Towards a Deep Reinforcement Learning Model of Master Bay Stowage Planning

被引：1

作者：

Van Twiller, Jaike ^{[1
]}

Grbic, Djordje ^{[1
]}

Jensen, Rune Moller ^{[1
]}

机构：

[1] IT Univ Copenhagen, Rued Langgaards Vej 7, DK-2300 Copenhagen, Denmark

来源：

COMPUTATIONAL LOGISTICS, ICCL 2023 | 2023年 / 14239卷

关键词：

Maritime logistics; Liner shipping; Stowage planning; Deep reinforcement learning; Markov decision processes; CONTAINER SHIPS; METHODOLOGY; ALGORITHM; NUMBER; REDUCE;

D O I：

10.1007/978-3-031-43612-3_6

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

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.

引用

页码：105 / 121

页数：17

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