As the demand for multi-chip products with high capacity and small size increases, semiconductor packaging facilities have been faced with complicated constraints such as re-entrant flows, sequence dependent setups, and alternative routes, which leads to difficulties in scheduling semiconductor manufacturing operations. Furthermore, due to the frequent variations in the relative importance between objectives as well as the variabilities in initial setup status, available machines, and production requirements, practitioners are obliged to obtain a schedule within a short amount of computation time. In this paper, we propose a novel two-phase framework that aims to quickly produce a schedule of semiconductor packaging facilities by using case-based reasoning for minimizing the weighted sum of machine loss time and waiting time of jobs. Specifically, in the case generation phase, a case database is constructed by solving case scheduling problems using an existing solver. The case reasoning phase is responsible for repairing operation type sequences in the cases to produce a schedule for an unseen scheduling problem whose production requirements, available machines, initial setup status, and weight between performance measures are different from those of cases. The extensive experimental results demonstrated that the proposed approach requires a short computation time similar to the rule-based methods while maintaining the quality of the schedules comparable to that of the existing metaheuristics.INDEX TERMS Semiconductor packaging facilities, flexible job shop scheduling, case-based reasoning, sequence dependent setups, schedule repair.

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One of the fundamental technologies for unmanned combat aerial vehicles and combat simulators is behavior optimization, which finds a behavior that maximizes the probability of winning a battle. With the advent of military science, combat logs became available, allowing machine learning algorithms to be used for the behavior optimization. Due to implicit attributes such as the experience of an operator that are not explicitly presented in log data, existing methods for behavior optimization have limitations in performance improvement. Furthermore, specific behaviors occur with low frequency, resulting in a dataset with imbalanced and empty values. Therefore, we apply a matrix factorization (MF) method, which is one of latent factor models and known for sophisticated imputation of empty values, to the behavior optimization problem of unmanned combat aerial vehicles. A situation-behavior matrix, whose elements are ratings indicating the optimality of behaviors in situations, is defined to implement the MF based method. Experiments for performance comparison were conducted on combat logs, in which the proposed method yielded satisfactory results. INDEX TERMS behavior optimization, unmanned vehicle, matrix factorization, reinforcement learning, situation-behavior matrix ABBREVIATIONS AM Advantage matrix. FOV Field of view. GA Genetic algorithm. LOS Line of sight. MF Matrix factorization. nDCG Normalized discounted cumulative gain. RL Reinforcement learning. SB Situation-behavior. UV Unmanned vehicle.