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Robot calibration highly depends on redundant measurement configurations to collect enough sample data for higher accuracy, but excessive measurements seem to be uneconomical and time-consuming. Thus lots of observability indexes to evaluate the goodness of the measurement configurations have been proposed. However, in some circumstances, it is of critical importance to obtain accurate kinematic parameters and estimate the end-effector pose precisely at the same time. Obviously one single observability index can hardly meet this need yet. Accordingly, after analyzing the essential constrains of robot calibration and the influence of measurement configurations on the observability indexes, an optimization model with two observability indexes to be taken into consideration is proposed in this paper, and then the existing DETMAX algorithm is modified to seek optimal design of measurement configurations, by adopting a set-constructing method and a set-shrinking method. Much better results have been obtained by simulation study, which implies that the proposed model and the modified DETMAX algorithm perform well in both kinematic parameter identification and pose estimation of the end-effector of the robot. [ABSTRACT FROM AUTHOR]

The Train Design Optimization Problem regards making optimal decisions on the number and movement of locomotives and crews through a railway network, so as to satisfy requested pick-up and delivery of car blocks at stations. In a mathematical programming formulation, the objective function to minimize is composed of the costs associated with the movement of locomotives and cars, the loading/unloading operations, the number of locomotives, and the crews’ return to their departure stations. The constraints include upper bounds for number of car blocks per locomotive, number of car block swaps, and number of locomotives passing through railroad segments. We propose here a heuristic method to solve this highly combinatorial problem in two steps. The first one finds an initial, feasible solution by means of an ad hoc algorithm. The second step uses the simulated annealing concept to improve the initial solution, followed by a procedure aiming to further reduce the number of needed locomotives. We show that our results are competitive with those found in the literature. [ABSTRACT FROM AUTHOR]

A hybrid uncertain model is introduced to deal with the uncertainties existing in a disc brake system in this paper. By the hybrid uncertain model, the uncertain parameters of the brake with enough sampling data are treated as probabilistic variables, while the uncertain parameters with limited data are treated as interval probabilistic variables whose distribution parameters are expressed as interval variables. Based on the hybrid uncertain model, the reliability-based design optimization (RBDO) of a disc brake with hybrid uncertainties is proposed to explore the optimal design for squeal reduction. In the optimization, the surrogate model of the real part of domain unstable eigenvalue of the brake system is established, and the upper bound of its expectation is adopted as the optimization objective. The lower bounds of the functions related to system stability, the mass, and the stiffness of design component are adopted as the optimization constraints. The combinational algorithm of Genetic Algorithm and Monte-Carlo method is employed to perform the optimization. The results of a numerical example demonstrate the effectiveness of the proposed optimization on improving system stability and reducing squeal propensity of a disc brake under hybrid uncertainties. [ABSTRACT FROM AUTHOR]

Safety design and probabilistic optimization are fields that are widely subject to uncertainty, thus making traditional deterministic methods highly unreliable for these two fields. Popular design optimizations methods widely used for safety design and probabilistic optimization are the performance measure approach (PMA) and the performance measure approach (RIA). In a problem where the analysis is performed from an infeasible design space, a modified reliability index approach (MRIA) is employed to address some inefficiency of the traditional RIA to be able to find the optimal solutions. The PMA uses an inverse reliability analysis, which is more computationally efficient at finding the most probable design points but has been reported to have numerical instabilities on some cases. In this paper, three benchmark examples were thoroughly studied with various initial points to examine the stability and efficiency of MRIA and PMA. A hybrid reliability approach (HRA) was then presented after determining a selection factor from the optimum conditions. The proposed HRA aims to determine which of the two optimization methods would be more appropriate during the optimization processes. [ABSTRACT FROM AUTHOR]

We present a novel linear observer with an extension dealing with polytopic uncertainties in a vehicle dynamic system to identify the side-slip angle. The performance optimization issue is addressed by the minimization of H∞ norm of the system considering the estimation error as an output and the steer angle as an input. Contrary to the standard robust optimal design approaches, we use a convex inner approximation technique to reduce the order of the observer and this enables us to derive suboptimal, fixed-order, and efficiently practicable estimators. Moreover, the numerical examples performed on two-track nonlinear model of the system are provided to illustrate the impacts of design parameters on the optimization results and the efficiency of the technique. [ABSTRACT FROM AUTHOR]

This study proposes a traffic congestion minimization model in which the traffic signal setting optimization is performed through a combined simulation-optimizationmodel. In thismodel, the TRANSYT traffic simulation software is combined with Differential Evolution (DE) optimization algorithm, which is based on the natural selection paradigm. In this context, the EQuilibrium Network Design (EQND) problem is formulated as a bilevel programming problem in which the upper level is the minimization of the total network performance index. In the lower level, the traffic assignment problem, which represents the route choice behavior of the road users, is solved using the Path Flow Estimator (PFE) as a stochastic user equilibrium assessment. The solution of the bilevel EQNDproblemis carried out by the proposed Differential Evolution and TRANSYT with PFE, the so-called DETRANSPFE model, on a well-known signal controlled test network. Performance of the proposed model is compared to that of two previous works where the EQND problem has been solved by Genetic-Algorithms- (GAs-) and Harmony-Search- (HS-) based models. Results show that the DETRANSPFE model outperforms the GA- and HS-based models in terms of the network performance index and the computational time required. [ABSTRACT FROM AUTHOR]

The type of mathematical modeling selected for the optimum design problems of steel skeletal frames affects the size and mathematical complexity of the programming problem obtained. Survey on the structural optimization literature reveals that there are basically two types of design optimization formulation. In the first type only cross sectional properties of frame members are taken as design variables. In such formulation when the values of design variables change during design cycles, it becomes necessary to analyze the structure and update the response of steel frame to the external loading. Structural analysis in this type is a complementary part of the design process. In the second type joint coordinates are also treated as design variables in addition to the cross sectional properties of members. Such formulation eliminates the necessity of carrying out structural analysis in every design cycle. The values of the joint displacements are determined by the optimization techniques in addition to cross sectional properties. The structural optimization literature contains structural design algorithms that make use of both type of formulation. In this study a review is carried out on mathematical and metaheuristic algorithms where the effect of the mathematical modeling on the efficiency of these algorithms is discussed. [ABSTRACT FROM AUTHOR]

We have proposed a new solution to the Multiobjective Linear Fractional Programming Problem (MOLFPP). The proposed solution is based on a theorem that deals with nonlinear fractional programming with single objective function and studied in the work by Dinkelbach, 1967. As a new contribution, we have proposed that is an efficient solution of MOLFPP if is x an optimal solution of problem Maxx∊X...(Ni(x) - Zi*Di(x)), where is Zi* = (Ni(x) - Zi*Di(x) for all . Hence, MOLFPP is simply reduced to linear programming problem (LPP). Some numerical examples are provided in order to illustrate the applications of the proposed method. The optimization software package, namely, WinQSB (Chang, 2001), has been employed in the computations. [ABSTRACT FROM AUTHOR]

This paper developed an effective optimization method, i.e., gradient-Hessian matrix-based method or second order method, of frame structures subjected to the transient loads. An algorithm of first and second derivatives of dynamic displacement and stress with respect to design variables is formulated based on the Newmark method. The inequality time-dependent constraint problem is converted into a sequence of appropriately formed time-independent unconstrained problems using the integral interior point penalty function method. The gradient and Hessian matrixes of the integral interior point penalty functions are also computed. Then the Marquardt's method is employed to solve unconstrained problems. The numerical results show that the optimal design method proposed in this paper can obtain the local optimum design of frame structures and sometimes is more efficient than the augmented Lagrange multiplier method. [ABSTRACT FROM AUTHOR]

The preliminary design of a jet aircraft wing, through the use of an integrated multidisciplinary design environment, is presented in this paper. A framework for parametric studies of wing structures has been developed on the basis of a multilevel distributed analysis architecture with a "hybrid strategy" process that is able to perform deterministic optimizations and tradeoff studies simultaneously. The particular feature of the proposed multilevel optimization architecture is that it can use different set of variables, defined expressly for each level, in a multi-level scheme using "low fidelity" and "high fidelity" models, as well as surrogate models. The prototype of the design environment has been developed using both commercial codes and in-house tools and it can be implemented in a geographically distributed and heterogeneous IT context. [ABSTRACT FROM AUTHOR]

Many genetic algorithms (GA) have been applied to solve different NP-complete combinatorial optimization problems so far. The striking point of using GA refers to selecting a combination of appropriate patterns in crossover, mutation, and and so forth and fine tuning of some parameters such as crossover probability, mutation probability, and and so forth. One way to design a robust GA is to select an optimal pattern and then to search for its parameter values using a tuning procedure. This paper addresses a methodology to both optimal pattern selection and the tuning phases by taking advantage of design of experiments and response surface methodology. To show the performances of the proposed procedure and demonstrate its applications, it is employed to design a robust GA to solve a project scheduling problem. Through the statistical comparison analyses between the performances of the proposed method and an existing GA, the effectiveness of the methodology is shown. [ABSTRACT FROM AUTHOR]