A Deep Q-Learning Network for Ship Stowage Planning Problem
Ship stowage plan is the management connection of quae crane scheduling and yard crane scheduling. The quality of ship stowage plan affects the productivity greatly. Previous studies mainly focuses on solving stowage planning problem with online searching algorithm, efficiency of which is significan...
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| Vydané v: | Polish maritime research Ročník 24; číslo 1; s. 102 - 109 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Gdansk
Sciendo
27.11.2017
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services |
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| ISSN: | 2083-7429, 1233-2585, 2083-7429 |
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| Abstract | Ship stowage plan is the management connection of quae crane scheduling and yard crane scheduling. The quality of ship stowage plan affects the productivity greatly. Previous studies mainly focuses on solving stowage planning problem with online searching algorithm, efficiency of which is significantly affected by case size. In this study, a Deep Q-Learning Network (DQN) is proposed to solve ship stowage planning problem. With DQN, massive calculation and training is done in pre-training stage, while in application stage stowage plan can be made in seconds. To formulate network input, decision factors are analyzed to compose feature vector of stowage plan. States subject to constraints, available action and reward function of Q-value are designed. With these information and design, an 8-layer DQN is formulated with an evaluation function of mean square error is composed to learn stowage planning. At the end of this study, several production cases are solved with proposed DQN to validate the effectiveness and generalization ability. Result shows a good availability of DQN to solve ship stowage planning problem. |
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| AbstractList | Ship stowage plan is the management connection of quae crane scheduling and yard crane scheduling. The quality of ship stowage plan affects the productivity greatly. Previous studies mainly focuses on solving stowage planning problem with online searching algorithm, efficiency of which is significantly affected by case size. In this study, a Deep Q-Learning Network (DQN) is proposed to solve ship stowage planning problem. With DQN, massive calculation and training is done in pre-training stage, while in application stage stowage plan can be made in seconds. To formulate network input, decision factors are analyzed to compose feature vector of stowage plan. States subject to constraints, available action and reward function of Q-value are designed. With these information and design, an 8-layer DQN is formulated with an evaluation function of mean square error is composed to learn stowage planning. At the end of this study, several production cases are solved with proposed DQN to validate the effectiveness and generalization ability. Result shows a good availability of DQN to solve ship stowage planning problem. |
| Author | Shen, Yifan Du, Xueqiang Zhao, Ning Xia, Mengjue |
| Author_xml | – sequence: 1 givenname: Yifan surname: Shen fullname: Shen, Yifan organization: Scientific Research Academy, Shanghai Maritime University, Shanghai, China – sequence: 2 givenname: Ning surname: Zhao fullname: Zhao, Ning organization: Logistics Engineering College, Shanghai Maritime University, Shanghai, China – sequence: 3 givenname: Mengjue surname: Xia fullname: Xia, Mengjue organization: Scientific Research Academy, Shanghai Maritime University, Shanghai, China – sequence: 4 givenname: Xueqiang surname: Du fullname: Du, Xueqiang organization: Logistics Engineering College, Shanghai Maritime University, Shanghai, China |
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| SubjectTerms | Container terminal Decision analysis Deep Q-Leaning Network (DQN) Evaluation Generalization Markov decision process Planning Scheduling Ship stowage plan Ships Stowage (onboard equipment) Training Value function approximation World class companies |
| Title | A Deep Q-Learning Network for Ship Stowage Planning Problem |
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