An Alternative Approach for Solving Bi-Level Programming Problems

An algorithm is proposed in this paper for solving two-dimensional bi-level linear programming problems without making a graph. Based on the classification of constraints, algorithm removes all redundant constraints, which eliminate the possibility of cycling and the solution of the problem is reached in a finite number of steps. Example to illustrate the method is also included in the paper.

Bi-Level Programming for the Optimal Nonlinear Distance-Based Transit Fare Structure Incorporating Principal-Agent Game

The urban transit fare structure and level can largely affect passengers’travel behavior and route choices.The commonly used transit fare policies in the present transit network would lead to the unbalanced transit assignment and improper transit resources distribution.In order to distribute transit passenger flow evenly and efficiently,this paper introduces a new distance-based fare pattern with Euclidean distance.A bi-level programming model is developed for determining the optimal distance-based fare pattern,with the path-based stochastic transit assignment(STA)problem with elastic demand being proposed at the lower level.The upper-level intends to address a principal-agent game between transport authorities and transit enterprises pursing maximization of social welfare and financial interest,respectively.A genetic algorithm(GA)is implemented to solve the bi-level model,which is verified by a numerical example to illustrate that the proposed nonlinear distance-based fare pattern presents a better financial performance and distribution effect than other fare structures.

Optimal Control for a Class of Complex Singular System Based on Adaptive Dynamic Programming

This paper presents a new design approach to achieve decentralized optimal control of high-dimension complex singular systems with dynamic uncertainties. Based on robust adaptive dynamic programming(robust ADP) method, controllers for solving the singular systems optimal control problem are designed. The proposed algorithm can work well when the system model is not exactly known but the input and output data can be measured. The policy iteration of each controller only uses their own states and input information for learning,and do not need to know the whole system dynamics. Simulation results on the New England 10-machine 39-bus test system show the effectiveness of the designed controller.

A Lagrange Relaxation Based Approach to Solve a Discrete-Continous Bi-Level Model

In this work we propose a solution method based on Lagrange relaxation for discrete-continuous bi-level problems, with binary variables in the leading problem, considering the optimistic approach in bi-level programming. For the application of the method, the two-level problem is reformulated using the Karush-Kuhn-Tucker conditions. The resulting model is linearized taking advantage of the structure of the leading problem. Using a Lagrange relaxation algorithm, it is possible to find a global solution efficiently. The algorithm was tested to show how it performs.

Adaptive path planning for unmanned aerial vehicles based on bi-level programming and variable planning time interval

This paper presents an adaptive path planner for unmanned aerial vehicles （UAVs） to adapt a real-time path search procedure to variations and fluctuations of UAVs’ relevant performances, with respect to sensory capability, maneuverability, and flight velocity limit. On the basis of a novel adaptability-involved problem statement, bi-level programming （BLP） and variable planning step techniques are introduced to model the necessary path planning components and then an adaptive path planner is developed for the purpose of adaptation and optimization. Additionally, both probabilistic-risk-based obstacle avoidance and performance limits are described as path search constraints to guarantee path safety and navigability. A discrete-search-based path planning solution, embedded with four optimization strategies, is especially designed for the planner to efficiently generate optimal flight paths in complex operational spaces, within which different surface-to-air missiles （SAMs） are deployed. Simulation results in challenging and stochastic scenarios firstly demonstrate the effectiveness and efficiency of the proposed planner, and then verify its great adaptability and relative stability when planning optimal paths for a UAV with changing or fluctuating performances.

基于改进对偶动态规划的产销者点对点交易全分散式出清方法

分布式资源的增长使得电力用户逐步从消费者转变为产销者。主要针对产销者点对点(peer-to-peer,P2P)交易全分散式出清问题开展研究。首先,以产销者总成本最小为目标,综合考虑过网费与网络约束,建立产销者P2P交易模型。其次,基于多阶段优化理论将P2P交易模型转化为全分散式优化模型,并以节点累积偏移电压和线路累积使用容量为状态变量,清晰地刻画每一对交易对配电网运行的影响情况。接着,利用对偶动态规划(dual dynamic programming,DDP)进行求解。为了提高求解效率,采用帕累托最优割技术对DDP进行改进,以减少冗余Benders割的产生,在保证算法精度的同时提高其收敛速度。最后,通过算例分析验证了所提方法的有效性。

智能合约真的会冲击合同法吗?

智能合约依托区块链得以实现,但受自身特征限制,无法成为合同法的未来。编写智能合约,需要将自然语言转换为编程语言,技术门槛较高,还会产生额外的成本与风险。区块链为智能合约赋予了匿名性与不可变动性,前者会阻碍当事人产生信任、限制司法介入、逃避国家监管,后者则导致当事人无法及时应对不可预见的变动。智能合约的自动执行性与不可变动性结合,导致当事人只能获得事后救济,消灭了合同履行中的弹性,影响效率。智能合约依赖预言机实现人机交互,但这消解了其无需人工介入的特征。混合链与预言机的运用可以改进智能合约,但几乎完全消灭了它的特征。智能合约可以影响设计合同的思路,但它最适合小额、即时、标准化的数字产品或服务交易,故意义有限。

Efficient Origin-Destination Estimation Using Microscopic Traffic Simulation with Restricted Rerouting

Traffic simulators are utilized to solve a variety of traffic-related problems.For such simulators,origin-destination(OD)traffic volumes as mobility demands are required to input,and we need to estimate them.The authors regard an OD estimation as a bi-level programming problem,and apply a microscopic traffic simulation model to it.However,the simulation trials can be computationally expensive if full dynamic rerouting is allowed,when employing multi-agent-based models in the estimation process.This paper proposes an efficient OD estimation method using a multi-agent-based simulator with restricted dynamic rerouting to reduce the computational load.Even though,in the case of large traffic demand,the restriction on dynamic rerouting can result in heavier congestion.The authors resolve this problem by introducing constraints of the bi-level programming problem depending on link congestion.Test results show that the accuracy of the link traffic volume reproduced with the proposed method is virtually identical to that of existing methods but that the proposed method is more computationally efficient in a wide-range or high-demand context.

A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

Demand response(DR)using shared energy storage systems(ESSs)is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU)price.A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this study.In this scheme,the interactions between selfish users and an operator are characterized as a Stackelberg game.Operator holds a large-scale ESS that is shared among users in the form of energy transactions.It sells energy to users and sets the selling price first.It maximizes its profit through optimal pricing and ESS dispatching.Users purchase some energy from operator for the reduction of their demand charges after operator's selling price is announced.This game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization model.The upper-level optimization maximizes operator's profit and the lower-level optimization minimizes users'costs.The bi-level model is transformed and linearized into a mixed-integer linear programming(MILP)model using the mathematical programming with equilibrium constraints(MPEC)method and model linearizing techniques.Case studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.

基于ADP的状态约束互联非线性系统的分散镇定

针对一类含有常数型状态约束的互联非线性系统,提出一种基于自适应动态规划(adaptive dynamic programming,ADP)的分散镇定方法.引入边界函数对原系统进行坐标变换,将状态约束系统转化为无约束系统.对转化后的系统构造独立子系统和改进的代价函数,将鲁棒分散镇定问题转化为最优调节问题.构建局部评判神经网络并采用策略迭代算法求解哈密顿-雅可比-贝尔曼(Hamilton-Jacobi-Bellman,HJB)方程,进而得到近似最优镇定律.通过李雅普诺夫稳定性理论证明了本文所提方法可使闭环互联系统和局部评判神经网络估计误差动态最终一致有界.数值仿真结果验证了所提出分散镇定方法的有效性.

Bus frequency optimization in a large-scale multi-modal transportation system:integrating 3D-MFD and dynamic traffic assignment

A properly designed public transport system is expected to improve traffic efficiency.A high-frequency bus service would decrease the waiting time for passengers,but the interaction between buses and cars might result in more serious congestion.On the other hand,a low-frequency bus service would increase the waiting time for passengers and would not reduce the use of private cars.It is important to strike a balance between high and low frequencies in order to minimize the total delays for all road users.It is critical to formulate the impacts of bus frequency on congestion dynamics and mode choices.However,as far as the authors know,most proposed bus frequency optimization formulations are based on static demand and the Bureau of Public Roads function,and do not properly consider the congestion dynamics and their impacts on mode choices.To fill this gap,this paper proposes a bi-level optimization model.A three-dimensional Macroscopic Fundamental Diagram based modeling approach is developed to capture the bi-modal congestion dynamics.A variational inequality model for the user equilibrium in mode choices is presented and solved using a double projection algorithm.A surrogate model-based algorithm is used to solve the bi-level programming problem.

基于干扰泄露控制和深度学习的分布式多点协作传输设计与优化

分布式多点协作传输在提升小区边缘用户的服务质量和系统频谱效率上具有巨大潜力,但通常站间频繁交换大量信息才能接近集中式多点协作传输的性能。然而,在实际无线通信系统中,考虑到信道时变性,频繁的信息交换和站间有限的回传带宽导致的较大时延给分布式多点协作传输技术带来严重的性能损失,因此其目前未得到广泛部署。为解决此问题,提出了一种新颖的信号质量衡量标准,称为信号平均泄漏加干扰噪声比(SALINR)。基于SALINR的设计使得站间无需信息交换即可实现完全分布式的联合传输。具体地,展示了基于SALINR的传输设计问题可以通过分式规划技术有效求解;进一步地,利用深度展开技术来设计波束赋形向量学习网络,从而有效减少计算复杂度。仿真结果表明,所提出的方法可接近集中式多点协作传输的性能且具有计算高效的优点。

Stochastic Bi-level Programming Model for Home Healthcare Scheduling Problems Considering the Degree of Satisfaction with Visit Time

Home health care(HHC)includes a wide range of healthcare services that are performed in customers'homes to help them recover.With the constantly increasing demand for health care,HHC policymakers are eager to address routing and scheduling problems from the perspective of optimization.In this paper,a bi-level programming model for HHC routing and scheduling problems with stochastic travel times is proposed,in which the degree of satisfaction with the visit time is simultaneously considered.The upper-level model is formulated for customer assignment with the aim of minimizing the total operating cost,and the lower-level model is formulated as a routing problem to maximize the degree of satisfaction with the visit time.Consistent with Stackelberg game decision-making,the trade-off relationship between these two objectives can be achieved spontaneously so as to reach an equilibrium state.A three-stage hybrid algorithm combining an iterated local search framework,which uses a large neighborhood search procedure as a sub-heuristic,a set-partitioning model,and a post-optimization method is developed to solve the proposed model.Numerical experiments on a set of instances including 10 to 100 customers verify the effectiveness of the proposed model and algorithm.

财政分权和公共卫生服务:山东省四县区结核病控制筹资分析

结核病是危害我国居民健康的主要公共卫生问题之一。本研究调查了山东省四个县区结核病卫生筹资现状 ,结果表明 ,经济欠发达地区比富裕地区结核病负担重 ,但他们的筹资能力却相对有限 ,政府对结核病控制项目的支持力度不够 ,向结核病患者收费成为补偿财政投入不足的主要手段。经济欠发达地区结核病卫生服务提供与富裕地区相比也有一定的差距。建立财政转移支付制度 ,提高对贫困地区公共卫生的投入 ,是结核病控制项目正常开展的重要保证。

水电厂参与市场竞争下的市场模拟及其市场势力削弱

为评估水电厂参与市场竞价时的市场势力问题,建立了多时段市场模拟模型,模型由动态规划法求解,各时段各状态下的决策为多厂商博弈的均衡点,各均衡点由迭代算法求解。通过比较分散调度与统一调度下计算结果的方法评估市场势力。算例表明,从中长期看,水电厂分散调度比统一调度电价更高且波动性大,采取双边合同可明显降低实时电价、减弱水电厂的市场势力。

基于回归神经网络多机械臂运动控制研究

该文将多协作冗余机械臂的运动控制问题转化成一个可完全分解的约束二次求解问题,并将该约束二次求解问题拆分成一系列独立的子问题,每个子问题对应于单个机械臂的运动控制模型,其大大简化了多协作机械臂的运动控制模型。另外,该文采用对偶回归神经网络求解该运动控制模型,每个独立的神经网络核对应于一个机械臂的控制模型。该文首次将对偶回归神经网络用于多冗余机械臂系统的控制问题中,具有很强的应用研究意义。与现有的对偶回归神经网络相比,该文提出的对偶回归神经网络结构非常特殊,其多项性能将大大提高,使得多协作机械臂的运动控制问题变成完全分布式。理论验证结果表明,该文提出的模型是全局稳定的并且可得到全局最优解。仿真实验结果表明本论文提出的方案是有效的。该文的研究结果可为多协作冗余机械臂的控制理论和技术发展提供新的思路。

含风电场电力系统的动态经济调度分散随机优化方法

针对含风电场电力动态经济调度的信息私密性和不确定性问题,提出一种分散随机优化求解方法。根据目标级联分析的原理,将含风电场的动态经济调度集中优化模型转化为随机分散优化模型。利用近似动态规划对各个区域的随机动态经济调度问题进行分散优化,并根据区域间边界节点相角信息对它们的优化方案进行协调。通过6节点2区域系统、IEEE多区域系统和某实际系统的算例分析,从多个角度验证了所提方法的有效性。

不确定环境下分散控制供应链物流计划优化

供应链物流计划是供应链管理的重要内容.针对节点无限扩展的分散控制供应链物流计划问题,在提出供应链元概念的基础上,考虑供应链节点企业上下游物料价格要素的随机性,应用随机机会约束规划理论,建立了多级多节点多产品分散控制供应链一体化物流计划模型;设计了基于随机模拟的混合智能算法求解模型;并以算例仿真验证了模型和算法的有效性,同时确定了算法参数合理的取值范围;最后通过仿真对模型参数进行了灵敏度分析.

基于离散双层规划的枢纽航线网络构建方法研究

针对枢纽航线网络的构建问题,充分考虑了航空公司和旅客双方的利益、旅客间决策的独立性,建立了枢纽航线网络离散双层规划模型,并为了在决策过程中达到整体目标的最优,协调顾客的独立决策,克服多层规划模型求解的复杂性和困难性,提出相应的交互式补偿模糊算法,最后通过构建由15个城市组成的枢纽航线网络,验证了模型的有效性和算法的简单实用性。

考虑大规模光伏和风电接入的主动配电网无功电源综合规划

不断攀升的可再生能源渗透率造成配电网电压越限问题突出。提出了一种基于综合时序灵敏度法与无功补偿候选节点聚类分区相结合的无功电源接入选址及容量优化配置方法。首先,提出应用综合时序无功电压灵敏度法确定无功补偿接入候选节点;接着,对配网节点进行聚类分区,以确定无功补偿的选址;此外,为了实现多种无功补偿电源的主动控制,还制定了三级无功电压运行控制策略以确定各种措施的运用顺序;最后,对改进的IEEE 33节点算例进行仿真分析,结果表明规划结果能有效解决间歇性分布式电源大量融入后引起的电压越限问题,并有较好的经济性。