Reliability-based multiobjective optimization (RBMO) is a method that integrates multiobjective optimization with a reliability analysis. The method is useful for a large or complicated design problem such as aerospace structure design. Reliability analysis generally requires the probabilistic distribution parameters of random variables such as the mean and standard deviation. However, for an actual design problem, the probabilistic parameters are sometimes estimated with insufficient accuracy because of a limited number of experiments. In that case, the uncertainty in the distribution parameter is not negligible. This study proposes the evaluation method to estimate the effect of the information uncertainty at first, where the uncertainty is evaluated by using the confidence interval. Some numerical examples illustrates the effectiveness of the proposed method in comparison with a conventional method, Gibbs sampling. Then, the effect of the parameter uncertainty on the RBMO is illustrated through numerical examples. The RBMO problem is formulated by using the satisficing trade-off method (STOM), where the multiobjective optimization problem is transformed into the equivalent single-objective optimization method. For the reliability-based design optimization, a modified SLSV (single-loop-single-vector) method is adopted for the computational efficiency. The effects of the parameter uncertainty on the selected Pareto solutions according to the aspiration level are investigated by using the confidence intervals of the Pareto solutions.
Recently, the development of small reconnaissance robots has been actively studied for the purpose of remote monitoring. However, difficulties related to the robot gaining access to a distant target area have been encountered in this research area. In this paper, a cross-bow-type launcher, which uses elastic energy to launch, was developed to launch a small robot to a designated target location. The design parameters were determined based on commercial users' comments and axiomatic design methodology to optimize the launcher system. This crossbow system was developed to satisfy functional requirements, such as protecting the robot during the launching process and achieving long-range shooting over 100 m, an accurate shooting range, high portability and ease of operation. In addition, field experiments were conducted to validate the performance of the developed launcher system.
The summer season witnesses a tremendous rise in electrical power consumption. To meet this electric demand, the reduction of electrical power demand in summer by improving the efficient utilization of facilities is critical. Currently, power management and power-saving efforts in the manufacturing industry are focused on improving efficiency through replacement and improvement of equipment that consumes large amounts of energy. However, recent advances in demand response have significantly increased awareness that can lead factory managers to schedule their energy consumption efficiently by shifting electrical loads. This paper presents an optimization model for managing facility operations with shifting electrical loads in an effort to deal with the most expensive hours of the day. An integer programming (IP) model is used as a formal presentation of the problem. The results from a performance analysis of the formulation in solving problems with different characteristics are prepared, along with illustrative examples. They demonstrate how a factory manager can shift electrical loads in response to electricity prices.