조선소 병렬 기계 공정에서의 납기 지연 및 셋업 변경 최소화를 위한 강화학습 기반의 생산라인 투입순서 결정
Reinforcement Learning for Minimizing Tardiness and Set-Up Change in Parallel Machine Scheduling Problems for Profile Shops in Shipyard
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초록
The profile shops in shipyards produce section steels required for block production of ships. Due to the limitations of shipyard’s production capacity, a considerable amount of work is already outsourced. In addition, the need to improve the productivity of the profile shops is growing because the production volume is expected to increase due to the recent boom in the shipbuilding industry. In this study, a scheduling optimization was conducted for a parallel welding line of the profile process, with the aim of minimizing tardiness and the number of set-up changes as objective functions to achieve productivity improvements. In particular, this study applied a dynamic scheduling method to determine the job sequence considering variability of processing time. A Markov decision process model was proposed for the job sequence problem, considering the trade-off relationship between two objective functions. Deep reinforcement learning was also used to learn the optimal scheduling policy. The developed algorithm was evaluated by comparing its performance with priority rules (SSPT, ATCS, MDD, COVERT rule) in test scenarios constructed by the sampling data. As a result, the proposed scheduling algorithms outperformed than the priority rules in terms of set-up ratio, tardiness, and makespan.
Abstract
The profile shops in shipyards produce section steels required for block production of ships. Due to the limitations of shipyard’s production capacity, a considerable amount of work is already outsourced. In addition, the need to improve the productivity of the profile shops is growing because the production volume is expected to increase due to the recent boom in the shipbuilding industry. In this study, a scheduling optimization was conducted for a parallel welding line of the profile process, with the aim of minimizing tardiness and the number of set-up changes as objective functions to achieve productivity improvements. In particular, this study applied a dynamic scheduling method to determine the job sequence considering variability of processing time. A Markov decision process model was proposed for the job sequence problem, considering the trade-off relationship between two objective functions. Deep reinforcement learning was also used to learn the optimal scheduling policy. The developed algorithm was evaluated by comparing its performance with priority rules (SSPT, ATCS, MDD, COVERT rule) in test scenarios constructed by the sampling data. As a result, the proposed scheduling algorithms outperformed than the priority rules in terms of set-up ratio, tardiness, and makespan.
참고문헌 (13)
- Chaudhry, I.A., "Minimisation of total tardiness for identical parallel machine scheduling using genetic algorithm", Sādhanā, 42(1), pp.11~21 (2017)
- Chen, C.L., "Hybrid metaheuristics for unrelated parallel machine scheduling with sequencedependent setup times", The International Journal of Advanced Manufacturing Technology, 43(1), pp.161~169 (2009)
- Julaiti, J., "Stochastic parallel machine scheduling using reinforcement learning", Journal of Advanced Manufacturing and Processing, 4(4), pp.e10119~ (2022)
- Kim, D. W., "Unrelated parallel machine scheduling with setup times using simulated annealing", Robotics and Computer-Integrated Manufacturing, 18(3-4), pp.223~231 (2002)
- Lee, C. H., "A dispatching rule and a random iterated greedy metaheuristic for identical parallel machine scheduling to minimize total tardiness", International journal of production research, 56(6), pp.2292~2308 (2018)
- Lee, J. H., "A tabu search algorithm for unrelated parallel machine scheduling with sequence-and machine-dependent setups: minimizing total tardiness", The International Journal of Advanced Manufacturing Technology, 69(9), pp.2081~2089 (2013)
- Lee, J.W., "Erection process planning &scheduling using genetic algorithm", Journal of the Society of Naval Architects of Korea, 32(1), pp.9~16 (1995)
- Paeng, B., "Deep reinforcement learning for minimizing tardiness in parallel machine scheduling with sequence dependent family setups", IEEE Access, 9, pp.101390~101401 (2021)
- 박중구, "정수 선형 최적화를 이용한 조선해양 의장품 제작 물량 할당에 관한 연구", 대한조선학회 논문집, 57(1), pp.45~51 (2020), 대한조선학회[DOI]
- Schulman, J., "Proximal policy optimization algorithms", arXiv preprint arXiv:1707.06347, ~ (2017)
- 손정열, "조선 산업의 블록 적치장 운영계획 휴리스틱알고리즘", 대한조선학회 논문집, 51(3), pp.239~245 (2014), 대한조선학회
- Zhang, Z., "Minimizing mean weighted tardiness in unrelated parallel machine scheduling with reinforcement learning", Computers & operations research, 39(7), pp.1315~1324 (2012)
- Zhang, Z., "Dynamic parallel machine scheduling with mean weighted tardiness objective by Q-Learning", The International Journal of Advanced Manufacturing Technology, 34(9), pp.968~980 (2007)