절삭가공에서의 기계선정을 위한 기계부하 예측

Machine load prediction for selecting machines in machining

Dynamic job shop environment requires not only more flexible capabilities of a CAPP system but higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations to be performed by predicting the machine loads. The developed algorithm is based on the multiple objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as Pareto-optimal solutions). The objective shows a combination of the minimization of part movement and the maximization of machine utility balance. The algorithm is characterized by a new and efficient method for nondominated sorting, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II.