The problem of a hydrogenic donor impurity in a cubic quantum dot with finite confinement potential is solved by the finite difference method. The discretized matrix equation needs to be solved for its eigenvalues and...
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The problem of a hydrogenic donor impurity in a cubic quantum dot with finite confinement potential is solved by the finite difference method. The discretized matrix equation needs to be solved for its eigenvalues and eigenfunctions. Ail algorithm of nonlinear programming is utilized for this problem. The results match references qualitatively in the weak regime, but introduce new problems in the strong and intermediate regimes. (C) 2008 Elsevier Ltd. All rights reserved.
A comparison study of global and local orthogonal collocation solutions for two optimal control problems, is presented. A collocation method is a method for the numerical solution of ordinary differential equation and...
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A comparison study of global and local orthogonal collocation solutions for two optimal control problems, is presented. A collocation method is a method for the numerical solution of ordinary differential equation and partial differential equations and integral equations. The Gausspseudospectral method (GPM), orthogonal collocation method, is used in both a global format and a local format for analyzing comparative characteristics. The GPM is applied globally and solutions are computed for various numbers of collocation points, where a single segment is used across the entire interval along with varied collocation points. The method is also applied locally, where the problem is segmented with each segment containing a small, mixed fixed number of collocation points. Results indicate that fixed number of collocation points show more accuracy than local collocation for smooth problems and can provide comparable results for problems with discontinuities.
We study several variations of the Bitran-Hax variable fixing method for the continuous quadratic knapsack problem. We close the gaps in the convergence analysis of several existing methods and provide more efficient ...
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We study several variations of the Bitran-Hax variable fixing method for the continuous quadratic knapsack problem. We close the gaps in the convergence analysis of several existing methods and provide more efficient versions. We report encouraging computational results for large-scale problems.
The numerical optimization of continuous parameters in electrotechnical design using electromagnetic field simulation is already standard. When integer-valued variables are involved, the complexity of the optimization...
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The numerical optimization of continuous parameters in electrotechnical design using electromagnetic field simulation is already standard. When integer-valued variables are involved, the complexity of the optimization problem rises drastically. In this paper, we describe a new sequential surrogate optimization approach for simulation-based mixed-integer nonlinear programming (NLP) problems. We apply the method for the optimization of combined integer- and real-valued geometrical parameters of the coils of a superconductive magnet.
The transparent ZnO films were deposited on quartz glass slides by RF magnetron sputtering and then the films are annealed at 500, 700, 900 degrees C for 60 min in pure O-2, The optical transmission spectra for the as...
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The transparent ZnO films were deposited on quartz glass slides by RF magnetron sputtering and then the films are annealed at 500, 700, 900 degrees C for 60 min in pure O-2, The optical transmission spectra for the as-grown and annealed ZnO films have been measured over the 300 nm to 700 nm spectral region. The optical constants and thickness of the films have been determined using a nonlinear programming method suggested by Birgin et al. The optical band gap has been determined from the absorption coefficient. The optical quality of ZnO films is improved by annealing. (C) 2007 Elsevier B.V. All rights reserved.
In this paper, we address the problem of allocating the work elements, belonging to the products of a lot, to the stations of an assembly line so as to minimize the makespan. The lots that are processed on the assembl...
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In this paper, we address the problem of allocating the work elements, belonging to the products of a lot, to the stations of an assembly line so as to minimize the makespan. The lots that are processed on the assembly line are characterized by a low overall demand for each product. There is no buffer permitted in between the stations, and the line operates under learning. In particular, the stations' learning slopes are assumed to be different. We present a procedure to determine the optimal assignments of the workload to. the stations under learning variability and show that it considerably affects these assignments. (C) 2006 Elsevier B.V. All rights reserved.
In this paper, a rectilinear-based congestion-driven floorplanning algorithm is presented to enhance the wire congestion and the CPU runtime. The proposed algorithm contains two stages, including the simulated-anneali...
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In this paper, a rectilinear-based congestion-driven floorplanning algorithm is presented to enhance the wire congestion and the CPU runtime. The proposed algorithm contains two stages, including the simulated-annealing (SA) based approach with the concept of ant algorithm (SANTA) and the nonlinear programming based method. The objective of the first stage and the second stage are to minimize the multiple objectives, such as the area, wire length and wire congestion, and to further improve the wire congestion of the local congested region without the area overhead, respectively. First, the effective concept of the ant algorithm is integrated into the multiple objectives floorplanner, which simultaneously minimizes area, wire congestion and the total wire length, to speedup the runtime. Besides, the nonlinear programming (NLP) based formulations are provided to perform the module reshaping, which maximizes the common length between two adjacent congested modules. For the floorplanner, the sequential-pair (SP) presentation is utilized to deal with the floorplan data at every iteration. For each iteration of the floorplanner, we first use SANTA to improve the neighbor searching and reduce the runtime. After performing the first state, we Will obtain a floorplan with the objectives of the area, wire congestion and total length. For the intermediate floorplan, we select the two adjacent soft modules located at the most congested regions and divide the two soft modules into a set of connected sub-rectangles. Hence, we further reduce the congestion by enlarging the common boundary between the selected adjacent modules. Of course, the modular reshaping technique significantly increases the common length and the capacity of pins to reduce the wire congestion by utilizing the nonlinear programming based approach. To deserve to be mentioned that there is no area overhead after we perform the modular reshaping for the selected adjacent modules. Compared to the results of the tr
In this paper, a ground structure approach for computational morphogenesis of building structure is shown. In this method, it is difficult to obtain exact solutions by general non-linear programming methods when the m...
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In this paper, a ground structure approach for computational morphogenesis of building structure is shown. In this method, it is difficult to obtain exact solutions by general non-linear programming methods when the members of ground structure increase. In this paper, some examples that the solution dose not converge to exact solution are shown, and a method for solving this problem is proposed. In the proposed method, SLP or CONLIN method is used as solver of the optimization problem. Several numerical examples are presented to demonstrate the effectiveness of the proposed method. Also, it is shown that this method can be used for computational morphogenesis of building structure.
This paper proposes a local search optimizer that, employed as an additional operator in multiobjective evolutionary techniques, can help to find more precise estimates of the Pareto-optimal surface with a smaller cos...
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This paper proposes a local search optimizer that, employed as an additional operator in multiobjective evolutionary techniques, can help to find more precise estimates of the Pareto-optimal surface with a smaller cost of function evaluation. The new operator employs quadratic approximations of the objective functions and constraints, which are built using only the function samples already produced by the usual evolutionary algorithm function evaluations. The local search phase consists of solving the auxiliary multiobjective quadratic optimization problem defined from the quadratic approximations, scalarized via a goal attainment formulation using an LMI solver. As the determination of the new approximated solutions is performed without the need of any additional function evaluation, the proposed methodology is suitable for costly black-box optimization problems.
Given design problems today, many organizations now implement Design for Six Sigma (DFSS) initiatives. One DFSS tool is quality function deployment (QFD), which is a design approach that proactively translates custome...
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Given design problems today, many organizations now implement Design for Six Sigma (DFSS) initiatives. One DFSS tool is quality function deployment (QFD), which is a design approach that proactively translates customers' needs into technical design requirements. The QFD methodology has been well documented in the literature, yet few researchers have investigated how this process may be used to solve multiresponse optimization problems. Here, we demonstrate a new approach that combines aspects of QFD within the traditional robust design methodology to address this issue. The methodology is demonstrated through an illustrated example, which is compared to the traditional robust design approach.
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