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Abstract: Elementary and junior high school buildings in Taiwan are designed to serve not only as places of education but also as temporary shelters in the aftermath of major earthquakes. Effective evaluation of the seismic resistance of school buildings is a critical issue that deserves further investigation. The National Center for Research on Earthquake Engineering (in Taiwan) currently employs performance-target ground acceleration (AP ) as the index to evaluate school structure compliance with seismic resistance requirements. However, computational processes are complicated, time consuming, and require the input of many experts. To address this problem, this paper developed an evolutionary support vector machine inference system (ESIS) that integrated two AI techniques, namely, the support vector machine (SVM) and fast messy genetic algorithm (fmGA). Based on training results, the developed system can predict the AP of a school building in a significantly shorter time base, thus increasing evaluation efficiency significantly. The validity of ESIS was tested using the 10-Fold Cross-Validation method. Another aim of this paper is to retain and apply expert knowledge and relevant experience to the solution of similar problems in the future. [Copyright &y& Elsevier]

Abstract: Enterprises evaluate customers’ preferences of high-tech products to appropriately adjust product lines when the technologies are ready for commercialization. Conjoint analysis (CA) is usually applied to measure customers’ preferences while the product line design model (PLDM) is used to simulate customers’ purchase decisions based on these preferences. The optimal product line extension scheme (PLES) can then be found. For customers unfamiliar with a new technology, it is difficult to precisely appraise preferences for new products. This paper proposes fuzzy PLDM that combines fuzzy theory with CA to take preference uncertainty into consideration. To precisely apply fuzzy preferences, fuzzy rating is used to simulate customers’ purchase decisions. This paper focuses on the notebook industry in Taiwan. Optimal PLES are obtained under Fuzzy and Crisp scenarios. The results show that customers with high preference uncertainty have different purchase decisions under above scenarios, which leads to a great inconsistency between the optimal PLES obtained from Fuzzy and Crisp scenarios. The Fuzzy scenario takes preference uncertainty into consideration and uses stricter standards to judge whether customers buy products with new technology. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a strategy for using students’ complementary competencies in cooperative learning to increase their English learning performance. The concept of complementary learning is based on the idea that teaching is learning. The foundation of the complementary learning concept is composed of three stages proposed to derive the optimal learning clusters—input stage, genetic algorithm (GA) stage, and output stage. In tests and verification of the feasibility of using optimal complementary learning clusters in increasing students’ English learning outcome, comparisons between the experimental group (the optimal complementary learning clusters) and the control group showed that students in the experimental group had higher performances in listening, speaking, and reading competencies than those in the control group. Finally, according to the respective importance weights of different English competencies in different learning objectives, the fuzzy linguistic terms were applied to derive optimal complementary learning clusters to maximize students’ learning outcome. [Copyright &y& Elsevier]

Abstract: In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as . After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), produce complex and unintelligible rules; and (2) statistic forecasting models, such as time series, require some basic assumptions for variables and build forecasting models based on mathematic equations, which are not easily understandable by stock investors. In order to refine these drawbacks of past models, this paper has proposed a model, based on adaptive-network-based fuzzy inference system which uses multi-technical indicators, to predict stock price trends. Three refined processes have proposed in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a correlation matrix; (2) use the subtractive clustering method to partition technical indicator value into linguistic values based on an data discretization method; (3) employ a fuzzy inference system (FIS) to extract rules of linguistic terms from the dataset of the technical indicators, and optimize the FIS parameters based on an adaptive network to produce forecasts. A six-year period of the TAIEX is employed as experimental database to evaluate the proposed model with a performance indicator, root mean squared error (RMSE). The experimental results have shown that the proposed model is superior to two listing models (Chen’s and Yu’s models). [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new method based on genetic algorithms to solve the optimal default point of the KMV model. In our empirical study, we compare the GA-KMV model with the QR-KMV and KMV models. The results indicate that the percentage of correctness of the GA-KMV model is higher than those for the other two models. This is to say, the GA-KMV model has a better goodness of fit. We also obtain the optimal default point for a Taiwan listed company. This can help us to predict the default point and improve the bank’s risk management performance. [Copyright &y& Elsevier]

Abstract: This paper establishes a simulation model for the supply chain of the hospital logistic system (SCHLS) based on the dynamic Taguchi method. The model derives optimal factor level combinations in the SCHLS setting when establishing adjustable contracting capacity between SCHLS and the pharmaceutical company. To attempt the goal, this study adopts the SCHLS case in the North Alliance of the Department of Health of Taiwan. The data collection uses a simulation based on Taguchi’s L 18 orthogonal table for SCHLS. This work also proposes an optimal approach, including the neural network (NN) and genetic algorithm (GA), to obtain an optimal robust design in achieving optimal SCHLS multi-performance. The research results show that the predicted response of multi-performance in the optimal approach is better than that in Taguchi. [ABSTRACT FROM AUTHOR]

Abstract: Accurate and immediate forecast in the short product life cycle of semiconductor market is difficult, but important. This paper proposed a grey model with factor analysis techniques to deal with the multi-factor forecasting problems. In the grey modeling, the use of genetic algorithm has the ability to search global optimum solution to construct two improved multivariable grey forecasting models that are AGAGM(1,N) and GAGM(1,N). These two models are applied for forecasting Taiwanese integrated circuit output. The results of the factor analysis show that the major factors of Taiwan’s integrated circuit output comprise R&D intensity, foreign investment, index of industrial production, trade specialization coefficient, and intra-industry trade coefficient. The improved multivariable grey forecasting models are found to be feasible and effective. [Copyright &y& Elsevier]

Abstract: This paper proposes a methodology that integrates in-house placement heuristics with genetic algorithms to solve the nesting problems of shoe making. The problems are classified as placing a set of irregular patterns on a regular area and limited to at most two different types of patterns on the area. Because of the intractability of the nesting problem, our objective is to utilize genetic algorithms’ fast convergence and solution quality to improve material utilization and reduce the calculation time of the pattern. Using the real-life data of two international brands of athletic shoes, the empirical results show that our proposed methodology can reduce average material requirements by 2.64% and average nesting time by 69.15% compared to those of current in-house software. The reduction of materials is becoming more important given that the industry is facing continuingly declining profit margins. [Copyright &y& Elsevier]