考虑风险关联的项目风险应对策略选择方法

提出解决考虑风险关联的项目风险应对策略选择问题的优化方法.首先给出风险关联的定义及风险关联程度的度量方法;其次,综合考虑项目工期、质量、项目风险应对成本3个因素,构建考虑风险关联的项目风险应对策略选择优化模型,通过求解模型得到满意的风险应对策略;最后,通过算例分析验证方法的可行性和有效性.研究结果表明,风险关联对项目风险应对决策有影响,在项目管理过程中需要考虑风险之间的关联作用.     

Gradient-enhanced response surface building

Response surfaces are frequently used as approximations in sequential optimization techniques, multidisciplinary optimization and reliability oriented analysis. In this paper several techniques will be exploited to construct response surfaces using both function values and corresponding derivatives, i.e. gradient-enhanced response surfaces (GERS). The way response surfaces are constructed on both function values and derivatives is not well crystallized. In particular how information on function values and derivatives must be weighted against each other is a point of discussion. To a certain extent, the best method seems to depend on the purpose of the response surface. The technique that is studied in this paper treats the response surface building as a multi-objective optimization problem. This enables a well-controlled balance between accuracy of the approximate gradients as well as the approximate function values. The method is studied for a series of test problems and compared to other techniques. This paper addresses in particularly the effect of the plan of experiment on accuracy.     

Modeling,analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches,based on finite element results

Recently, twist extrusion has found extensive applications as a novel method of severe plastic deformation for grain refining of materials. In this paper, two prominent predictive models, response surface method and artificial neural network (ANN) are employed together with results of finite element simulation to model twist extrusion process. Twist angle, friction factor and ram speed are selected as input variables and imposed effective plastic strain, strain homogeneity and maximum punch force are considered as output parameters. Comparison between results shows that ANN outperforms response surface method in modeling twist extrusion process. In addition, statistical analysis of response surface shows that twist extrusion and friction factor have the most and ram speed has the least effect on output parameters at room temperature. Also, optimization of twist extrusion process was carried out by a combination of neural network model and multi-objective meta-heuristic optimization algorithms. For this reason, three prominent multi-objective algorithms, non-dominated sorting genetic algorithm, strength pareto evolutionary algorithm and multi-objective particle swarm optimization (MOPSO) were utilized. Results showed that MOPSO algorithm has relative superiority over other algorithms to find the optimal points.     

Approximate global optimization with convexity estimation of response surface using Kriging method

This paper describes a new method for approximate global optimization using convexity estimation of a multi-peaked or partially non-convex response surface. This method is based on convexity estimation of a response surface and a cell-based clustering technique. Convexity of an approximated function is estimated from the Hessian matrix and its eigenvalue. For this purpose, a Kriging-based convexity estimation method is also introduced in this paper. At first, a formulation for the convexity estimation with the Kriging method is provided. The convexity of an objective function at each location is estimated without using a finite difference based technique. With using this convexity estimation and a cell-based clustering technique, convex clusters are constructed in a solution space. The global optimization is performed with iterative local optimization to the convex clusters. From the numerical results, validity and effectiveness of the proposed method are confirmed.     

A consistent formulation for imposing packaging constraints in shape optimization using Vertex Morphing parametrization

This paper aims at imposing no-penetration condition over arbitrary surfaces which act as bounding surfaces, also known as packaging constraints, on the design surface of shape optimization problem. We use Vertex Morphing technique for the shape parametrization. Vertex Morphing is a consistent surface control approach for node-based shape optimization. The suitability of this technique has been assessed and demonstrated for a wide range of engineering applications without geometric shape constraints. In this contribution, a consistent formulation is presented for the implementation of numerous point-wise geometric constraints in four main steps. First, a potential contact between optimization surface points and the bounding surface is identified via the so-called gap function. Second, the shape gradients of objective functions and active constraints are mapped onto the Vertex Morphing’s control space, where the optimization problem is formulated. Third, the linear least squares method is used to project the steepest-descent search direction onto the subspace tangent to the mapped active constraints. Finally, the feasible design update is mapped onto the geometry space. To verify the perfect consistency between the geometry space (where the constraints are formulated) and the control space (where the optimization problem is solved) two applications of CFD shape optimization in the automotive industry are presented.     

Topology and parameter optimization of a foaming jig reinforcement structure by the response surface method

A dilemma in the foaming of inner polyurethane (PU) pieces for household refrigerators is that of keeping the production costs down without adversely affecting the dimension precision. One way to do this is to reduce the electric power consumption spent running the polyurethane foaming line in which a number of heavy foaming jigs are continuously circulating, by optimally designing the reinforcement structure of the jig. In this paper, topology optimization and parameter optimization utilizing the response surface method (RSM) are applied for the optimum design of the jig reinforcement structure, in order to minimize the total jig weight while securing the dimension precision of the foamed urethane case. Both the reliability of the approximated response surfaces and the validity of the proposed optimization procedure are verified through illustrative optimization experiments. In addition, it is confirmed that the proposed procedure provides us with an optimum reinforcement structure which remarkably reduces the total jig weight.     

基于CMMI的软件项目风险缓解策略优化模型研究

在基于CMMI的软件工程实践过程中，风险管理被集成在CMMI框架下的第三级，与过程管理融于同一个框架之中。因此，基于CMMI的软件项目风险管理能够得到来自组织的有效信息，使软件开发朝着有规律、可预测的方向发展。本文针对目前基于CMMI的软件项目风险管理尚缺乏有效技术支持的现状，初步探讨了风险响应计划的制定问题，并针对实际提出一种风险缓解策略优化模型，为软件项目管理人员的风险决策提供辅助手段。     

凸轮磨恒线速加工过程曲线优化

为了提高凸轮轴的加工效率和加工精度,开发可用于凸轮轴磨削修整系统的动态优化模块。根据凸轮轴恒线速磨削原理,在Matlab中完成了数学模型的建模,采用三次样条曲线拟合插值法对加工过程曲线进行了优化,从而得到了波动更小的速度曲线并减少了恒线速度磨削所引起的联动轴响应问题。实验表明,该方法很好地实现了在二次磨削中对凸轮轴表面轮廓的修整,提高了凸轮轴的表面质量。     

An investigation of structural optimization in crashworthiness design using a stochastic approach

In this paper the response surface methodology (RSM) and stochastic optimization (SO) are compared with regard to their efficiency and applicability in crashworthiness design. Optimization of simple analytic expressions and optimization of a front rail structure are the applications used to assess the respective qualities of both methods. A low detail vehicle structure is optimized to demonstrate the applicability of the methods in engineering practice. The investigations reveal that RSM is better compared to SO for fewer than 10–15 design variables. The convergence behaviour of SO improves compared to RSM when the number of design variables is increased. A novel zooming method is proposed which improves the convergence behaviour. A combination of both the RSM and the SO is efficient, stochastic optimization could be used in order to determine appropriate starting points for an RSM optimization, which continues the optimization. Two examples are investigated using this combined method.     

An approximate optimization strategy using refined hybrid metamodel

An approximate model called metamodel or surrogate model is a mathematical model that numerically approximates response of a system during an engineering simulation process or test. The introduction of a metamodel makes it possible to express response defined in the design problem as a simple mathematical function of design variables. A metamodel can be built with response surface method (RSM), kriging, neural network, radial basis function, and so on. Each method has its advantages and disadvantages. A combined metamodel called hybrid model, ensemble model, or multiple surrogates has been developed to maximize each metamodel's strength. The hybrid model of this research includes RSM and kriging. Besides, a strategy to refine the hybrid metamodel is implemented by reducing design space. In this process, information related to Hessian is utilized for an unconstrained optimization problem, on the contrary feasibility for a constrained optimization problem. This research presents a new hybrid metamodel-based optimization strategy called refined hybrid metamodel. Five mathematical test problems, two-bar design, spring design, and propeller shaft design problems are solved with the suggested method, verifying its usefulness. Most of the optimal results with the proposed method are closer to exact solutions with smaller function evaluations than existing methods.     

Data-driven,using design of dynamic experiments,versus model-driven optimization of batch crystallization processes

A new data-driven experimental design methodology, design of dynamic experiments (DoDE), is proposed as a means of developing a response surface model that can be used to effectively optimize batch crystallization processes. This data-driven approach is especially useful for complex processes for which it is difficult or impossible to develop a knowledge-driven model in a timely fashion for the optimization of an industrial process. Design of dynamic experiments [1] generalizes the formulation of time-invariant design variables from design of experiments, allowing for consideration of time-variant design variables in the experimental design. When combined with response surface modeling and an appropriate optimization algorithm, a data-driven optimization methodology is produced, which we call DoDE optimization. The method is used here to determine the optimal cooling rate profile, which integrates to give the optimum temperature profile, for a batch crystallization process. To examine the effectiveness of the DoDE optimization method, the data-driven optimum temperature profile is compared to the optimum temperature profile obtained using a model-based optimization technique for the potassium nitrate–water batch crystallization model developed by Miller and Rawlings [2]. The temperature profiles calculated using DoDE optimization yield response values within a few percent of the true model-based optimum values. A sensitivity analysis is performed on one case study to evaluate the distribution of the response variable from each method in the presence of parameter and initial seed distribution variability. It is demonstrated that there is partial overlap in the distributions when only variability in the model parameters is evaluated and there is substantial overlap when variability is included in both the model and initial seed distribution parameters. From this evidence, it can be concluded that the DoDE optimization method has the potential to be a useful data-driven optimization tool for batch crystallization processes where a first-principles model is not available or cannot be developed due to time and/or cost constraints.     

The auto‐geometric correction of multi‐projector for cylindrical surface using Bézier patches

Geometric calibration to projection images is an indispensable operation for projection‐based spatial display. In this paper, we propose a new method for correcting images generated in a computer onto a cylindrical surface accurately, which can project a high‐resolution projection image with pixels matching avoiding too much manual operation. Images waiting to be projected are pre‐warped according to the rough correspondence between projectors and physical surface. To solve the errors resulting from unexpected pixel shifts in overlap projection area, we fit the Bézier interpolation to the images and apply the optimization theory with added constraints to correct the projection image accurately. This optimization process, by taking the pixels with specific significance on the images as the basis of calculation, avoids the traditional ways of translating the control points of the Bézier surface directly. The final results achieve a completely accurate projection picture even if the projection surface shape is inaccurate and irregular. We present the details of the proposed accurate calibration algorithm and illustrate our method, which, with its scalability, can achieve perfect projection efficiently and accurately with experiments.     

蒙特卡罗法用于多指标试验设计的优化计算

以一优化的问题为例,探讨应用蒙特卡罗法去计算多指标试验设计的优化问题。展示及解释了蒙特卡罗法的BASICI程序,比较蒙特卡罗法与网格法的优化结果,原来两种结果几乎相同。但实践表明,蒙特卡罗法既可用于多指标试验设计的优化计算,又方便灵活地实现多指标优化的目的。     

虚拟样机工程中的项目资源均衡优化研究

虚拟样机工程的复杂性不断增加,需有科学的项目管理技术来实现高效组织与管理。在工程项目资源均衡优化的调整中,通常以资源方差来衡量资源的均衡性,方差越小资源均衡性就越好,但其调整过程通常要经过一系列繁琐的推断过程。本文引入遗传算法,针对其中的"工期固定—资源均衡"问题,同时考虑作业之间的相关性限制约束,建立模型并求解;在多种资源优化中,根据每种资源对其资源均衡程度的重要性,给定权系数,用多目标优化中的线性加权系数法,将多种资源优化问题转化为按单种资源优化方法来求解,有效地解决了虚拟样机工程中的项目资源均衡优化问题。     

Multiobjective optimization for crash safety design of vehicles using stepwise regression model

In automotive industry, structural optimization for crashworthiness criteria is of special importance. Due to the high nonlinearities, however, there exists substantial difficulty to obtain accurate continuum or discrete sensitivities. For this reason, metamodel or surrogate model methods have been extensively employed in vehicle design with industry interest. This paper presents a multiobjective optimization procedure for the vehicle design, where the weight, acceleration characteristics and toe-board intrusion are considered as the design objectives. The response surface method with linear and quadratic basis functions is employed to formulate these objectives, in which optimal Latin hypercube sampling and stepwise regression techniques are implemented. In this study, a nondominated sorting genetic algorithm is employed to search for Pareto solution to a full-scale vehicle design problem that undergoes both the full frontal and 40% offset-frontal crashes. The results demonstrate the capability and potential of this procedure in solving the crashworthiness design of vehicles.     

Isogeometric topology optimization using trimmed spline surfaces

In the present work, a new spline based topology optimization using trimmed spline surfaces and the isogeometric analysis is proposed. In the proposed approach, the trimmed surface analysis which can treat topologically complex spline surfaces using trimming information provided by CAD systems is employed for structural response analysis and sensitivity calculation in the topology optimization. The outer and inner boundaries of design models are represented by a spline surface and trimming curves. Design variables used in this approach are the coordinates of control points of a spline surface and those of trimming curves. New sensitivity formulations for the control points in the trimmed surface analysis are proposed and their efficiency and accuracy are verified. The creation of new inner fronts during optimization is allowed for the topological flexibility. An inner front merging algorithm is also presented. The proposed spline based topology optimization is used to solve some benchmarking problems. Design space dependency which is one of serious shortcomings in conventional topology optimization approaches is naturally eliminated by the proposed spline based optimization. Design dependent load problems which are difficult to treat with conventional grid based topology optimization methods are easily dealt with by the proposed one. It is also shown that post-processing effort for converting to CAD model is eliminated by using the same spline information in numerical analysis and design optimization.     

并行子空间优化在无人机总体设计中的应用

飞机设计是一个多学科的复杂的系统工程,各个学科通常相互影响、相互耦合.这使得飞机设计过程日趋复杂,设计周期越来越长,开发成本越来越高,而并行子空间优化(CSSO)是解决这些问题的一种有效方法.文中对基于神经网络响应面的并行子空间优化算法及其在无人机总体方案设计优化中的应用进行了研究.并行子空间优化算法将多学科耦合的无人机设计优化问题分解为不同的子空间问题,在不同的子空间中建立各自的神经网络响应面,通过响应面完成各子空间之间的数据交换与协调,以此来逼近设计空间最优解.应用结果表明,CSSO算法能有效地应用于无人机总体方案优化设计.     

Two-level optimization of airframe structures using response surface approximation

In this paper, a two-level optimization approach is developed for the preliminary and conceptual design of airframe structures. The preliminary design, involving a single objective multidisciplinary optimization, constitutes the lower level where ASTROS (Automated STRuctural Optimization System) is employed for multidisciplinary optimization. The conceptual design, which is carried out at the upper level, aims mainly at configuration design. The multiple objectives are incorporated as a single objective function by using the K-S function formulation. The objective function and constraints at the upper level are modelled through response surface approximation. During the upper level optimization process, the branch and bound method is applied for solving the problem with discrete design variables. The proposed strategy is demonstrated by the optimization of an Intermediate Complexity Wing (ICW) model. Received June 23, 1999