Minimize makespan of permutation flowshop using pointer network

During the shipbuilding process, a block assembly line suffers a bottleneck when the largest amount of material is processed. Therefore, scheduling optimization is important for the productivity. Currently, sequence of inbound products is controlled by determining the input sequence using a heuristic or metaheuristic approach. However, the metaheuristic algorithm has limitations in that the computation time increases exponentially as the number of input objects increases, and separate optimization calculations are required for every problem. Also, the heuristic such as dispatching algorithm has the limitation of the exploring the problem domain. Therefore, this study tries a reinforcement learning algorithm based on a pointer network to overcome these limitations. Reinforcement learning with pointer network is found to be suitable for permutation flowshop problem, including input-order optimization. A trained neural network is applied without re-learning, even if the number of inputs is changed. The trained model shows the meaningful results compared with the heuristic and metaheuristic algorithms in makespan and computation time. The trained model outperforms the heuristic and metaheuristic algorithms within a limited range of permutation flowshop problem.

Automated summary

During the shipbuilding process, optimizing the scheduling of a block assembly line is crucial for productivity. This study proposes using a reinforcement learning algorithm based on a pointer network to improve the control of inbound product sequence. The trained model shows promising results compared to heuristic and metaheuristic algorithms in terms of makespan and computation time.