Expansion planning for waste‐to‐energy systems using waste forecast prediction sets

We consider an expansion planning problem for Waste‐to‐Energy (WtE) systems facing uncertainty in future waste supplies. The WtE expansion plans are regarded as strategic, long term decisions, while the waste distribution and treatment are medium to short term operational decisions which can adapt to the actual waste collected. We propose a prediction set uncertainty model which integrates a set of waste generation forecasts and is constructed based on user‐specified levels of forecasting errors. Next, we use the prediction sets for WtE expansion scenario analysis. More specifically, for a given WtE expansion plan, the guaranteed net present value (NPV) is evaluated by computing an extreme value forecast trajectory of future waste generation from the prediction set that minimizes the maximum NPV of the WtE project. This problem is essentially a multiple stage min‐max dynamic optimization problem. By exploiting the structure of the WtE problem, we show this is equivalent to a simpler min‐max optimization problem, which can be further transformed into a single mixed‐integer linear program. Furthermore, we extend the model to optimize the guaranteed NPV by searching over the set of all feasible expansion scenarios, and show that this can be solved by an exact cutting plane approach. We also propose a heuristic based on a constant proportion distribution rule for the WtE expansion optimization model, which reduces the problem into a moderate size mixed‐integer program. Finally, our computational studies demonstrate that our proposed expansion model solutions are very stable and competitive in performance compared to scenario tree approaches. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 47–70, 2016     

Risk‐sensitive sizing of responsive facilities

We develop a risk‐sensitive strategic facility sizing model that makes use of readily obtainable data and addresses both capacity and responsiveness considerations. We focus on facilities whose original size cannot be adjusted over time and limits the total production equipment they can hold, which is added sequentially during a finite planning horizon. The model is parsimonious by design for compatibility with the nature of available data during early planning stages. We model demand via a univariate random variable with arbitrary forecast profiles for equipment expansion, and assume the supporting equipment additions are continuous and decided ex‐post. Under constant absolute risk aversion, operating profits are the closed‐form solution to a nontrivial linear program, thus characterizing the sizing decision via a single first‐order condition. This solution has several desired features, including the optimal facility size being eventually decreasing in forecast uncertainty and decreasing in risk aversion, as well as being generally robust to demand forecast uncertainty and cost errors. We provide structural results and show that ignoring risk considerations can lead to poor facility sizing decisions that deteriorate with increased forecast uncertainty. Existing models ignore risk considerations and assume the facility size can be adjusted over time, effectively shortening the planning horizon. Our main contribution is in addressing the problem that arises when that assumption is relaxed and, as a result, risk sensitivity and the challenges introduced by longer planning horizons and higher uncertainty must be considered. Finally, we derive accurate spreadsheet‐implementable approximations to the optimal solution, which make this model a practical capacity planning tool.© 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Strategic capacity decision‐making in a stochastic manufacturing environment using real‐time approximate dynamic programming

In this study, we illustrate a real‐time approximate dynamic programming (RTADP) method for solving multistage capacity decision problems in a stochastic manufacturing environment, by using an exemplary three‐stage manufacturing system with recycle. The system is a moderate size queuing network, which experiences stochastic variations in demand and product yield. The dynamic capacity decision problem is formulated as a Markov decision process (MDP). The proposed RTADP method starts with a set of heuristics and learns a superior quality solution by interacting with the stochastic system via simulation. The curse‐of‐dimensionality associated with DP methods is alleviated by the adoption of several notions including “evolving set of relevant states,” for which the value function table is built and updated, “adaptive action set” for keeping track of attractive action candidates, and “nonparametric k nearest neighbor averager” for value function approximation. The performance of the learned solution is evaluated against (1) an “ideal” solution derived using a mixed integer programming (MIP) formulation, which assumes full knowledge of future realized values of the stochastic variables (2) a myopic heuristic solution, and (3) a sample path based rolling horizon MIP solution. The policy learned through the RTADP method turned out to be superior to polices of 2 and 3. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

Route optimization for multiple searchers

We consider a discrete time‐and‐space route‐optimization problem across a finite time horizon in which multiple searchers seek to detect one or more probabilistically moving targets. This article formulates a novel convex mixed‐integer nonlinear program for this problem that generalizes earlier models to situations with multiple targets, searcher deconfliction, and target‐ and location‐dependent search effectiveness. We present two solution approaches, one based on the cutting‐plane method and the other on linearization. These approaches result in the first practical exact algorithms for solving this important problem, which arises broadly in military, rescue, law enforcement, and border patrol operations. The cutting‐plane approach solves many realistically sized problem instances in a few minutes, while existing branch‐and‐bound algorithms fail. A specialized cut improves solution time by 50[percnt] in difficult problem instances. The approach based on linearization, which is applicable in important special cases, may further reduce solution time with one or two orders of magnitude. The solution time for the cutting‐plane approach tends to remain constant as the number of searchers grows. In part, then, we overcome the difficulty that earlier solution methods have with many searchers. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

无人机系统自主控制技术研究现状与发展趋势

无人机系统是未来进行信息对抗、夺取信息优势、实施火力打击的重要手段。"自主性"是无人机系统区别于有人机最重要的技术特征,实现无人机系统的自主控制,提高其智能程度,是无人机系统的重要发展趋势。对无人机系统自主控制问题进行了阐述,首先分析了无人机系统自主控制技术的发展需求,然后介绍了自主控制的概念和自主等级的划分;分析了无人机系统自主控制技术的研究现状,提出了无人机系统自主控制的关键技术问题,主要包括体系结构、感知与认知、规划与控制、协同与交互等;最后对无人机系统自主控制技术的发展趋势进行了展望。     

巡航导弹航迹规划中雷达探测盲区的快速构造算法

现代防空系统对巡航导弹的低空飞行和突防造成极大的威胁,利用防空系统中预警雷达的探测盲区进行隐蔽飞行是提高巡航导弹生存能力的重要手段,在此雷达探测盲区的快速构造算法是关键。说明了什么是雷达探测盲区,分析了影响雷达探测盲区的三个主要因素,并就对巡航导弹影响最大的雷达地形遮蔽盲区构造了基于极坐标的快速算法,并用一个地形实例验证了该方法的有效性。     

地心甚高轨道星座构形协同捕获控制策略

针对地心甚高轨道星座构形协同捕获控制问题,基于虚拟编队方法设计了协同捕获控制策略,采用三脉冲燃耗最优轨迹规划算法对构形捕获轨迹进行协同规划;并且结合自适应全程积分滑模控制器对卫星各自转移轨迹进行跟踪控制。以10万km轨道高度的三星星座构形捕获为例进行仿真验证,仿真结果表明:该策略可以有效应用于地心甚高轨道星座构形捕获控制,能够在燃耗较少的情况下使星座中卫星同时到达各自的标称位置,同时具有较高的精度。     

一种基于多Agent强化学习的多星协同任务规划算法

在分析任务特点和卫星约束的基础上给出了多星协同任务规划问题的数学模型.引入约束惩罚算子和多星联合惩罚算子对卫星Agent原始的效用值增益函数进行改进,在此基础上提出了一种多卫星Agent强化学习算法以求解多星协同任务分配策略,设计了基于黑板结构的多星交互方式以降低学习交互过程中的通信代价.通过仿真实验及分析证明该方法能...     

空地多目标序贯攻击三维最优航迹实现

单机或多机协同执行对地多目标攻击任务将成为未来空地战争的一种重要形式。制定了合适的分段规划策略,建立了水平面和垂直面的空地多目标序贯攻击最优航迹模型,应用经典的可行方向法对模型求解。根据水平面和垂直面规划的相互关系及规划结果,得到三维最优航迹,为飞行导航提供有效依据。最后通过实例验证了所建模型及方法的有效性。     

建筑设计消防审核常见技术问题探析

针对建筑设计消防审核工作中容易出现的技术问题,从消防车道与建筑中人行通道的设置、自动喷水灭火系统末端试水装置与试水阀的设置、特殊场所快速响应喷头的选用、标准喷头的设置、自动喷水灭火系统集热板的应用、室内消火栓系统阀门的设置以及防火卷帘两侧火灾探测器组与警报装置的设置等方面解读了相关消防技术规范的规定,系统分析了建筑设计消防审核中的常见错误与问题,提出了有关技术规范规定的合理解释和使用建议。