평블록 조립 공정에 대한 강화학습 기반 스케줄링 알고리즘 개발

• Published in: 한국CDE학회 논문집 Vol. 26; no. 2; pp. 81 - 92
• Main Authors: 조영인(Young In Cho), 남소현(So Hyun Nam), 우종훈(Jong Hun Woo)
• Format: Journal Article
• Language: Korean
• Published: (사)한국CDE학회 01.06.2021; 한국CDE학회
• Subjects: 기계공학; Panel block shop; Reinforcement learning; Adaptive Scheduling; DES simulation
• ISSN: 2508-4003; 2508-402X
• DOI: 10.7315/CDE.2021.081

Rule-based heuristic algorithms and meta-heuristic algorithms have been studied to solve the scheduling problems of production systems. In recent research, reinforcement learning-based adaptive scheduling algorithms have been studied to solve complex problems with highdimensional and vast state space. A production system in shipyards is a high-variable system where various production factors such as space, workforce, and resources are related. Adaptive scheduling according to the changes in the production system and surrounding environment must be performed in shipyards. In this paper, the main focus was on building a basic reinforcement learning model for scheduling problems of shipyards. A simplified model of the panel block shop in shipyards was assumed and the optimal policy for determining the input sequence of blocks was learned to reduce the flow time. The open source-based DES simulation kernel Simpy was incorporated into the environment of the reinforcement learning model. KCI Citation Count: 4