积载
强化学习
计算机科学
可扩展性
端口(电路理论)
运筹学
多样性(控制论)
人工智能
海湾
建筑
工程类
土木工程
数据库
艺术
电气工程
结构工程
视觉艺术
作者
Jaike van Twiller,Djordje Grbic,Rune Møller Jensen
标识
DOI:10.1007/978-3-031-43612-3_6
摘要
Major liner shipping companies aim to solve the stowage planning problem by optimally allocating containers to vessel locations during a multi-port voyage. Due to a large variety of combinatorial aspects, a scalable algorithm to solve a representative problem is yet to be found. This paper will show that deep reinforcement learning can optimize a non-trivial master bay planning problem. Our experiments show that proximal policy optimization efficiently finds reasonable solutions, serving as preliminary evidence of the potential value of deep reinforcement learning in stowage planning. In future work, we will extend our architecture to address a full-featured master bay planning problem.
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