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Solving Container Pre-Marshalling Problem using Monte-Carlo Tree Search and Deep Neural Network

Liu, Jianwei (2023) Solving Container Pre-Marshalling Problem using Monte-Carlo Tree Search and Deep Neural Network. Master's Thesis, University of Pittsburgh. (Unpublished)

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The container pre-marshaling problem (CPMP) is a significant challenge in container terminal operations, aiming to optimize the relocation of containers to improve efficiency. Despite extensive research on exact and heuristic methods for the container relocation problem (CRP) and CPMP, reinforcement learning (RL) remains underexplored in the literature. This thesis proposes a Monte-Carlo Tree Search (MCTS) method combined with the Lowest Priority First Heuristic (LPFH) to solve the CPMP efficiently.

The MCTS method incorporates the LPFH heuristic to achieve consistent simulation results and minimize the number of movements needed for container relocation. Our approach achieves near state-of-the-art results in several instances with acceptable inference speed. Additionally, this thesis introduces a machine learning-based method to estimate the number of relocations required for a given CPMP configuration. We train a deep learning model with both convolutional neural network (CNN) and multi-layer perceptron (MLP) architectures on self-generated data, identifying important features to achieve over 90% classification accuracy in small instances.

The proposed approach has the potential to provide more efficient and effective solutions to the CPMP than traditional optimization methods. Overall, this thesis contributes to the CPMP literature by introducing novel methods for solving the problem and providing valuable insights into the potential of machine learning and RL for solving complex optimization problems.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Liu, Jianweijil264@pitt.edujil2640009-0009-4026-045X
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorBo,
Committee MemberRajgopal,
Committee MemberLee,
Date: 13 June 2023
Date Type: Publication
Defense Date: 12 April 2023
Approval Date: 13 June 2023
Submission Date: 13 April 2023
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 38
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Industrial Engineering
Degree: MSIE - Master of Science in Industrial Engineering
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: reinforcement learning, machine learning, convolutional neural network, multi-layer perceptron
Date Deposited: 13 Jun 2023 14:06
Last Modified: 13 Jun 2023 14:06

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  • Solving Container Pre-Marshalling Problem using Monte-Carlo Tree Search and Deep Neural Network. (deposited 13 Jun 2023 14:06) [Currently Displayed]


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