A non-linear multi-criteria model for strategic fms selection decisions

The strategic decision of selecting an optimal flexible manufacturing system (FMS) configuration is a complicated question which involves evaluating trade-offs between a number of different, potentially conflicting criteria such as annual production volume, flexibility, production and investment costs and average throughput of the system. Recently, several structured multicriteria approaches have been proposed to aid management in the FMS selection process. While acknowledging the non-linear nature of a number of the relationships in the model, notably between batch size and the number of batches produced of each part, these studies used linear simplifications to illustrate the decision dynamics of the problem. These linear models were shown to offer useful analytical tools in the FMS pre-design process. Owing to the non-linearities of the true relationships, however, the trade-offs between the criteria could not fully be explored within the linear framework. This paper builds on the two-phase decision support framework proposed by Stam and Kuula (1991) and uses a modified non-linear multi-criteria formulation to solve the problem. The software used in the illustration can easily be implemented, is user-interactive and menu-driven. The methodology is applied to real data from a Finnish metal product company and the results are compared with those obtained in previous studies.     

Pairwise agreement of gradient directions in interactive multicriterion optimization

Statistical tests involving mean directions have in the past been limited to two- and three-dimensional settings, perhaps owing to their primary applications to such fields as geology, meteorology and related earth sciences. In the study of interactive multicriterion optimization it becomes necessary to compare gradient directions obtained from decision makers by two or more methods. Typically these direction vectors are in a higher-dimensional space. This paper provides a general procedure based on Householder transformations which is potentially suitable for any finite dimension. An illustration and comparison of the method are provided.     

Multicriteria Optimization Technique for Optimal Design of Orthotropic Bridges

The design improvement of large‐scale structures such as cable stayed and suspension bridges with large spans is one of the major engineering optimization problems faced by design engineers. In many real‐life engineering design problems, it is necessary to carry out large‐scale experimental physical models for only one prototype to construct the feasible solution set that is too expensive and not practical. For these reasons, an experimental search for optimal solutions is often not carried out at all. This paper presents a technique for multicriteria analysis, which involve the finite element analysis of the prototype in the optimization process. The improvement of the Suez Canal Bridge in Egypt is introduced as a real‐life large‐scale case study. The parameter space investigation method, the visual basic for application programming language, and Femap as finite element analysis software provide an implementation tools to construct the feasible and Pareto solution sets for the studied bridge. An efficient combination between the parameter space investigation method and the finite element programme was successfully investigated to obtain the Pareto solution set. This study shows possibility to apply the multicriteria optimization method for more applications on different large‐scale structural systems in the future. Copyright © 2013 John Wiley & Sons, Ltd.     

Multiobjective Optimization for the Asset Allocation of European Nonlife Insurance Companies

An optimal asset allocation is crucial for nonlife insurance companies. The most previous studies focused on this topic use a mono‐objective technique optimization. This technique usually allows the maximization of shareholders' expected utility. As nonlife insurance company is a complex system, it has many stakeholders other than shareholders. So, the satisfaction of the shareholders' expected utility cannot lead usually to the satisfaction of other stakeholders' objectives. Therefore, the focus on utility maximization can be a destruction source of other objectives such as productivity, competitiveness and solvency. Our developed model integrates simulation approach with a multiobjective particle swarm optimization algorithm. This model insures an optimal asset allocation that maximizes, simultaneously, shareholders expected utility and technical efficiency of European nonlife insurance companies. The empirical application conducts a comparison between the attained results with multiobjective optimization technique and mono‐objective technique to search the optimal asset allocation for nonlife insurance companies. Our results show that the investment portfolio will be more diversified between most available investment assets. In addition, any decision maker should take account of different stakeholders' objectives. Accordingly, multiobjective optimization allows us to find the best asset allocation that maximizes simultaneously expected utility and technical efficiency of nonlife insurance companies. Copyright © 2013 John Wiley & Sons, Ltd.     

Perceived Occurrence of the Second of Two Closely Spaced Stimuli in a Long Movement

A 75-deg. movement of the arm in the vertical plane was used to examine the conscious perception of the apparent time of occurrence of a reversal signal S₂. A marked perceptual delay was found whether S₂ occurred during or after the reaction time to the first stimulus. This finding was consistent with Henry’s memory drum theory of neuromotor response, as well as with the efference theory of conscious perception proposed by Festinger and associates.     

Analytical Inverse Optimization in Two-Hand Prehensile Tasks

The authors explored application of analytical inverse optimization (ANIO) method to the normal finger forces in unimanual and bimanual prehensile tasks with discrete and continuously changing constraints. The subjects held an instrumented handle vertically with one or two hands. The external torque and grip force changed across trials or within a trial continuously. Principal component analysis showed similar percentages of variance accounted for by the first two principal components across tasks and conditions. Compared to unimanual tasks, bimanual tasks showed significantly more frequent inability to find a cost function leading to a stable solution. In cases of stable solutions, similar second-order polynomials were computed as cost functions across tasks and condition. The bimanual tasks, however, showed significantly worse goodness-of-fit index values. The authors show that ANIO can be used in tasks with slowly changing constraints making it an attractive tool to study optimality of performance in special populations. They also show that ANIO can fail in multifinger tasks, likely due to irreproducible behavior across trials, more likely to happen in bimanual tasks compared to unimanual tasks.     

The effects on classifier performance of 2D discrete wavelet transform analysis and whale optimization algorithm for recognition of power quality disturbances

Power Quality (PQ) is becoming more and more important day by day in the electric network. Signal processing, pattern recognition and machine learning are increasingly being studied for the automatic recognition of any disturbances that may occur during the generation, transmission, and distribution of electricity. There are three main steps to identify the PQ disturbances. These include the use of signal processing methods to calculate the features representing the disturbances, the selection of those that are more useful than these feature sets to prevent the creation of a complex classification model, the creating a classification model that recognizes multiple classes using the selected feature subsets. In this study, one-dimensional (1D) PQ disturbances signals are transformed into two-dimensional (2D) signals, 2D discrete wavelet transforms (2D-DWT) are used to extract the features. The features are extracted by using the wavelet families such as Daubechies, Biorthogonal, Symlets, Coiflets and Fejer-Korovkin in 2D-DWT to analyze PQ disturbances. Whale Optimization Algorithm (WOA) and k-nearest neighbor (KNN) classifier determine the feature subsets. Then, WOA and k nearest neighbor (KNN) classifier are used to determine the feature group. By using KNN and Support Vector Machines (SVM) classification methods, Classifier models that distinguish PQ disturbances are formed. The main aim of the study is to determine the features derived from 2D wavelet coefficients for different wavelet families and to determine which of them has a better classification performance to distinguish PQ disturbances signals. At the same time, different classification methods are simulated and a model which can classify PQ disturbances signals with high performance is created. Also, the generated models are analysed for their performance in terms of different noise levels (40 dB, 30 dB, 20 dB). The result of this simulation study shows that the model developed to classify PQ disturbances is superior to conventional models and other 2D signal processing methods in the literature. In addition, it was concluded that the proposed method can cope better with noisy signals by low computational complexity and higher classification rate.     

Multiobjective optimization for interwoven systems

In practical situations, complex systems are often composed of subsystems or subproblems with single or multiple objectives. These subsystems focus on different aspects of the overall system, but they often have strong interactions with each other and they are usually not sequentially ordered or obviously decomposable. Thus, the individual solutions of subproblems do not generally induce a solution for the overall system. Here, we strive to identify “re‐composition architectures” of such “interwoven” systems. Our intention is to connect the subsystems adequately, analyze the resulting performance, model/solve the overall system, and improve the overall solution instead of just solving each subsystem separately. We review recent developments in this field and discuss modeling and solution paradigms in a general and unified framework using the example of an interwoven system consisting of two interacting subsystems.     

过度医疗的伦理学会诊及其治疗处方--兼论临床诊治最优化伦理准则

过度医疗是一个极其复杂的医学-社会问题,给予医学伦理学的解析是解决这一问题的最基本的工作。目前,服务质量虚高而服务收费真高是过度医疗的实质和特点;它貌似临床最优化服务,但却严重背离临床诊治最优化准则,对二者进行鉴别诊断是必要的、可能的;伦理学会诊的结论是：该病为疑难病综合征,由医者医学伦理素质低下、医院管理背弃伦理理念、医改政策中伦理机制缺席等多病因相互作用所致;所以,根治过度医疗的处方必须是全方位的、系列连续的、标本兼治的。