Research of Enterprise Storage Ecosystem Based on Storage Theory and Nonlinear Discrete Optimization

Warehousing and transferring strategies are an important part of business operations. The issue of optimal warehousing and transferring strategy is studied in this paper. Wal-Mart in Wuhan serves as an example to establish a (s, S) random storage strategy model, a Markov chain model, and a nonlinear discrete programming model, aiming at maximizing the profit per cycle of every branch and further maximizing the company’s total profit per cycle. Among them, the random storage strategy model establishes a security zone of inventory for every branch, that is, it can meet consumers’ demand without spending too much storage costs. The Markov chain model is used to get the probability of losing sales opportunities in every branch. The nonlinear discrete programming model takes into account the horizontal transferring among branches, which further maximizes the company’s overall profit expectations. The three models above can be used to formulate inventory strategies, assess risks, and provide advice for every branch in order to form a complete storage ecosystem and provide constructive suggestions for the company’s operations.

Leader-Following Consensus for Discrete-Time Multi-Agent Systems with Parameter Uncertainties Based on the Event-Triggered Strategy

In this paper, the leader-following consensus for discrete-time nmlti-agent systems with parameter uncertainties is investigated based on the event-triggered strategy. And the parameter un- certainty is assmned to be norm-bounded. A consensus protocol is designed based on the event-triggered strategy to make the multi-agent systems achieve consensus without continuous communication among agents. Each agent only needs to observe its own state to determine its own triggering instants under the triggering function in this paper. In addition, a sufficient condition for the existence of the event- triggered consensus protocol is derived and presented in terms of the linear matrix inequality. Finally, a numerical example is given to illustrate to efficiency of the event-triggered consensus protocol proposed in this paper.

Event-Triggered Consensus for Discrete-Time Multi-agent Systems with Parameter Uncertainties Based on a Predictive Control Scheme

In this paper,the event-triggered consensus for linear discrete-time multi-agent systems with parameter uncertainties is investigated.The parameter uncertainty is assumed to be normbounded.An event-triggered consensus protocol based on the predictive control method is proposed to make the multi-agent system achieve consensus.And for the design of the consensus protocol,the problem of estimating the control input is transformed into the problem of estimating state differences between agents.Furthermore,the event-triggered consensus protocol proposed in this paper only demands each agent to mornitor its state to determine its event-triggered instants.A sufficient existence condition for the consensus protocol is proposed based on the linear matrix inequality.And a sufficient condition for the nonexistence of the Zeno-like behaviour is also derived.Finally,a numerical example is given to illustrate that the event-triggered consensus protocol proposed in this paper can make the multi-agent system with parameter uncertainties achieve consensus effectively.

OUTPUT MAXIMIZATION CONTROL FOR VSCF WIND ENERGY CONVERSION SYSTEM USING EXTREMUM CONTROL STRATEGY

The energy conversion optimization control strategy is presented for a family of horizontal-axis variablespeed fixed-pitch wind energy conversion systems,working in the partial load region.The system uses a variablespeed wind turbine(VSWT)driving a squirrel-cage induction generator(SCIG)connected to a grid.A new maximum power point tracking(MPPT)approach is proposed based on the extremum seeking control principles under the assumption that the wind turbine model and its parameters are poorly known.The aim is to drive the average position of the operation point close to optimality.Here the wind turbulence is used as search disturbance instead of inducing new sinusoidal search signals.The discrete Fourier transform(DFT)process of some available measures estimates the distance of operation point to optimality.The effectiveness of the proposed MPPT approach is validated under different operation conditions by numerical simulations in MATLAB/SIMULINK.The simulation results prove that the new approach can effectively suppress the vibration of system and enhance the dynamic performance of system.

Optimization of operational strategies for rich gas enhanced oil recovery based on a pilot test in the Bakken tight oil reservoir

Horizontal well drilling and multistage hydraulic fracturing have been demonstrated as effective approaches for stimulating oil production in the Bakken tight oil reservoir.However,after multiple years of production,primary oil recovery in the Bakken is generally less than 10%of the estimated original oil in place.Gas huff‘n’puff(HnP)has been tested in the Bakken Formation as an enhanced oil recovery(EOR)method;however,most field pilot test results showed no significant incremental oil production.One of the factors affecting HnP EOR performance is premature gas breakthrough,which is one of the most critical issues observed in the field because of the presence of interwell fractures.Consequently,injected gas rapidly reaches adjacent production wells without contacting reservoir rock and increasing oil recovery.Proper conformance control is therefore needed to avoid early gas breakthrough and improve EOR performance.In this study,a rich gas EOR pilot in the Bakken was carefully analyzed to collect the essential reservoir and operational data.A simulation model with 16 wells was then developed to reproduce the production history and predict the EOR performance with and without conformance control.EOR operational strategies,including single-and multiple-well HnP,with different gas injection constraints were investigated.The simulation results of single-well HnP without conformance control showed that a rich gas injection rate of at least 10 MMscfd was needed to yield meaningful incremental oil production.The strategy of conformance control via water injection could significantly improve oil production in the HnP well,but injecting an excessive amount of water also leads to water breakthrough and loss of oil production in the offset wells.By analyzing the production performance of the wells individually,the arrangement of wells was optimized for multiple-well HnP EOR.The multiwell results showed that rich gas EOR could improve oil production up to 7.4%by employing conformance control strategies.Furthermore,replacing rich gas with propane as the injection gas could result in 14%of incremental oil production.

Optimization Design of RC Ribbed Floor System Using Eagle Strategy with Particle Swarm Optimization

The eagle strategy algorithm is combined with particle swarm optimization in this paper.The new algorithm,denoted as the ES-PSO,is implemented by interfacing Etabs structural analysis codes.ES-PSO is used to optimize the RC ribbed floor system,including floor and underground garage roof.By considering the effects of reinforcement,the principle of virtual work is applied to calculate the deflections of components.Construction cost is taken as the objective function and the constraint conditions are required to satisfy.Accordingly,the optimal layout,the optimal sections of the beams and slabs and the corresponding reinforcements are obtained for different column grids.In this investigation,the RC ribbed floor system is optimized according to the Chinese standard,whose column grids are 8.4 m and 8.4 m.The performance of the ES-PSO algorithm is good enough,which can be applied to practical engineering.The paper can also provide a basis for subsequent optimization design of monolithic structures.

混合白鲸优化算法求解柔性作业车间调度问题

针对柔性作业车间调度问题(flexible job-shop scheduling problem,FJSP),提出一种混合白鲸优化算法(hybrid beluga whale optimization,HBWO)对其求解,旨在最小最大化完工时间。采用既定策略改进标准白鲸优化算法(beluga whale optimization,BWO),加快其收敛速度;基于机器选择和工序排序问题设计双层编码方案,解决FJSP离散化问题;采用主动编码及种群初始化策略,提高求解质量;基于工序的开始和结束时间确定关键路径和关键块,注重各工序时间维度;引入贪心思想至基于关键路径的混合变邻域搜索策略中,加大勘测搜索空间及减少无效搜索;此外,引入遗传算子防止算法陷入局部最优;通过35个标准算例的仿真实验与分析,证明了算法在求解FJSP问题中具有有效性。

基于多维空间离散基准的目标检测网络设计

针对目标检测网络中检测头的设计缺少依据,不能对不同数据集进行相应优化的问题,用一至四维的空间离散基准和正样本点(组)分配冲突率模型,提出了相应的设计依据。即基于位置的分配原则中,针对特定数据集的正样本点在4个回归量所组成的空间维度中的分布特点,在不高于指定的分配冲突率的条件下,以减少正负样本不均衡度和系统计算资源为目的,设计出具有最少点数检测头的规律。其本质是一个回归基准编码和正负样本分配设计的过程,进而实现检测性能与资源消耗之间的平衡。得出基于卷积目标检测网络检测头的回归基准可以采用4个回归中的任意组合,但需结合样本分配策略与数据集的正样本分配冲突率进行综合设计的结论。

基于离散事件模拟的航站楼运行韧性分析

提高航站楼运行韧性是减少偶然事件下人员财产损失、减轻破坏程度、尽快恢复功能的最佳办法。目前,对航站楼缺乏基于运行性能指标时变过程的韧性定量分析理论与技术。基于此,提出综合体现鲁棒性、快速性与系统性能损失的航站楼系统综合韧性指标的航站楼运行韧性理论框架,基于离散事件模拟,得出航站楼运行系统在设备损坏、人员缺席2种扰动情景及不同扰动时间、旅客到达率下的韧性变化规律,并提出提高安检效率及设备冗余率2种韧性提升策略。结果表明:系统鲁棒性指标、性能损失指标均与扰动时间及旅客到达率呈负相关;对于设备损坏和人员缺席2种情景,提升安检效率使系统综合韧性水平分别从原有的0.325、0.054提升至0.834、0.913,提升设备冗余率使系统综合韧性水平从0.22提升至0.638。

包含快照选择策略的POD-QDEIM油浸式变压器绕组瞬态温升降阶算法研究及其实验验证

为了提高采用有限元方法对油浸式电力变压器绕组瞬态温升进行仿真的计算速度,该文提出一种包含快照选择策略的本征正交分解(proper orthogonal decomposition,POD)-基于列主元QR分解的离散经验插值(QR factorization-discrete empirical interpolation method,Q-DEIM)(POD-QDEIM)算法。首先,文章基于最小二乘有限元法与迎风有限元法推导绕组流热耦合计算方程;其次,通过引入POD方法,降低有限元方程阶数提高求解效率;同时提出POD自适应快照选择策略,在保证降阶模型精度的前提下,减小快照矩阵规模;随后,为了改善POD方法求解非线性问题时效率不高的劣势,文章结合基于Q-DEIM方法,通过插值的方法形成方程中的非线性项,从而提高每一时步非线性项的形成效率;最后,基于110 kV油浸式电力变压器绕组基本结构,建立了八分区分匝绕组传热模型以及绕组温升试验装置对所提算法进行验证,仿真及实验结果表明POD-QDEIM降阶算法的计算精度在工程上属于可接受范围,其计算效率较全阶计算提升了近11.72倍,说明该算法在油浸式电力变压器绕组瞬态温升快速仿真领域的工程应用价值。

基于γ随机搜索策略的无人机集群海上任务分配

针对无人机(UAV)集群海上作战态势复杂、作战任务多样、作战单元异构的特点,建立了海上无人机集群多目标任务分配优化模型,并针对该模型提出了一种基于γ随机搜索策略的改进离散粒子群算法(γ-DPSO)。将作战态势细节与复杂作战需求等引入无人机集群任务分配问题,建立契合作战场景的无人机集群任务分配作战模型;基于粒子编码矩阵,设计均衡搜索策略、γ随机搜索策略、分阶段自适应参数,提出基于γ随机搜索策略的改进离散粒子群算法,解决离散粒子群算法易陷入局部最优造成未成熟收敛的问题。仿真结果表明:针对所建立的符合海上作战特点的无人机集群多目标任务分配优化模型,所提算法可有效解决无人机集群多目标任务分配问题,所提改进策略提高了算法的收敛速度与算法精度。

考虑清淤策略的桩基时变可靠度分析

为发展淤积岸坡-高桩码头相互作用体系结构可靠性分析方法,基于Fick第二定律推导了能考虑氯离子侵蚀效应的二维扩散模型,并采用时间离散法提出了一种综合考虑结构抗力衰减、淤积作用量化和预测,以及桩基受力变形分析的高桩码头桩基时变可靠度数值计算方法。并结合以目标可靠度为导向的清淤策略,编写了淤积岸坡-高桩码头体系时变可靠度外挂求解程序DPRP。在此基础上,开展了多工况计算分析,探讨了高桩码头桩基时变可靠度的演化规律。计算分析结果表明:码头近海侧桩基可靠度更易低于目标可靠度,在实际工程中需重点养护与修复;泥沙淤积作用会降低多数桩基时变可靠度,而对于近海侧及近岸侧且向岸倾斜的斜桩,其时变可靠度则会因泥沙淤积作用而增大。该高桩码头桩基时变可靠度数值求解流程框架及对应外挂子程序DPRP,可为高桩码头-岸坡相互作用体系的相关工程设计与可靠性评价提供参考。

Multi-UAV coordination control by chaotic grey wolf optimization based distributed MPC with event-triggered strategy

The paper proposes a new swarm intelligence-based distributed Model Predictive Control(MPC)approach for coordination control of multiple Unmanned Aerial Vehicles(UAVs).First,a distributed MPC framework is designed and each member only shares the information with neighbors.The Chaotic Grey Wolf Optimization(CGWO)method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem(FHOCP).Then,the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint.Further,an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach,which considers the predicted state errors and the convergence of cost function.Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.

基于PnP-ADMM的离散体结构拓扑优化

基于PnP-ADMM算法框架,提出了一种基于PnP-ADMM框架的离散变拓扑优化改进算法,旨在求解柔度更小的最优解。通过将PnP-ADMM框架推导目标函数和各约束的对应离散子变量的迭代式,采用敏度过滤的方法消除棋盘格,同时,应用移动限制策略逼近目标约束下的最优结构布局。通过MBB梁和悬臂梁算例实验,该方法计算得到的结构柔度与DVTOPCRA算法相比有明显下降,验证了该方法的可行性和有效性,为求解离散体结构最小化柔度提供了一种新的方法。

具有实际约束的多阶段M-V投资组合时间一致性策略研究

从动态规划的角度分析,方差算子的不可分离性导致标准的多阶段均值-方差模型的最优投资策略不满足时间一致性。文章采用条件期望映射的方法,构建了一个具有交易成本、借贷约束和阈值约束的多阶段M-V投资组合模型。由于考虑了交易成本,该模型是一个具有路径依赖性的动态优化问题。为了获得其时间一致性投资策略,文章将该问题近似地转化为连续性动态规划模型,证明最优解的近似度,并运用离散迭代算法求解。最后,使用上海证券交易所的部分历史数据验证了模型和算法的有效性。

Hybrid Adaptive Event-Triggered Consensus Control with Intermittent Communication and Control Updating

This paper studies the distributed consensus control for linear multi-agent systems under discontinuous communication and control updating.A fully distributed event-triggered adaptive control protocol with strictly positive minimum interevent time(MIET)guarantees is proposed.First,an event-triggered distributed adaptive control law without using prior global information of network topologies is presented,which achieves asymptotic consensus via discrete control updating and intermittent communication.Then,a hybrid adaptive event-triggering scheme with an internal timer is designed that is activated only when the timer decreases to zero from a specified positive value.Under the proposed triggering scheme,not only Zeno behavior is excluded but also a strictly positive MIET between any two consecutive events is guaranteed,which facilitates the physical implementation.In contrast to the existing related results,the proposed fully distributed protocol only needs low-frequency communication and control updating,while ensuring the strictly positive MIET property.Finally,a simulation example is given to illustrate the effectiveness of the theoretical results.

基于PlantSimulation的半导体机台配置优化仿真分析

半导体制造工厂的机台数量配置对产线的生产效率具有重要影响,然而传统机台配置方法不能将时间维度和复杂派工策略统筹考虑,难以量化评估机台配置对产能的影响,容易出现投资浪费、产能不足、空间布局不合理等问题。在Plant Simulation仿真软件中建立半导体制造执行系统(MES)实时派工机制,模拟工厂的真实运行状态,同时提出了一种机台配置的优化算法。将此方法用于某半导体工厂的设备配置,有效提升了产能及设备利用率,为半导体及其他行业的机台配置提供借鉴和参考。

Influence of wind-blown sand content on the mechanical quality state of ballast bed in sandy railways

During the operation of sandy railways, the challenge posed by wind-blown sand is a persistent issue. An in-depth study on the influence of wind-blown sand content on the macroscopic and microscopic mechanical properties of the ballast bed is of great significance for understanding the potential problems of sandy railways and proposing reasonable and adequate maintenance and repair strategies. Building upon existing research, this study proposes a new assessment indicator for sand content. Utilizing the discrete element method(DEM) and fully considering the complex interactions between ballast and sand particles, three-dimensional(3D) multi-scale analysis models of sandy ballast beds with different wind-blown sand contents are established and validated through field experiments. The effects of varying wind-blown sand content on the microscopic contact distribution and macroscopic mechanical behavior(such as resistance and support stiffness) of ballast beds are carefully analyzed. The results show that with the increase in sand content, the average contact force and coordination number between ballast particles gradually decrease, and the disparity in contact forces between different layers of the ballast bed diminishes. The longitudinal and lateral resistance of the ballast bed initially decreases and then increases, with a critical point at 10% sand content. At 15% sand content, the lateral resistance is mainly shared by the ballast shoulder. The longitudinal resistance sharing ratio is always the largest on the sleeper side, followed by that at the sleeper bottom, and the smallest on the ballast shoulder. When the sand content exceeds 10%, the contribution of sand particles to stiffness significantly increases, leading to an accelerated growth rate of the overall support stiffness of the ballast bed, which is highly detrimental to the long-term service performance of the ballast bed. In conclusion, it is recommended that maintenance and repair operations should be promptly conducted when the sand content of the ballast bed reaches or exceeds 10%.

基于角度信息的1比特RIS波束赋形

智能超表面(Reconfigurable Intelligent Surface,RIS)技术是第六代移动通信网络(6th generation mobile networks,6G)的关键支撑技术之一,可显著增强信号强度和质量,减轻通信干扰,提升通信性能;相较于传统有源天线阵列,RIS硬件成本低,被动工作方式带来低功耗的特性。波束赋形技术是RIS辅助通信系统的关键技术之一,过去的研究主要集中在连续相位波束赋形上,解决典型的通信波束赋形目标,包括能量效率最大化、信噪比最大化以及传输速率最大化等。这些研究取得了显著的成果,为通信系统的性能提升做出了贡献。然而,在实际应用中,由于成本和硬件方面的限制,离散波束赋形设计更受青睐。现有的离散波束赋形方案难以保证最优性,且依赖于高开销的RIS级联信道估计。在这一背景下,本文提出了两种1比特RIS波束赋形方法,包括基于分割的最优波束赋形方法和基于贪婪策略的低复杂度波束赋形方法。这两种方法分别通过巧妙的分割和迭代优化,实现了只需角度信息即可生成指定方向高增益波束的目标。具体而言,基于分割的波束赋形方法通过复平面分割的方式,巧妙地设计各RIS单元的相位,可获得最优解;基于贪婪策略的波束赋形方法,通过对各RIS单元相位迭代优化的方式,寻找1比特相位配置,能够取得接近最优的性能且具有更低的复杂度。此外,随着RIS相位误差的增加,分割法相对于贪婪法略显优势。