多目标线性规划的几何模型求解法

提出多目标线性规划求解的一种新方法:几何模型求解法(简称GMM)。GMM以作者已有的研充为基础,在求出全部可行域顶点一次得到多目标函数值后,建立决策空间的几何模型,决策者用此模型进行决策分析,找到决策空间中使所有目标函数值都达到令决策者满意的某一点。该法有利于求解大规模多目标线性规划问题。

利用三维模型快速求解岩层产状的方法

岩层产状一般可利用机械罗盘或电子罗盘进行测量,当岩层位于高陡地段,无法手持罗盘进行测量时可采用三点法,即通过全站仪或其它测量手段测量岩层面上不在同一直线上的3个点来求解岩层产状.传统的三点法一般通过公式计算求得产状,求解过程繁琐,不直观.本文研究了一种利用三维模型快速求解岩层产状的方法,并利用公式法进行了验算,验算结果表明三维模型求解岩层产状的方法直观简单,结果可靠.

基于吸收光谱原理的高温水汽浓度测量方法研究

可调谐半导体激光吸收光谱（tunable diode laser absorption spectroscopy,简称为TDLAS）技术具有高灵敏度、快速响应、非接触式、环境适应性强等优点,能够实现燃烧温度、组分浓度、速度等参量的实时动态在线测量。为准确测量高温下的水汽浓度,采用窄带半导体激光器作为光源,结合实验室的高温测量系统,记录了常压下1.39μm附近水汽在773~1 273K温度范围内的吸收光谱,利用多线组合非线性最小二乘法拟合得到高温吸收光谱的吸光度,找出了两条适合高温水汽浓度测量的吸收线7 154.35和7 157.73cm^-1,首次提出高温水汽浓度测量的模型求解方法,该方法测得的高温下水汽浓度符合理论推理,浓度测量的标准误差低于0.2%,相对误差低于6%。通过实验验证了该测量方法的可行性。

Perturbed Solving Method for Interdecadal Sea-air Oscillator Model

A coupled system of the interdecadal sea-air oscillator model is studied. The E1 Nifio-southem oscillation （ENSO） atmospheric physics oscillation is an abnormal phenomenon involved in the tropical Pacific ocean-atmosphere interactions. The oscillator model is involved with the variations of both the eastern and western Pacific anomaly pat- terns. This paper proposes an ENSO atmospheric physics model using a method of the perturbation theory. The aim is to create an asymptotic solving method for the ENSO model. Employing the perturbed method, the asymptotic solution of corresponding problem is obtained, and the asymptotic behaviour of the solution is studied. Thus we can obtain the prognoses of the sea surface temperature anomaly and related physical quantities.

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.

Performance Prediction of Structured Packing Column for Cryogenic Air Separation with Hybrid Model

A detailed investigation of a thermodynamic process in a structured packing distillation column is of great impor- tance in prediction of process efficiency. In order to keep the simplicity of an equilibrium stage model and the accu- racy of a non-equilibrium stage model, a hybrid model is developed to predict the structured packing column in cryogenic air separation. A general solution process for the equilibrium stage model is developed to solve the set of equations of the hybrid model, in which a separation efficiency function is introduced to obtain the resulting tri-diagonal matrix and its solution by the Thomas algorithm. As an example, the algorithm is applied to analyze an upper column of a cryogenic air separation plant with the capacity of 17000 m3·h-1. Rigorous simulations are conducted using Aspen RATEFRAC module to validate the approach. The temperature and composition distributions are in a good agreement with the two methods. The effects of inlet/outlet position and flow rate on the temperature and composition distributions in the column are analyzed. The results demonstrate that the hybrid model and the solution algorithms are effective in analvzin~ the distillation process for a a cryogenic structured packing column.

A GA approach to vehicle routing problem with time windows considering loading constraints

As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with the objective of scheduling multiple transport routes considering loading constraints along with time penalty function to minimize the total cost. Then a genetic algorithm( GA) is developed. The specific encoding and genetic operators for FVRPTW are devised.Especially,in order to accelerate its convergence,an improved termination condition is given. Finally,a case study is used to evaluate the effectiveness of the proposed algorithm and a series of experiments are conducted over a set of finished vehicle routing problems. The results demonstrate that the proposed approach has superior performance and satisfies users in practice. Contributions of the study are the modeling and solving of a complex FVRPTW in logistics industry.

Analysis of characteristic functions for equivalent circuit model in monolithic transformer

A model of monolithic transformers is presented, which is analyzed with characteristic functions. A closed- form analytical approach to extract all the model parameters for the equivalent circuit of Si-based on-chip transformers is proposed. A novel de-coupling technique is first developed to reduce the complexity in the Y parameters for the transformer, and the model parameters can then be extracted analytically by a set of characteristic functions. Simulation based on the extracted parameters has been carried out for transformers with different structures, and good accuracy is obtained compared to a 3-demensional full-wave numerical electro- magnetic field solver. The presented approach will be very useful to provide a scalable and wide-band compact circuit model for Si-based RF transformers.

Pseudo-invariant Eigen-Operator Method for Solving Field-Intensity-Dependent Jaynes-Cummings Model

By using Gumming （JC） model. energy-level gap of this the pseudo invariant eigen-operator method we The pseudo-invariant eigen-operator is found in JC Hamiltonian is derived. This approach seems analyze the field-intensity-dependent Jaynes terms of the supersymmetric generators. The concise.

A scenario relaxation algorithm for finite scenario based robust assembly line balancing

A balancing problem for a mixed model assembly line with uncertain task processmg Ume anO daily model mixed changes is considered, and the objective is to minimize the work variances between stations in the line. For the balancing problem for the scenario-based robust assembly line with a finitely large number of potential scenarios, the direct solution methodology considering all potential scenarios is quite time-consuming. A scenario relaxation algorithm that embeds genetic al- gorithm is developed. This new algorithm guarantees termination at an optimal robust solution with relatively short running time, and makes it possible to solve robust problems with large quantities of potential scenarios. Extensive computational results are reported to show the efficiency and effectiveness of the proposed algorithm.

Reinforcement Learning Algorithm for Solving Load Commitment Problem Considering a General Load Model

Under the smart grid paradigm, in the near future all consumers will be exposed to variable pricing schemes introduced by utilities. Hence, there is a need to develop algorithms which could be used by the consumers to schedule their loads. In this paper, load scheduling problem is formulated as a LCP （load commitment problem）. The load model is general and can model atomic and non-atomic loads. Furthermore, it can also take into consideration the relative discomfort caused by delay in scheduling any load. For this purpose, a single parameter ＂uric＂ is introduced in the load model which captures the relative discomfort caused by delay in scheduling a particular load. Guidelines for choosing this parameter are given. All the other parameters of the proposed load model can be easily specified by the consumer. The paper shows that the general LCP can be viewed as multi-stage decision making problem or a MDP （Markov decision problem）. RL （reinforcement learning） based algorithm is developed to solve this problem. The efficacy of the algorithm is investigated when the price of electricity is available in advance as well as for the case when it is random. The scalability of the approach is also investigated.

Application of homotopy perturbation method to solve two models of delay differential equation systems

In this paper, two delay differential systems are considered, namely, a famous model from mathematical biology about the spread of HIV viruses in blood and the advanced Lorenz system from mathematical physics. We then apply the homotopy perturbation method （HPM） to find their approximate solutions. It turns out that the method gives rise to easily obtainable solutions. In addition, residual error functions of the solutions are graphed and it is shown that increasing the parameter n in the method improves the results in both cases.