考虑多场景新能源预测的月度机组组合研究

为提高月度机组组合对不确定新能源的适应性,提出了一种考虑多场景新能源预测的月度机组组合方法,以提升电网新能源消纳能力。首先介绍了新能源多场景预测的基本概念,并提出了多场景新能源的表示模型。在此基础上,以预期运行成本及新能源损失电量最小化为目标,考虑电力电量平衡、网络安全、电厂合同电量等运行约束,构建了考虑多场景新能源预测的月度机组组合模型。该模型本质上为混合整数非线性规划问题,可以利用CPLES等规划程序包求解。以IEEE-30节点系统构造的算例表明,该方法能够充分考虑不确定新能源对电网运行的影响,提高月度机组组合方式对不确定性新能源消纳的鲁棒性。通过调整运行成本和新能源损失电量之间的比例系数,可以优化机组组合方式,协调系统运行经济性和节能性,提升电网运行整体效益。     

共享汽车系统空车调度的鲁棒优化模型

面向共享汽车系统的运营商与潜在用户,针对实现最大利润的空车调度问题,同时考虑乘客需求信息的不确定性对调度过程的影响,利用基于可调决策规则的鲁棒优化方法进行建模与求解.在共享汽车系统中,乘客的出行需求是不确定的,给出相应的不确定集合描述,将乘客的出行需求限制在一定的区间内,并灵活限制时间上的乘客需求之和,以减小模型的保守性.在此基础上引入可调决策规则,使得空车调度的策略可以根据已实现的需求进行调整,提出空车调度的鲁棒优化模型及其可解的线性规划形式.仿真实验利用真实的滴滴订单信息模拟用户使用共享汽车出行的需求,展示该模型所提出的空车调度策略(相较于确定性模型)会投入更多的费用在空车调度上,使运营商在平均意义和最差情况下均获得更大的利润并满足更多的乘客需求,表明所提出模型的鲁棒性和实用性.     

可持续闭环供应链网络的多目标优化研究

针对可持续的闭环5供应链网络优化问题,考虑柔性供应策略、设施改造对网络优化的影响,并以经济成本最小、碳排放量最低、社会效益最大为优化目标,构建了多目标的多情景-模糊优化模型。针对模型中的不确定参数,用三角模糊数与多情景优化的方法处理其模糊性和随机性,然后根据不同的分析角度,分别用线性加权法和NSGA-[Ⅱ]算法对模型进行求解,基于算例的求解结果,对目标权重的灵敏度、模型的稳健性以及目标间的冲突性进行了分析。证明了考虑柔性供应策略和设施改造问题的必要性;给出了闭环供应链网络设计的决策建议。     

An incomplete m-exchange algorithm for solving the large-scale multi-scenario knapsack problem

This paper introduces a fast heuristic based algorithm for the max-min multi-scenario knapsack problem. The problem is a variation of the standard 0-1 knapsack problem, in which the profits of the items vary under different scenarios, though the capacity of the knapsack is fixed. The objective of the problem is to find the optimal packing of a set of items so that the minimum total profits of the items in the knapsack over all different scenarios is maximized. For some large-scaled instances, traditional branch-and-bound techniques cannot find an optimal solution within reasonable time, thus we propose a collection of incomplete m-exchange algorithms which are able to produce high quality solutions in just a few minutes of cpu time. Various computational results are also given.     

求解多场景护士分配问题的扰动变异遗传算法

针对当前经典的护士排班问题中的一个重要分支——护士分配问题,分析了病人护理等级的特点、护士和病人的配合关系、护士技术职称等方面对护士的工作负荷的影响,建立了一个改进的随机规划模型,使模型更符合中国医院的情况。然后根据问题解的结构,设计了一个扰动变异遗传算法,在解内部的每一个向量以一定概率添加扰动实现变异。实验结果显示,与最新的随机贪心算法、基于Benders分解的启发式算法对比,扰动变异遗传算法能在30min内得到更高质量的解,为护士每班次减少超过8.9%的工作负荷。特别地,在求解多场景、多约束,而且解的优势并非块状连续的护士分配问题中,扰动变异遗传算法优势更加明显。     

基于GRU的智能手机多场景步态认证

基于步态的智能手机认证研究大多针对单一受控场景,而未考虑多场景变化对认证准确性的影响。智能手机以及用户的运动方向在不同场景下会发生变化,当使用对方向变化敏感的传感器采集用户步态数据时,可能会因场景的改变出现一定的偏差。因此,为智能手机提供一种多场景下的高精度步态认证方法已成为亟待解决的问题。此外,模型训练算法的选取是决定步态认证准确率和效率的关键。目前流行的基于长短期记忆（LSTM,longshort-termmemory）网络的认证模型能够实现较高的认证准确率,但其训练参数较多,内存占用较大,训练效率有待提升。针对现有步态认证方案未满足多场景认证需求、模型认证和训练且难以兼顾高效率与高准确率的问题,提出了基于门控循环单元（GRU,gaterecurrentunit）的智能手机多场景步态认证方案。通过小波变换对步态信号进行初步降噪处理,并采用自适应的步态周期分割算法对循环的步态信号进行切分。为满足多步态场景的认证需求,采用坐标系转换方法对步态信号进行方向无关性处理,以消除智能手机方向以及用户运动方向对认证结果的影响。为实现高准确率认证以及高效率训练模型,利用不同体系结构的GRU以及多种...     

Variations in the application of a budget of uncertainty optimization approach

It is possible to consider uncertainty simultaneously with the design process. In fact, a Budget of Uncertainty(BoU) can be determined alongside the design solution, allowing the determination of uncertainty intervals for selected design variables and problem parameters. This paper presents a new strategy for optimization under uncertainty which provides for this simultaneous design and uncertainty determination. To test the theory, a simple Taylor series expansion strategy is used to propagate uncertainty in a design problem’s objectives and constraints and a new BoU design algorithm is formulated. Due to the need for competing objectives, nominal performance and robust design, the new formulation is a multiobjective problem with primary and secondary weights to allow for lexicographic weights of uncertain parameters and variation between optimal and robust solutions. This paper compares and contrasts three different Goal Programming techniques as solutions to the multiobjective problem. Within the paper, the term Budget of Uncertainty (BoU) is used to describe the fundamental idea of uncertainty allocation across design variables and problem parameters as well as for a shorthand to describe the presented formulation. An engineering design problem, that of a helical spring, is presented to further illustrate the new method, and an uncertainty budget is considered which trades uncertainty in coil diameter against uncertainty in wire diameter.     

Controller Design Under Fuzzy Pole-Placement Specifications: An Interval Arithmetic Approach

This paper discusses fuzzy specifications for robust controller design, as a way to define different specification levels for different plants in a family and allow the control of performance degradation. Controller synthesis will be understood as mapping a fuzzy plant onto a desired fuzzy set of closed-loop specifications. In this context, a fuzzy plant is considered as a possibility distribution on a given plant space. In particular, pole placement in linear plants with fuzzy parametric uncertainty is discussed, although the basic idea is general and could be applied to other settings. In the case under consideration, the controller coefficients are the solution of a fuzzy linear system of equations with a particular semantics. Modal interval arithmetic is used to solve the system for each alpha-cut. The intersection of the solutions, if not empty, constitutes the solution to the robust control problem     

A new level-set based approach to shape and topology optimization under geometric uncertainty

Geometric uncertainty refers to the deviation of the geometric boundary from its ideal position, which may have a non-trivial impact on design performance. Since geometric uncertainty is embedded in the boundary which is dynamic and changes continuously in the optimization process, topology optimization under geometric uncertainty (TOGU) poses extreme difficulty to the already challenging topology optimization problems. This paper aims to solve this cutting-edge problem by integrating the latest developments in level set methods, design under uncertainty, and a newly developed mathematical framework for solving variational problems and partial differential equations that define mappings between different manifolds. There are several contributions of this work. First, geometric uncertainty is quantitatively modeled by combing level set equation with a random normal boundary velocity field characterized with a reduced set of random variables using the Karhunen–Loeve expansion. Multivariate Gauss quadrature is employed to propagate the geometric uncertainty, which also facilitates shape sensitivity analysis by transforming a TOGU problem into a weighted summation of deterministic topology optimization problems. Second, a PDE-based approach is employed to overcome the deficiency of conventional level set model which cannot explicitly maintain the point correspondences between the current and the perturbed boundaries. With the explicit point correspondences, shape sensitivity defined on different perturbed designs can be mapped back to the current design. The proposed method is demonstrated with a bench mark structural design. Robust designs achieved with the proposed TOGU method are compared with their deterministic counterparts.     

Empty container repositioning strategy in intermodal transport with demand switching

In this paper, we study the empty container inventory repositioning problem with customer demand switching in intermodal transport. The objective of this article is to solve the empty container repositioning problem by contract coordination theory, and to improve the coordination of empty container management and the profit of each participant. We consider an intermodal transport system composed of the rail firm and the liner firm. First, we have considered the situation of no cooperation between the dry port and the seaport, and established a model where there is only the customer demand switching without the occurrence of empty container repositioning. Next, we consider the cooperation between the dry port and the seaport in the decentralized model and the centralized model, and set up the empty container repositioning models from the seaport to the dry port respectively. We analyse the optimal inventory level for the dry port and the seaport under different models, and the effect of the repositioning price on the optimal inventory level. We then apply the contract coordination theory to the empty container inventory repositioning problem. We propose an inventory coordination strategy based on a revenue sharing contract and coordinate the intermodal transport system by choosing the appropriate contract parameters. The results of the study show that under the guidance of the seaport, the revenue sharing contract can achieve a win-win situation for the dry port and the seaport.     

集装箱海运空箱分派随机规划模型研究*

通过对海运集装箱空箱分派过程的仔细分析,在考虑需求不确定性的基础上引入了空箱运输能力的不确定性这一重要因素,建立了同时考虑供需平衡约束以及需求和空箱运输能力不确定性的空箱分派随机规划模型,并应用机会约束规划方法对模型求解.最后通过数值仿真,揭示了运输成本、租箱成本和存储成本等参数和空箱运输能力的不确定性对集装箱空箱分派策略的影响机制.     

考虑双重不确定性的公交时刻表分布鲁棒优化模型

城市公交系统由于受外界干扰,其需求和运行环境在时空上呈现高度不确定性,给日常运营组织带来了巨大挑战.为增强公交系统对于客流需求和运行场景双重不确定性的应对能力,提出一种权衡服务质量和服务鲁棒性的单一线路时刻表优化方法.方法采用离散场景集刻画需求的不确定性,并以滞留人数的期望值和条件风险值最小化为目标,综合考虑多方面约束,构建多场景耦合的分布鲁棒优化模型(DRO).为方便模型求解,运用模糊集描述场景发生概率的不确定性,再借助对偶理论和常规线性化方法将原模型转化为等价的混合整数线性规划形式.最后通过实际案例对方法进行分析,结果表明:等价转换得到的线性形式可由GUROBI优化软件快速求得最优解; DRO模型所得时刻表能有效应对双重不确定性;随着不确定性的上升,分布鲁棒优化方法相较于传统随机规划方法体现出更强的鲁棒性,可以切实改善公交系统运营的稳定性.     

判断具有多线性约束条件的凸空间是否为空的交边算法

本文研究被若干线性约束条件界定的凸空间是否为空的判断问题，此问题在线性规划中有广泛的实际背景。本文提出了一个求解此问题的交边算法，该算法已被编程实现了，实际试算表明，其平均计算时间复杂度不高，是目前求解同类问题的算法中的较优者。     

基于新的空胞腔策略的矢量量化码书设计方法

为了克服传统LBG算法中的空胞腔现象,提出了一种基于码字间距最大化的新的空胞腔策略。利用离码书距离最大的输入矢量来修改胞腔中的码字,旨在形成码字的合理分布,减小矢量量化的平均失真。实验结果表明：提出的策略能有效地消除空胞腔现象,获得性能较好的码书,其峰值信噪比比传统的LBG算法提高了3 dB。     

The robust vehicle routing problem with time windows

This paper addresses the robust vehicle routing problem with time windows. We are motivated by a problem that arises in maritime transportation where delays are frequent and should be taken into account. Our model only allows routes that are feasible for all values of the travel times in a predetermined uncertainty polytope, which yields a robust optimization problem. We propose two new formulations for the robust problem, each based on a different robust approach. The first formulation extends the well-known resource inequalities formulation by employing adjustable robust optimization. We propose two techniques, which, using the structure of the problem, allow to reduce significantly the number of extreme points of the uncertainty polytope. The second formulation generalizes a path inequalities formulation to the uncertain context. The uncertainty appears implicitly in this formulation, so that we develop a new cutting plane technique for robust combinatorial optimization problems with complicated constraints. In particular, efficient separation procedures are discussed. We compare the two formulations on a test bed composed of maritime transportation instances. These results show that the solution times are similar for both formulations while being significantly faster than the solutions times of a layered formulation recently proposed for the problem.     

基于改进YOLOv3的火灾检测与识别

现阶段火灾频发,需要自动进行火灾的检测与识别,虽然存在温度、烟雾传感器等火灾检测手段,但是检测实时性得不到保证.为了解决这一问题,提出了基于改进YOLOv3的火灾检测与识别的方法.首先构建一个多场景大规模火灾目标检测数据库,对火焰和烟雾区域进行类别和位置的标注,并针对YOLOv3小目标识别性能不足的问题进行了改进.结合深度网络的特征提取能力,将火灾检测与识别形式化为多分类识别和坐标回归问题,得到了不同场景下火焰和烟雾两种特征的检测识别模型.实验表明,本文提出的改进YOLOv3算法对不同拍摄角度、不同光照条件下的火焰和烟雾检测都能得到理想的效果,同时在检测速度上也满足了实时检测的需求.     

Uncertain programming models for portfolio selection with uncertain returns

In an indeterminacy economic environment, experts’ knowledge about the returns of securities consists of much uncertainty instead of randomness. This paper discusses portfolio selection problem in uncertain environment in which security returns cannot be well reflected by historical data, but can be evaluated by the experts. In the paper, returns of securities are assumed to be given by uncertain variables. According to various decision criteria, the portfolio selection problem in uncertain environment is formulated as expected-variance-chance model and chance-expected-variance model by using the uncertainty programming. Within the framework of uncertainty theory, for the convenience of solving the models, some crisp equivalents are discussed under different conditions. In addition, a hybrid intelligent algorithm is designed in the paper to provide a general method for solving the new models in general cases. At last, two numerical examples are provided to show the performance and applications of the models and algorithm.     

不确定分数阶多涡卷混沌系统自适应重复学习同步控制

研究了不确定分数阶多涡卷混沌系统的自适应重复学习同步控制问题.通过利用滞环函数,设计了一类参数可调的分数阶多涡卷混沌系统.针对这类分数阶多涡卷混沌系统,在考虑非参数化不确定性、周期时变参数化不确定性、常参数化不确定性和外部扰动情况下,提出了一种重复学习同步控制方案.利用自适应神经网络技术补偿了系统中的函数型不确定性,通过自适应重复学习控制技术处理了周期时变参数化不确定性,并利用自适应鲁棒学习项处理了神经网络逼近误差和干扰的影响,实现了主系统和从系统的完全同步.综合利用分数阶频率分布模型和类Lyapunov复合能量函数方法证明了同步误差的学习收敛性.数值仿真验证了所提方法的有效性.     

一种无线传感器网络能耗模型及有效性分析

提高网络的能量有效性是无线传感器网络设计的关键.针对现有的正方形网络中由于中继次数无法预知,由此带来的不确定性导致相同传输距离下不同传输路径的能耗表达式不同的问题,引入最小跳数技术,采用加权平均算法,建立了坐标系下正方形网络的能耗模型,推导出更为准确的正方形网格的能耗表达式,解决了能耗表达式模糊的问题.仿真分析表明算法...     

Searching for empty convex polygons

A key problem in computational geometry is the identification of subsets of a point set having particular properties. We study this problem for the properties of convexity and emptiness. We show that finding empty triangles is related to the problem of determining pairs of vertices that see each other in a star-shaped polygon. A linear-time algorithm for this problem which is of independent interest yields an optimal algorithm for finding all empty triangles. This result is then extended to an algorithm for finding empty convex r-gons (r> 3) and for determining a largest empty convex subset. Finally, extensions to higher dimensions are mentioned.