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This study focuses on developing fully functional cutting stock software by utilizing an improved multi-phase heuristic algorithm to find the optimal solution for the cutting materi al. Issues which commonly appear in real-world cutting stock applications are also discussed

Many diseases associated with mental deterioration among aged patients can be effectively treated using neurological treatments. Research shows that electroencephalography (EEG) can be used as an independent prognostic indicator of morbidity and mortality. Unfortunately, EEG data are typically inaccessible to modern software. It is therefore important to design a comprehensive approach to integrate EEG results into institutional medical systems. A customized EEG system utilizing a database management approach was designed to bridge the gap between the commercial EEG software and hospital data management platforms. Practical and useful medical findings are discoursed from statistical analysis of large amounts of EEG data.

The use of simulation models to manage and regulate property–liability insurers has gained in popularity over the last decade. This paper introduces a hybridized search optimization algorithm, also known as a Memetic Algorithm, for use with these insurer simulation models. The proposed algorithm combines the merits of both local and global search optimization techniques, and provides an efficient and robust approach for insurance model application. Our research investigated whether this enhanced optimization algorithm could further improve the results of a simulation model. As part of this investigation, a company-wide simulation model of a property–liability insurer was coupled with the proposed hybrid algorithm to tackle a typical multi-period asset allocation problem. The resulting asset allocations obtained by the proposed memetic algorithm coupled with the simulation model demonstrated better results than currently available investment strategies. The significant and robust improvements put forth in the present research demonstrate the great potential of our multi-phase hybrid algorithm in enhancing simulation model capabilities.

This paper proposes an optimization approach for generating an investment strategy for multi-period asset-liability management of long-term with-profit life insurance policies. Our approach uses models to simulate the processes insurance companies employ when determining multi-period investment strategies over a given planning horizon. The approach utilizes an enhanced heuristic algorithm to determine optimal multi-period investment strategies. Simulation models take into account asset numbers, objective functions, and asset allocation frequency. Strategy performance is evaluated by applying three single-period investment strategies to the simulation models. Computational results not only verify the efficiency and robustness of the algorithm, but also demonstrate the effectiveness of frequent asset reallocation, and dispute the suitability of traditional top-down investment strategies in maximizing investment returns of with-profit insurance policies.

Highlights? A simulation algorithm, evolution strategies, enable slong-term investment selection of defined-contribution pensions. ? Multi-period asset allocation matches the target liability amount by ther etirement date and reduces downside risk. ? Simulations reveal promising multi-period asset investment strategies without high sensitivity to single-point forecasts. ? Algorithm results achieve higher average investment returns and lower levels of volatility under different conditions. ? With downside risk monitoring, this approach can help risk-seeking managers and investors enhance investment performance. This paper presents an optimization approach to analyze the problems of portfolio selection for long-term investments, taking into consideration the specific target replacement ratio for defined-contribution (DC) pension scheme; the purpose is to generate an effective multi-period asset allocation that reaches an amount matching the target liability at retirement date and reduce the downside risk of the investment. A multi-period asset liability simulation model was used to generate 4000 asset return predictions, and an evolutionary algorithm, evolution strategies, was incorporated into the model to generate multi-period asset allocations under four conditions, considering different weights for measuring the importance of matching the target liability and different periods of downside risk measurement. Computational results showed that the evolutionary algorithm, evolution strategies, is a very robust and effective approach to generate promising asset allocations under all the four cases. In addition, computational results showed that the promising asset allocations revealed valuable information, which is able to help fund managers or investors achieve a higher average investment return or a lower level of volatility under different conditions.

This paper addresses recent developments of grid generation techniques employing nonuniform rational B-spline formulations (NURBS). Grid refinement algorithms for arbitrary curves and surfaces with NURBS maintain high fidelity of the geometry, and allow time-efficient interactive grid generation. Bezier curve formulations are utilized to subdivide surface patches to accomplish a smooth and near orthogonal zonal grid. A CAD/Grid interface code based on NURBS formulations has been developed which converts CAD data from IGES files into the format accepted by the structured grid system. Two interactive grid generation codes, a general-purpose code and a customized one for turbomachinery configurations, have been developed on the basis of NURBS' definition.

Geometry modeling and mesh generation are pre-processing steps in computational engineering as many computational engineering disciplines require a valid numerical mesh for solving the respective governing equations. However, mesh generation is often the most labor-intensive phase during the entire numerical simulation cycle, as a user needs to cautiously maintain the fidelity of the geometry while creating high quality meshes on and about the geometry. Non-Uniform Rational B-Spline (NURBS) is currently the most robust and most popular technology that is currently used as the de facto geometry representation between CAD and mesh generation systems. Unfortunately, not all the geometry acquired by the user can be readily represented in NURBS. The focus of this paper is to describe the approaches that address issues associated with structured mesh generation in the process of reverse engineering. These approaches utilize 3-D laser scanners or contact scanners to digitize the 3-D models and obtain point cloud that defines the geometry. These points are used to generate high quality 3D structured surface mesh through an interactive process.