Solving partially observable agent‐intruder games with an application to border security problems

In this paper, we introduce partially observable agent‐intruder games (POAIGs). These games model dynamic search games on graphs between security forces (an agent) and an intruder given possible (border) entry points and high value assets that require protection. The agent faces situations with dynamically changing, partially observable information about the state of the intruder and vice versa. The agent may place sensors at selected locations, while the intruder may recruit partners to observe the agent's movement. We formulate the problem as a two‐person zero‐sum game, and develop efficient algorithms to compute each player's optimal strategy. The solution to the game will help the agent choose sensor locations and design patrol routes that can handle imperfect information. First, we prove the existence of ?‐optimal strategies for POAIGs with an infinite time horizon. Second, we introduce a Bayesian approximation algorithm to identify these ?‐optimal strategies using belief functions that incorporate the imperfect information that becomes available during the game. For the solutions of large POAIGs with a finite time horizon, we use a solution method common to extensive form games, namely, the sequence form representation. To illustrate the POAIGs, we present several examples and numerical results.     

To catch an intruder: Part A—uncluttered scenario

We analyze an interdiction scenario where an interceptor attempts to catch an intruder as the intruder moves through the area of interest. A motivating example is the detection and interdiction of drug smuggling vessels in the Eastern Pacific and Caribbean. We study two models in this article. The first considers a nonstrategic target that moves through the area without taking evasive action to avoid the interdictor. We determine the optimal location the interceptor should position itself to best respond when a target arrives. The second model analyzes the strategic interaction between the interceptor and intruder using a Blotto approach. The intruder chooses a route to travel on and the interceptor chooses a route to patrol. We model the interaction as a two‐player game with a bilinear payoff function. We compute the optimal strategy for both players and examine several extensions. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 29–40, 2017     

Optimal sensor placement for underwater threat detection

The optimization framework for optimal sensor placement for underwater threat detection has been developed. It considers single‐period and multiperiod detection models, each of which includes two components: detection algorithm and optimization problem for sensor placement. The detection algorithms for single‐period and multiperiod models are based on likelihood ratio and sequential testing, respectively. For the both models, the optimization problems use the principle of superadditive coverage, which is closely related to energy‐based and information‐based approaches. An algorithm for quasi‐regular sensor placement approximating solutions to the optimization problems has been developed based on corresponding continuous relaxations and a criterion for its applicability has been obtained. Numerical experiments have demonstrated that the algorithm consistently outperforms existing optimization techniques for optimal sensor placement.© 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

A location‐inventory model with lateral transshipments

We study an infinite horizon periodic stochastic inventory system consisting of retail outlets and customers located on a homogenous line segment. In each period, the total demand, generated by the customers on the line, is normally distributed. To better match supply and demand, we incorporate lateral transshipments. We propose a compact model in which the strategic decisions—the number and locations of retail outlets—are determined simultaneously with the operational decisions—the inventory replenishment and transshipment quantities. We find the optimal balance between the risk‐pooling considerations, which drive down the optimal number of retail outlets, and lateral transshipments, which drive up the optimal number of retail outlets. We also explore the sensitivity of the optimal number of retail outlets to various problem parameters. This article presents a novel way of integrating lateral transshipments in the context of an inventory‐location model. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

基于边界交叉法的高超声速滑翔飞行器多目标轨迹优化研究

针对高超声速滑翔飞行器复杂约束条件下多目标轨迹设计问题，基于边界交叉法和伪谱法提出了其多目标轨迹优化方法。首先，分析了高超声速滑翔飞行器复杂约束轨迹优化问题的特点，提出了多目标轨迹优化问题。然后，采用边界交叉法和伪谱法将多目标轨迹优化问题转化为一组单目标优化子问题，利用非线性规划算法分别求解。在优化过程中，将已求解子问题的解作为下一个子问题的初始值。利用上述方法求解了最大横程和最小峰值热流轨迹优化问题，仿真结果表明：本文方法能够有效搜索到优化轨迹的Pareto前沿，可以为高超声速滑翔飞行器轨迹设计提供参考。     

Aircraft routing under the risk of detection

The deterministic problem for finding an aircraft's optimal risk trajectory in a threat environment has been formulated. The threat is associated with the risk of aircraft detection by radars or similar sensors. The model considers an aircraft's trajectory in three‐dimensional (3‐D) space and represents the aircraft by a symmetrical ellipsoid with the axis of symmetry directing the trajectory. Analytical and discrete optimization approaches for routing an aircraft with variable radar cross‐section (RCS) subject to a constraint on the trajectory length have been developed. Through techniques of Calculus of Variations, the analytical approach reduces the original risk optimization problem to a vectorial nonlinear differential equation. In the case of a single detecting installation, a solution to this equation is expressed by a quadrature. A network optimization approach reduces the original problem to the Constrained Shortest Path Problem (CSPP) for a 3‐D network. The CSPP has been solved for various ellipsoid shapes and different length constraints in cases with several radars. The impact of ellipsoid shape on the geometry of an optimal trajectory as well as the impact of variable RCS on the performance of a network optimization algorithm have been analyzed and illustrated by several numerical examples. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

基于多项式设计法的小推力轨迹设计

形状逼近法是小推力轨迹设计中的一种有效方法,然而现有的方法大都假定运动轨迹为某一特定的形状,而且没有考虑推力加速度的约束限制。针对小推力轨道交会问题,提出一种基于多项式的轨迹设计方法。结合极坐标系,建立基于多项式的三自由度轨迹运动模型,将轨迹设计问题转化为求解多项式的系数问题;根据运动模型推导轨迹的动力学特性,建立约束方程,并以消耗燃料最少作为性能指标,采用序列二次规划的方法对多项式的系数进行寻优计算。该方法不仅能求解多个形状设计参数不确定性问题,而且还能满足推力加速度的约束限制。仿真验证了该方法的正确性和可用性,该方法可为任务设计初步阶段的轨迹设计和燃料消耗预估提供一定的技术参考。     

An infiltration game with time dependent payoff

The problem of assigning patrol boats, subject to resource constraints, to capture or delay an infiltrator with perishable contraband attempting escape across a long, narrow strait is formulated as a two-sided time sequential game. Optimal mixed strategies are derived for the situation of one patrol boat against one smuggler. Procedures for obtaining numerical solutions for R > 1 patrol boats are discussed.     

A combination of Lagrangian relaxation and column generation for order batching in steelmaking and continuous‐casting production

This article considers the order batching problem in steelmaking and continuous‐casting production. The problem is to jointly specify the slabs needed to satisfy each customer order and group all the slabs of different customer orders into production batches. A novel mixed integer programming model is formulated for the problem. Through relaxing the order assignment constraints, a Lagrangian relaxation model is then obtained. By exploiting the relationship between Lagrangian relaxation and column generation, we develop a combined algorithm that contains nested double loops. At the inner loop, the subgradient method is applied for approximating the Lagrangian dual problem and pricing out columns of the master problem corresponding to the linear dual form of the Lagrangian dual problem. At the outer loop, column generation is employed to solve the master problem exactly and adjust Lagrangian multipliers. Computational experiments are carried out using real data collected from a large steel company, as well as on large‐scaled problem instances randomly generated. The results demonstrate that the combined algorithm can obtain tighter lower bound and higher quality solution within an acceptable computation time as compared to the conventional Lagrangian relaxation algorithm. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

基于RCS的三维低可探测性轨迹优化方法

针对基于雷达散射截面(RCS)规避雷达威胁的飞行轨迹优化问题,提出了低可探测性三维轨迹优化的求解方法.通过B样条拟合构建连续可微的RCS数据模型,结合三维飞行动力学模型,建立规避雷达威胁下的飞行运动控制模型.将轨迹优化问题描述成为最优控制问题,其中飞行姿态控制、轨迹约束、边界条件作为约束条件,以降低雷达探测概率和减少飞行时间为目标函数.运用高斯伪谱法( GPM)将连续的最优控制问题转换为离散的非线性规划问题进行求解.仿真结果证明本文方法实现了求解单基地雷达和双基地雷达探测环境中低可探测性三维轨迹优化问题,有效降低了飞行过程中的雷达探测概率和暴露时间.     

混合遗传算法及其在运载火箭最优上升轨道设计中的应用

运载火箭最优上升轨道设计问题是一类终端时刻未定、终端约束苛刻的最优控制问题,经典算法求解这类问题时收敛性差、局部收敛等问题表现得比较突出。针对上述问题,将具有良好全局收敛性的遗传算法应用到运载火箭最优上升段设计问题求解中,为了提高遗传算法的收敛速度和克服早熟问题,结合遗传算法和单纯型算法的优点,设计了两种混合遗传算法。计算结果表明,所设计的混合遗传算法是求解复杂问题的有效全局优化方法,可以成功地解决一类终端时刻可变飞行器最优控制问题。     

Optimal patrol to uncover threats in time when detection is imperfect

Consider a patrol problem, where a patroller traverses a graph through edges to detect potential attacks at nodes. An attack takes a random amount of time to complete. The patroller takes one time unit to move to and inspect an adjacent node, and will detect an ongoing attack with some probability. If an attack completes before it is detected, a cost is incurred. The attack time distribution, the cost due to a successful attack, and the detection probability all depend on the attack node. The patroller seeks a patrol policy that minimizes the expected cost incurred when, and if, an attack eventually happens. We consider two cases. A random attacker chooses where to attack according to predetermined probabilities, while a strategic attacker chooses where to attack to incur the maximal expected cost. In each case, computing the optimal solution, although possible, quickly becomes intractable for problems of practical sizes. Our main contribution is to develop efficient index policies—based on Lagrangian relaxation methodology, and also on approximate dynamic programming—which typically achieve within 1% of optimality with computation time orders of magnitude less than what is required to compute the optimal policy for problems of practical sizes. © 2014 Wiley Periodicals, Inc. Naval Research Logistics, 61: 557–576, 2014     

On sequencing with earliest starts and due dates with application to computing bounds for the (n/m/G/Fmax) problem

Recent efforts to improve lower bounds in implicit enumeration algorithms for the general (n/m/G/F_max) sequencing problem have been directed to the solution of an auxiliary single machine problem that results from the relaxation of some of the interference constraints. We develop an algorithm that obtains optimal and near optimal solutions for this relaxed problem with relatively little computational effort. We report on computational results achieved when this method is used to obtain lower bounds for the general problem. Finally, we show the equivalence of this problem to a single machine sequencing problem with earliest start and due date constraints where the objective is to minimize the maximum lateness.     

高超声速再入轨迹跟踪控制的微分变换方法

针对多约束条件下高超声速飞行器再入制导问题,提出一种基于微分变换法求解最优反馈控制的全状态标准轨迹跟踪制导律。利用滚动时域控制方法设计易于在线执行的闭环跟踪制导策略,在每个制导周期内将标准轨迹跟踪问题转化为线性时变系统状态调节器问题,并通过最优控制理论进一步转化为两点边值问题,采用微分变换法进行求解获得最优反馈控制律。数值仿真表明微分变换法的引入有效解决了传统两点边值问题求解的数值不稳定性与耗时问题,所设计的闭环制导律对状态偏差与模型不确定性具有较强的鲁棒性,可为工程设计提供有益参考。     

Balancing a one‐way corridor capacity and safety‐oriented reliability: A stochastic optimization approach for metro train timetabling

In urban rail transit systems of large cities, the headway and following distance of successive trains have been compressed as much as possible to enhance the corridor capacity to satisfy extremely high passenger demand during peak hours. To prevent train collisions and ensure the safety of trains, a safe following distance of trains must be maintained. However, this requirement is subject to a series of complex factors, such as the uncertain train braking performance, train communication delay, and driver reaction time. In this paper, we propose a unified mathematical framework to analyze the safety‐oriented reliability of metro train timetables with different corridor capacities, that is, the train traffic density, and determine the most reliable train timetable for metro lines in an uncertain environment. By employing a space‐time network representation in the formulations, the reliability‐based train timetabling problem is formulated as a nonlinear stochastic programming model, in which we use 0‐1 variables to denote the time‐dependent velocity and position of all involved trains. Several reformulation techniques are developed to obtain an equivalent mixed integer programming model with quadratic constraints (MIQCP) that can be solved to optimality by some commercial solvers. To improve the computational efficiency of the MIQCP model, we develop a dual decomposition solution framework that decomposes the primal problem into several sets of subproblems by dualizing the coupling constraints across different samples. An exact dynamic programming combined with search space reduction strategies is also developed to solve the exact optimal solutions of these subproblems. Two sets of numerical experiments, which involve a relatively small‐scale case and a real‐world instance based on the operation data of the Beijing subway Changping Line are implemented to verify the effectiveness of the proposed approaches.     

A branch‐and‐price approach for the stochastic generalized assignment problem

In this article, we address a stochastic generalized assignment machine scheduling problem in which the processing times of jobs are assumed to be random variables. We develop a branch‐and‐price (B&P) approach for solving this problem wherein the pricing problem is separable with respect to each machine, and has the structure of a multidimensional knapsack problem. In addition, we explore two other extensions of this method—one that utilizes a dual‐stabilization technique and another that incorporates an advanced‐start procedure to obtain an initial feasible solution. We compare the performance of these methods with that of the branch‐and‐cut (B&C) method within CPLEX. Our results show that all B&P‐based approaches perform better than the B&C method, with the best performance obtained for the B&P procedure that includes both the extensions aforementioned. We also utilize a Monte Carlo method within the B&P scheme, which affords the use of a small subset of scenarios at a time to estimate the “true” optimal objective function value. Our experimental investigation reveals that this approach readily yields solutions lying within 5% of optimality, while providing more than a 10‐fold savings in CPU times in comparison with the best of the other proposed B&P procedures. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 131–143, 2014     

基于Gauss伪谱法和直接打靶法结合的月球定点着陆轨道优化

将一种求解最优控制问题的新方法—高斯伪谱法( Gauss Pseudospectral Method-GPM)和传统的直接打靶法有效结合,对月球着陆器定点软着陆轨道快速优化问题做出了研究.推导了高精度模型下着陆动力学方程.针对优化方法各自的特点和多约束条件下最优月球软着陆轨道设计的难点,提出了问题求解的串行优化策略:将控制变量和终端时间一同作为优化变量,同时离散控制变量与状态变量,取较少的Gauss节点,利用GPM求解初值,初值的求解采用从可行解到最优解的串行优化策略;在Gauss节点上离散控制变量,利用直接打靶法求解精确最优解.仿真结果表明,本文提出的轨道优化方法具有较强的鲁棒性和快速收敛性.     

Random disambiguation paths for traversing a mapped hazard field

We consider the problem of safely and swiftly navigating through a spatial arrangement of potential hazard detections in which each detection has associated with it a probability that the detection is indeed a true hazard. When in close proximity to a detection, we assume the ability—for a cost—to determine whether or not the hazard is real. Our approach to this problem involves a new object, the random disambiguation path (RDP), which is a curve‐valued random variable parametrized by a binary tree with particular properties. We prove an admissibility result showing that there is positive probability that the use of an RDP reduces the expected traversal length compared to the conventional shortest zero‐risk path, and we introduce a practically computable additive‐constant approximation to the optimal RDP. The theoretical considerations are complemented by simulation and example. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

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.     

Scheduling policies for an antiterrorist surveillance system

This article concerns scheduling policies in a surveillance system aimed at detecting a terrorist attack in time. Terrorist suspects arriving at a public area are subject to continuous monitoring, while a surveillance team takes their biometric signatures and compares them with records stored in a terrorist database. Because the surveillance team can screen only one terrorist suspect at a time, the team faces a dynamic scheduling problem among the suspects. We build a model consisting of an M/G/1 queue with two types of customers—red and white—to study this problem. Both types of customers are impatient but the reneging time distributions are different. The server only receives a reward by serving a red customer and can use the time a customer has spent in the queue to deduce its likely type. In a few special cases, a simple service rule—such as first‐come‐first‐serve—is optimal. We explain why the problem is in general difficult and we develop a heuristic policy motivated by the fact that terrorist attacks tend to be rare events. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009