Decomposition of Mathematical Programming Models for Aircraft Wing Design Facilitating the Use of Dynamic Programming Approach

Aircraft designers strive to achieve optimal weight-reliability tradeoffs while designing an aircraft. Since aircraft wing skins account for more than fifty percent of their structural weight, aircraft wings must be designed with utmost care and attention in terms of material types and thickness configurations. In particular, the selection of thickness at each location of the aircraft wing skin is the most consequential task for aircraft designers. To accomplish this, we present discrete mathematical programming models to obtain optimal thicknesses either to minimize weight or to maximize reliability. We present theoretical results for the decomposition of these discrete mathematical programming models to reduce computer memory requirements and facilitate the use of dynamic programming for design purposes. In particular, a decomposed version of the weight minimization problem is solved for an aircraft wing with thirty locations (or panels) and fourteen thickness choices for each location to yield an optimal minimum weight design.

THE APPLICATION OF THE BRANCH AND BOUND METHOD FOR DETERMINING THE MINIMUM FLOW OF A TRANSPORT NETWORK

Blockage is a kind of phenomenon frequently occurred in a transport network, in which the human beings are the moving subjects. The minimum flow of a network defined in this paper means the maximum flow quantity through the network in the seriously blocked situation. It is an important parameter in designing and operating a transport network, especially in an emergency evacuation network. A branch and bound method is presented to solve the minimum flow problem on the basis of the blocking flow theory and the algorithm and its application are illustrated by examples.

Attractor-Based Simultaneous Design of the Minimum Set of Control Nodes and Controllers in Boolean Networks

Design of control strategies for gene regulatory networks is a challenging and important topic in systems biology. In this paper, the problem of finding both a minimum set of control nodes (control inputs) and a controller is studied. A control node corresponds to a gene that expression can be controlled. Here, a Boolean network is used as a model of gene regulatory networks, and control specifications on attractors, which represent cell types or states of cells, are imposed. It is important to design a gene regulatory network that has desired attractors and has no undesired attractors. Using a matrix-based representation of BNs, this problem can be rewritten as an integer linear programming problem. Finally, the proposed method is demonstrated by a numerical example on a WNT5A network, which is related to melanoma.

A Dynamic Programming Approach to the Design of Composite Aircraft Wings

A light and reliable aircraft has been the major goal of aircraft designers. It is imperative to design the aircraft wing skins as efficiently as possible since the wing skins comprise more than fifty percent of the structural weight of the aircraft wing. The aircraft wing skin consists of many different types of material and thickness configurations at various locations. Selecting a thickness for each location is perhaps the most significant design task. In this paper, we formulate discrete mathematical programming models to determine the optimal thicknesses for three different criteria: maximize reliability, minimize weight, and achieve a trade-off between maximizing reliability and minimizing weight. These three model formulations are generalized discrete resource-allocation problems, which lend themselves well to the dynamic programming approach. Consequently, we use the dynamic programming method to solve these model formulations. To illustrate our approach, an example is solved in which dynamic programming yields a minimum weight design as well as a trade-off curve for weight versus reliability for an aircraft wing with thirty locations (or panels) and fourteen thickness choices for each location.

Mathematical Model and Simulation of Harmful Invasive Plant Control

Plant invasion refers to the phenomenon that some plants grow too fast due to they are far away from the original living environment or predators, affecting the local environment. With the development of tourism and trade, the harm caused by invasive plants will be more and more serious. Therefore, it is necessary to ex- plore an effective method for controlling plant invasion through qualitative and quan- titative research. In this paper, the models were established for the early and late harmful plant invasion control. The huge computation was completed by the com- puter programming to obtain the optimal solutions of the models. The real meaning of the optimal solution was further discussed. Through numerical simulations and discussion, it could be concluded that the quantitative research on the invasive plant control had a certain application value.

A Novel Method for the Application of the ECMS(Equivalent Consumption Minimization Strategy)to Reduce Hydrogen Consumption in Fuel Cell Hybrid Electric Vehicles

Fuel cell hybrid electric vehicles are currently being considered as ideal means to solve the energy crisis and global warming in today’s society.In this context,this paper proposes a method to solve the problem related to the dependence of the so-called optimal equivalent factor(determined in the framework of the equivalent consumption minimum strategy-ECMS)on the working conditions.The simulation results show that under typical conditions(some representative cities being considered),the proposed strategy can maintain the power balance;for different initial battery’s states of charge(SOC),after the SOC stabilizes,the fuel consumption is 5.25 L/100 km.

A Weighted Goal Programming Model for the DASH Diet Problem: Comparison with the Linear Programming DASH Diet Model

A Linear Programming DASH diet model for persons with hypertension has previously been formulated and daily minimum cost diet plans that satisfy the DASH diets’ tolerable intake level of the nutrients for 1500 mg a day Sodium level and different daily calorie levels were obtained using sample foods from the DASH diet eating plan chart. But the limitation in the use of linear programming model in selecting diet plans to meet specific nutritional requirements which normally results in the oversupply of certain nutrients was evident in the linear programming DASH diet plan obtained as the nutrient level of the diet plans obtained had wide deviations of from the DASH diets’ tolerable upper and lower intake level for the given calorie and sodium levels. Hence the need for a model that gives diet plans with minimized nutrients’ level deviations from the DASH diets’ tolerable intake level for different daily calorie and sodium level at desired cost. A weighted Goal Programming DASH diet model that minimizes the daily cost of the DASH eating plan as well as deviations of the diets’ nutrients content from the DASH diet’s tolerable intake levels is hereby presented in this work. The formulated weighted goal programming DASH diet model is further illustrated using chosen sample foods from the DASH food chart as used in the work on the linear programming DASH diet model for a 1500 mg sodium level and 2000 calories a day diet plan as well as for 1800, 2200, 2400, 2600, 2800 and 3000 daily calorie levels. A comparison of the DASH nutrients’ composition of the weighted Goal Programming DASH diet plans and those of the linear programming DASH diet plans were carried out at this sodium level and the different daily calorie levels. It was evident from the results of the comparison that the weighted goal programming DASH diet plans has minimized deviations from the DASH diet’s tolerable intake levels than those of the linear programming DASH diet plans.

Minimum Dwell Time for Global Exponential Stability of a Class of Switched Positive Nonlinear Systems

This paper will investigate global exponential stability analysis for a class of switched positive nonlinear systems under minimum dwell time switching, whose nonlinear functions for each subsystem are constrained in a sector field by two odd symmetric piecewise linear functions and whose system matrices for each subsystem are Metzler. A class of multiple time-varying Lyapunov functions is constructed to obtain the computable sufficient conditions on the stability of such switched nonlinear systems within the framework of minimum dwell time switching.All present conditions can be solved by linear/nonlinear programming techniques. An example is provided to demonstrate the effectiveness of the proposed result.

Sequential quadratic programming-based non-cooperative target distributed hybrid processing optimization method

The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.

A Complex Algorithm for Solving a Kind of Stochastic Programming

Considering that the probability distribution of random variables in stochastic programming usually has incomplete information due to a perfect sample data in many real applications, this paper discusses a class of two-stage stochastic programming problems modeling with maximum minimum expectation compensation criterion (MaxEMin) under the probability distribution having linear partial information (LPI). In view of the nondifferentiability of this kind of stochastic programming modeling, an improved complex algorithm is designed and analyzed. This algorithm can effectively solve the nondifferentiable stochastic programming problem under LPI through the variable polyhedron iteration. The calculation and discussion of numerical examples show the effectiveness of the proposed algorithm.

基于CCP-AGABP双层综合能源系统调度策略

针对综合能源系统(IES)中可再生能源与电热冷负荷因不确定影响引起功率偏差问题,实现IES优化调度,将机会约束规划(CCP)的随机模型和自适应遗传算法改进的反向传播神经网络(AGABP)的预测模型相结合,提出了双层IES优化调度策略。上层利用CCP对日前优化调度中的不确定性问题进行处理,缓解因可再生能源与多能负荷预测误差引起的功率较大偏差的问题,下层基于AGABP的预测模型进行日内优化,对日前优化调度偏差进行修正。针对随机优化模型难以求解的问题,采用确定性等价类的方法,将模型中的机会约束等价转换为确定性约束,并通过交替方向乘子法(ADMM)对改进的模型进行优化求解。最后,通过算例仿真验证了所提调度策略的有效性。

Minimum Living Cost Program in Zhejiang

The promulgation of the Rules represents a crucial step toward institutionalization and standardization of the minimum cost of living program, which marks the end of the arbitrariness in the traditional relief behavior. In a country, to what degree the citizens enjoy human rights, it depends on the law. The minimum cost of living system has to be ensured by law in its progress toward institutionalization and standardization. This has been testified by the history of the development of the social security systems, either in Germany, which is regarded as a pioneer in modern social security systems or the United States.

Practical Application of Out-of-Kilter Algorithm

The algorithm under this name, together with the variants, is a method that solves the problems of optimal flow and costs. Examples of such problems are planning and procurement, scheduling by contractors, distribution and supply systems, transport on the road or rail network, electricity transmission, computer and telecommunications networks, pipe transmission systems (water, oil, …), and the like. The main goal of any business organization is to increase profits and satisfy its customers. Because business is an integral part of our environment, their goals will be limited by certain environmental factors and economic conditions. The out-of-kilter algorithm is used to solve a complex allocation problem involving interactive and conflicting personal choices subject to interactive resource constraints. The paper presents an example of successful use of this algorithm and proposes an extension to the areas of corporate and social planning. Customer demand, warehousing, and factory capacity were used as input for the model. First, we propose a linear programming approach to determine the optimal distribution pattern to reduce overall distribution costs. The proposed model of linear programming is solved by the standard simplex algorithm and the Excel-solver program. It is noticed that the proposed model of linear programming is suitable for finding the optimal distribution pattern and total minimum costs.

基于加权最小绝对值状态估计的有源配电网拓扑辨识方法

在配电系统状态估计中,一方面众多网络设备难以完全监控导致量测数据不足,另一方面,频繁的拓扑切换亟须开发高效的算法。因此,提出了一种配电网拓扑辨识的加权最小绝对值状态估计方法。首先,对传统基于线性规划的加权最小绝对值状态估计方法进行改进,以提高计算效率;其次,在重新制定的加权最小绝对值状态估计方法中加入补充变量和约束,以适应拓扑辨识问题,形成了一个基于混合整数线性规划的加权最小绝对值状态估计方法;最后,4个算例系统的仿真结果验证了所提方法在不同数量的实时测量、高伪测量误差、被坏数据破坏的测量以及未知支路状态场景下的优异性能。

盐环定扬黄灌区五泵站运行优化研究

梯级泵站担负着农业和生活供水的重要任务,运行中消耗巨大能量,因此,提高泵站效率,降低能耗,对国民经济具有重大意义。为合理选择泵站机组在不同工况下的组合方式,实现单级泵站的运行优化,通过分析水泵运行特性以及管路损失特性,建立泵站能耗优化数学模型。采用动态规划法,以泵站运行总能耗最低作为目标函数,确定约束条件和递推方程,编写MATLAB动态规划程序对模型求解,能够计算出最优的开机组合。以陕甘宁盐环定扬黄灌区五泵站为研究对象,计算结果表明:采用动态规划法,利用计算机语言,可以快速并准确地在满足约束条件下所有的水泵开机组合型式中寻找出最优的开机组合,实现泵站的能耗减少,提高泵站的运行效率。建立的数学模型具有较强的实用性和通用性,可以为泵站的运行提供最优决策,对提高泵站的经济效益具有一定指导意义和应用价值。

基于CCP的ECU在线编程技术的实现

针对车身ECU升级困难的现状,在分析IAP技术的基础上,提出一种基于CAN总线,采用CCP协议实现ECU在线升级的设计方案,并给出在单片机XC167CI的具体实现。阐述上位机文件下载工具中对加载文件的处理方法和下位机bootloader的实现方法。实验结果表明,该系统可以通过CAN总线实现ECU的应用代码的更新功能,且操作简单、运行稳定。

基于多目标线性规划的本体冲突消除方法

针对本体逻辑冲突消除传统方法未充分利用本体逻辑性质的缺陷,引入Shapley值法刻画本体逻辑性质,构建基于多目标0-1整数线性规划的本体冲突消除方法,使用分层序列法求解多目标线性规划模型。其解遵循理性放弃原则,避免传统方法中目标权重的平均性或随机性。实验结果表明,采用该方法能够令解集遵循理性放弃原则的前提下,利用本体逻辑性质有效减少解集数量,从而提高决策效率。

STAR-RIS辅助ISAC系统波束赋形优化方法

本文提出了将通信感知一体化(Integrated Sensing and Communication,ISAC)系统与同时透射和反射可重构智能表面(Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces,STAR-RIS)相结合的系统模型,解决了传统可重构智能表面RIS不能实现全空间通信与感知的问题;同时考虑在STAR-RIS上安装低成本的专用传感器以实现ISAC系统在STAR-RIS上执行目标感知的一种新颖有源架构,解决了雷达感知的严重路径损耗问题.本文旨在联合优化ISAC基站处的波束赋形和STAR-RIS的无源波束赋形,以最大化通信用户的加权和速率(Weighted Sum Rate,WSR),同时保证感知性能的最低信噪比(signal-to-noise ratio,SNR).为了解决该复杂非凸优化问题,交替优化基站波束赋形及STAR-RIS无源波束赋形.针对所提的满足雷达感知SNR最低要求下最大化WSR问题,基站处波束赋形的优化子问题等价为加权最小均方误差(Weighted Minimum Mean Square Error,WMMSE)问题,STAR-RIS处无源波束赋形优化子问题等价为分式规划(Fractional Programming,FP)问题.进一步,分别将优化的非凸子问题转化为二次约束二次规划(Quadratic Constraint Quadratic Programming,QCQP),并使用半正定松弛(Semidefinite Relaxation,SDR)技术将它们分别转化为凸的半正定规划(Semidefinite Programming,SDP)子问题进行迭代求解.仿真结果验证了所采用新型STAR-RIS辅助ISAC方案的优点和所提算法在提高WSR性能上的有效性.

基于CCP和多智能体优化算法的微电网经济运行

随着社会的发展,电力系统正在从传统的集中式结构网络向分布式结构的智能电网转变。分布式电源(DG)并网对于分布式结构的微电网调度尤其重要。为了使微电网在安全可靠供电的同时经济运行,采用机会约束规则(CCP)和DG多智能体一致性原则,建立了微网经济优化模型。考虑到风电、光伏发电系统存在很强的随机性,在求解时采用随机模拟技术。选择一个主DG来控制增量成本,并将DG增量成本作为一致性变量;采用基于随机模拟技术的粒子群算法(PSO),求解包含随机变量的规划问题。对微网5个光伏DG的仿真验证结果表明:在不同负荷下,采用该模型进行优化计算,达到增量成本一致的效果,符合供需平衡。仿真结果验证了该模型能够满足微网经济运行的需求。

青梅汁啤酒生产中CCP难点分析与有效实施探讨

青梅汁啤酒是都市消费新时尚,其安全问题是大众关注的焦点,对青梅汁啤酒生产过程进行品质安全管控并建立体系十分必要,通过对食品安全管理体系中CCP点和HACCP计划的分析,确立了青梅汁啤酒生产过程中原料验收(包括麦芽、大米、青梅汁及啤酒瓶)、发酵、杀菌和成品检验4个关键控制点,同时制定出对应的关键限值,可以有效控制和提升产品品质与安全管理水平,预防食品风险的发生。