Bi-level programming model for reconstruction of urban branch road network

Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level programming model for reconstructing the branch roads was set up. The upper level model was for determining the enlarged capacities of the branch roads, and the lower level model was for calculating the flows of road sections via the user equilibrium traffic assignment method. The genetic algorithm for solving the bi-level model was designed to obtain the reconstruction capacities of the branch roads. The results show that by the bi-level model and its algorithm, the optimum scheme of urban branch roads reconstruction can be gained, which reduces the saturation of arterial roads apparently, and alleviates traffic congestion. In the data analysis the arterial saturation decreases from 1.100 to 0.996, which verifies the micro-circulation transportation's function of urban branch road network.

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.

Linear-in-Parameter Models Based on Parsimonious Genetic Programming Algorithm and Its Application to Aero-Engine Start Modeling

A novel Parsimonious Genetic Programming （PGP） algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditional Genetic Programming（GP） is used to generate the nonlinear input-output models that are represented in a binary tree structure; then, the Orthogonal Least Squares algorithm （OLS） is used to estimate the contribution of the branches of the tree （refer to basic function term that cannot be decomposed anymore according to special rule） to the accuracy of the model, which contributes to eliminate complex redundant subtrees and enhance GP＇s convergence speed; and finally, a simple, reliable and exact linear-in-parameter nonlinear model via GP evolution is obtained. The real aero-engine start process test data simulation and the comparisons with Support Vector Machines （SVM） validate that the proposed method can generate more applicable, interpretable models and achieve comparable, even superior results to SVM.

Genetic programming-based chaotic time series modeling

This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.

A TRUST REGION METHOD WITH A CONIC MODEL FOR NONLINEARLY CONSTRAINED OPTIMIZATION

Trust region methods are powerful and effective optimization methods. The conic model method is a new type of method with more information available at each iteration than standard quadratic-based methods. The advantages of the above two methods can be combined to form a more powerful method for constrained optimization. The trust region subproblem of our method is to minimize a conic function subject to the linearized constraints and trust region bound. At the same time, the new algorithm still possesses robust global properties. The global convergence of the new algorithm under standard conditions is established.

Design of a Computational Heuristic to Solve the Nonlinear Liénard Differential Model

In this study,the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks(ANNs)along with the hybridization procedures of global and local search approaches.The global search genetic algorithm(GA)and local search sequential quadratic programming scheme(SQPS)are implemented to solve the nonlinear Liénard model.An objective function using the differential model and boundary conditions is designed and optimized by the hybrid computing strength of the GA-SQPS.The motivation of the ANN procedures along with GA-SQPS comes to present reliable,feasible and precise frameworks to tackle stiff and highly nonlinear differentialmodels.The designed procedures of ANNs along with GA-SQPS are applied for three highly nonlinear differential models.The achieved numerical outcomes on multiple trials using the designed procedures are compared to authenticate the correctness,viability and efficacy.Moreover,statistical performances based on different measures are also provided to check the reliability of the ANN along with GASQPS.

Model and algorithm of optimizing alternate traffic restriction scheme in urban traffic network

An optimization model and its solution algorithm for alternate traffic restriction(ATR) schemes were introduced in terms of both the restriction districts and the proportion of restricted automobiles. A bi-level programming model was proposed to model the ATR scheme optimization problem by aiming at consumer surplus maximization and overload flow minimization at the upper-level model. At the lower-level model, elastic demand, mode choice and multi-class user equilibrium assignment were synthetically optimized. A genetic algorithm involving prolonging codes was constructed, demonstrating high computing efficiency in that it dynamically includes newly-appearing overload links in the codes so as to reduce the subsequent searching range. Moreover,practical processing approaches were suggested, which may improve the operability of the model-based solutions.

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.

Maximum likelihood estimation of nonlinear mixed-effects models with crossed random effects by combining first-order conditional linearization and sequential quadratic programming

Nonlinear mixed-eirects (NLME) modek have become popular in various disciplines over the past several decades.However,the existing methods for parameter estimation imple-mented in standard statistical packages such as SAS and R/S-Plus are generally limited k) single-or multi-level NLME models that only allow nested random effects and are unable to cope with crossed random effects within the framework of NLME modeling.In t his study,wc propose a general formulation of NLME models that can accommodate both nested and crassed random effects,and then develop a computational algorit hm for parameter estimation based on normal assumptions.The maximum likelihood estimation is carried out using the first-order conditional expansion (FOCE) for NLME model linearization and sequential quadratic programming (SCJP) for computational optimization while ensuring positive-definiteness of the estimated variance-covariance matrices of both random effects and error terms.The FOCE-SQP algorithm is evaluated using the height and diameter data measured on trees from Korean larch (L.olgeiisis var,Chang-paienA.b) experimental plots aa well as simulation studies.We show that the FOCE-SQP method converges fast with high accuracy.Applications of the general formulation of NLME models are illustrated with an analysis of the Korean larch data.

多模式耦合的城际铁路列车开行方案优化方法

随着区域经济一体化和都市圈的快速发展,城际铁路线路运营环境因各制式轨道交通的相互影响而变得更加复杂,进而对列车开行方案的编制提出更高要求。从城际铁路的视角出发,探究多模式耦合交通系统的竞争与协作机制。在竞争层面,采用多项Logit模型评估城际铁路对本线客流的吸引力;在协同层面,引入接续时间窗概念,分析城际铁路与干线轨道交通不同接续条件下的换乘过程。在此基础上,考虑客流、车站和区间能力、上座率、列车运行等约束,构建以综合客运周转量和企业效益最大化为目标的非线性整数规划模型,设计模拟退火算法求解。结果表明,多模式耦合的城际铁路列车开行方案优化方法可以提升城际铁路客运分担率至39.57%,提供81.97%的换乘联运服务,显著增强了城际铁路服务效能。

考虑运行图抽线调整的城际铁路动车周转计划编制

针对现有城际铁路动车组周转计划编制方法未考虑运行线客座率分布,导致抽线调整时难以单独停运低客座率车次而不影响高客座率车次的问题,本文提出一种适用于运行图抽线调整的动车组周转计划优化编制方法。以最小化动车组使用数量及交路中运行线客座率差异为目标,考虑满足一级修等约束条件,构建非线性整数规划模型;通过线性化处理并结合Hierholzer's算法筛选合格解的方式,高效求解模型。案例分析表明:相较于只考虑动车组使用数量的传统方法,该模型在不增加动车组使用数量的情况下,可以显著降低各交路中运行线客座率的差异。优化后的交路中运行线客座率极差提高了68.67%,交路中运行线客座率方差之和降低至对比方案的66.27%。抽线调整时,采用优化方案能够更精确地优先取消低客座率运行线,有效保留高客座率运行线。在抽线条数分别设定为1,2,3的情况下,优化方案停开的运行线中,客座率低于60%的运行线的占比均保持在77%以上,而对比方案中该比例均未超过45%;优化方案中被取消车次的平均客座率比对比方案分别降低了4.48%、5.88%和6.01%。

产品族架构设计与供应链延迟决策的主从交互优化

鉴于延迟策略的研究较少关注到产品族架构设计与整条供应链延迟决策之间存在的内在交互影响,提出对产品族架构设计与供应链延迟决策的一种主从交互优化方法。通过构建在其之间的主从交互决策机制,建立了一个以产品族架构设计为主、供应链延迟决策为从的非线性双层规划模型。模型上层是开发商设计产品族架构和决策其中的延迟产品模块类型,从而最大化单位成本的顾客效用;下层的决策主体包括多个供应商、多个制造商、多个延迟承包商及多个分销商,它们分别通过优化产品族的延迟制造过程来最小化各自的运营成本。针对模型求解的复杂性,设计了一种嵌套遗传算法进行求解。以智能冰箱产品族延迟生产案例验证所提优化模型和求解算法的可行性,并通过对多项式分对数选择规则中的参数θ进行灵敏度分析实验得出了一些管理启示。

基于SMPA的半挂车自动泊车运动规划方法研究

半挂车辆的非稳定运动学特性为其泊车过程中自主运动规划带来严峻挑战。针对半挂车在多障碍物的静态场景中泊车运动规划算法效率低、结果平滑性差等问题,本文提出了序列式运动规划方法(sequential motion planning algorithm,SMPA)。首先,提出了基于二次规划策略和改进双向快速扩展随机树(bidirectional rapidly-exploring random tree algorithm,Bi-RRT)的初始路径生成方法。然后,结合车辆非完整微分约束下的路径节点可行性判别方法研究,提出基于概率的目标偏向采样策略,提高了采样效率。最后,构建了面向车辆系统控制变量连续性的非线性最优化控制模型,解决泊车换向点的对接问题,提高了泊车轨迹平滑性。仿真结果表明,该方法在多障碍物场景中,规划时间相比Hybrid A*和Bi-RRT分别降低了86.71%和21.44%,轨迹质量也更具优越性。

NCRPE约束下基于关键链的项目群延误费用非线性优化模型

合同项目延误将有可能给项目群中其它合同项目和NCRPE削峰所带来不利影响,它不但是一种非线性关系,而且在多利益主体环境下它具有连锁和放大效应。为了降低这种不利影响程度,针对现有延误和索赔规则只适用单项目的不足,如何通过有效监控关键链项目群的缓冲,提升合同项目和项目群的按时完工率是降低业主风险的关键。首先,分析合同项目延误给项目群带来的不利影响,剖析延误费用非线性优化原理。其次,引入关键链,研究并构建延误费用非线性优化模型。最后,开展项目群Q的案例分析。研究表明,本文构建的模型能够在提高资源均衡程度的同时,实现工期和费用的双优化,有助于管理者根据工期偏好程度,在费用、工期、资源均衡及最大需求强度之间作出权衡。

基于模型预测控制与阻抗控制的受限机构操作方法

针对移动机械臂对受限运动机构操作的需要,提出一种基于模型预测控制(model predictive control,MPC)和阻抗控制(impedance control,IC)的移动机械臂全身运动规划与控制的实时方法。允许移动机械臂在操作过程中感知环境的动态变化并进行全身避障,形成使移动底盘和机械臂协调良好的运动以及使移动机械臂与环境的交互具备柔顺性。移动机械臂开门操作的仿真实验结果表明:该方法在移动机械臂全身运动规划、阻抗控制方面具备有效性,具有良好的应用前景。

基于抗扰动Smith预估补偿的磨煤机出口风粉温度优化控制

“双碳”背景下,作为高能耗高排放的火电机组,其燃烧性能直接影响发电效率和碳排放量。锅炉燃烧优化控制对运行优化和灵活调峰是有重要意义。电站锅炉磨煤机的出口风粉温度控制面临大延迟、多干扰问题,对燃煤机组安全稳定运行具有重要影响。基于Smith预估控制器,在补偿回路中增设反馈环节,实现了高效抗干扰,提升磨煤机风粉温度控制性能。通过Taylor级数展开和待定系数法求取模型近似转换参数,获得一阶惯性纯滞后模型,进而设计抗扰动型Smith预估补偿控制器。通过与单回路PID控制试验对比,发现Smith预估补偿控制算法可有效补偿控制对象的纯延迟滞后环节,单位阶跃响应超调量降低36.5%,调节时间缩短65.8%。相比经典Smith预估补偿控制算法,抗扰动Smith预估补偿控制算法可提高控制算法的抗扰动性能,并显著改善控制对象的动态特性,单位阶跃响应超调量减小44.7%,调节时间缩短15.7%。同时,实现了扰动最大偏差和收敛时间的降低。最后,在某660 MW超临界一次再热燃煤机组上应用该方法,实现了磨煤机出口风粉温度实际值对设定值的平稳跟踪,进一步验证了该方法的有效性。

显式非线性车辆稳定性预测控制模型保真度分析

为了同时兼顾车辆运动控制与控制分配,并且考虑模型可信度的影响,提出了一种显式非线性车辆稳定性预测控制模型保真度分析方法。利用多参数非线性规划问题的多参数二次规划逼近算法生成显式解,基于最优控制问题,结合运动控制和控制分配方面,提出了一种显式非线性模型预测控制车辆稳定性控制方法。进一步通过驻留试验中的客观指标对不同预测模型的控制器进行评估。最后通过仿真实验可以得到模型保真度对显式非线性模型预测车辆稳定性控制器性能的影响。

甲醇制对二甲苯反应系统的多组分耦合研究

甲醇制芳烃工艺产物成分复杂、对二甲苯含量低,耦合芳烃间转化技术能提升对二甲苯收率,但存在多组分匹配复杂、耦合非线性的难题。本文通过梳理5类反应产物集总与6种转化技术之间的对应、竞争、共生和协同关系,构建超结构模型并将之转化为非线性规划方程进行求解,提出甲醇制芳烃和芳烃间转化技术的高效耦合方法,揭示了反应产物和转化技术的多组分协调匹配机制,实现了组分中碳氢元素向对二甲苯的定向转化,为甲醇制对二甲苯的高效发展提供了思路。

基于混合遗传算法的可变尺寸货物装箱问题研究

目的针对冷链运输中的生鲜打包及装载优化问题,提出一种允许货物以体积恒定为前提进行尺寸变化的包装装载方案,以最大化集装箱的空间利用率。方法基于上述问题,构建非线性混合整数规划模型,为了方便CPLEX或LINGO等求解器对该非线性混合整数规划模型进行求解,采用一种分段线性化方法,将该非线性模型进行线性化处理。由于所研究问题具有NP-hard属性,无论是CPLEX还是LINGO都无法有效求解大规模算例,因此设计一种有效结合遗传算法与深度、底部、左部方向优先装载(Deepest bottom left with fill,DBLF)的算法。结果大小规模算例实验验证结果表明,混合遗传算法能够在合理时间内获得最优解或近似最优解。结论所提出的可变尺寸包装方案有效提高了装载率,有益于客户和物流公司。

多波束测深技术在测线问题中的优化研究

主要研究多波束测深技术实现过程中海水深度、覆盖宽度和相邻条带的重叠率的数学模型及给定待测目标海域如何确立测量船的最优测量布线问题。首先,根据测量船和待测海域信息建立二维平面直角坐标系和三维空间直角坐标系,利用直线与直线、直线与平面、平面与平面之间的位置关系建立数学模型。其次,给定待测目标海域信息,通过目标函数和约束条件建立优化模型,采用一维遍历搜索算法,并利用MATLAB软件求解,得到最优的测量线路。