes cannot cross, there is a minimum distance between cranes and jobs cannot be done simultaneously. The objective is to find a crane‐to‐job matching which maximizes throughput under these constraints. We provide dynamic programming algorithms, a probabilistic tabu search, and a squeaky wheel optimization heuristic for solution. Experiments show the heuristics perform well compared with optimal solutions obtained by CPLEX for small scale instances where a squeaky wheel optimization with local search approach gives good results within short times. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.