There always exist nonzero inspection errors whether inspectors are humans or automatic inspection machines. Inspection errors can be categorized by two types, type I error and type II error, and they can be regarded as either a constant or a random variable. Under the assumption that two types of random inspection errors are distributed with the “uniform” distribution on a half-open interval starting from zero, it was proved that inspectors overestimate any given fraction defective with the probability more than 50%, if and only if the given fraction defective is smaller than a critical value, which depends upon only the ratio of a type II error over a type I error. In addition, it was also proved that the probability of overestimation approaches one hundred percent as a given fraction defective approaches zero. If these critical phenomena hold true for any error distribution, then it might have great economic impact on commercial inspection plans due to the unfair overestimation and the recent trend of decreasing fraction defectives in industry. In this paper, we deal with the same overestimation problem, but assume a “symmetrical triangular” distribution expecting better results since our triangular distribution is closer to a normal distribution than the uniform distribution. It turns out that the overestimation phenomenon still holds true even for the triangular error distribution.

Optimum lot size calculation for real world manufacturing environment has been focused since last few decades. Several extensions have been made to the basic economic order and production order quantity models to realize the possible practical situations in industry. However, focus on work-in-process inventory has been ignored relatively. This paper provides a comprehensive review of the models developed for group technology based manufacturing environment focusing on work-in-process inventory. Models have been extended from a perfect manufacturing conditions to an imperfect manufacturing situation considering rework, rejection and inspection. Optimum lot size has been evaluated using a simple algebraic optimization approach. Significant parameters are highlighted using sensitivity analysis for the developed models. Numerical example is used to illustrate the utilization of such models in day-to-day production setups and the impact of significant factors’ variation on total cost and optimum lot size.

이 논문은 물류배송의 불확실성하에서의 생산자의 배송전략 문제를 다루고 있다. 즉 생산자의 배송수량이 전량 온전한 상태로 소매업체의 창고에 배송되지 못하는 상황을 고려한 것이다. 물류배송의 불확실성을 묘사하기 위해 물류배송을 위해 사용되는 개개의 트럭 또는 선박으로부터 발생되는 파손 제품의 수가 가각 독립적이며 동일한 확률분포를 따른다는 가정이 사용되었다. 또한, 최초 배송수량과 배송을 위해 사용된 전체 트럭 또는 선박의 대수를 기존의 단일구간 신문팔이

최근 게임분야에서 수준 높은 인공지능 에이전트의 구현은 많은 주목을 받고 있다. 그 중 Monte-Carlo Tree Search(MCTS)는 완전 정보를 가진 게임에서 무작위 탐색을 통해 최적의 해를 구할 수 있는 알고리즘으로, 수식으로 표현되지 않는 경우에 근사치를 계산하는 용도로 적합하다. 하스스톤과 같은 Trading Card Game(TCG) 장르의 게임은 상대방의 카드와 플레이 를 예측할 수 없기 때문에 불완전 정보를 가지고 있다. 본 논문에서는 불완전 정보 카드 게임 에서 인공지능 에이전트를 생성하기 위해 MCTS 알고리즘을 응용하는 방법을 제안하고, 현재 서비스되는 하스스톤 게임에 적용하여 봄으로써 MCTS 알고리즘의 실용성을 검증한다.