Data-driven dynamic stacking strategy for export containers in container terminals

Park Hyun Ji; Cho Sung Won; Nanda AbhilashaPark Jin Hyoung

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Data-driven dynamic stacking strategy for export containers in container terminals

机译：Data-driven dynamic stacking strategy for export containers in container terminals

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摘要

This study investigates a method for improving real-time decisions regarding the storage location of export containers while the containers are arriving. To manage the decision-making process, we propose a two module-based data-driven dynamic stacking strategy that facilitates stowage planning. Module 1 generates the Gaussian mixture model (GMM) specific to each container group for container weight classification. Module 2 implements the data-driven dynamic stacking strategy as an online algorithm to determine the storage location of an arriving container in real time. Numerical experiments were conducted using real-life data to validate the effectiveness of the proposed method compared to other alternative stacking strategies. These experiments revealed that the performance of the proposed method is robust, and therefore it can improve yard operations and container terminal competitiveness.

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来源
《Flexible Services and Manufacturing Journal》 |2023年第1期|170-195|共26页
作者
Park Hyun Ji; Cho Sung Won; Nanda AbhilashaPark Jin Hyoung;
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作者单位

Korea Res Inst Ships & Ocean Engn;

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原文格式 PDF
正文语种英语
中图分类柔性制造系统及柔性制造单元;
关键词
Container terminals; Container stacking problem (CSP); Machine learning; Gaussian mixture model (GMM); DERIVING DECISION RULES; OUTBOUND CONTAINERS; SPACE ALLOCATION; LOCATION ASSIGNMENT; OPERATIONS-RESEARCH; IMPORT CONTAINERS; SUPPORT-SYSTEM; STORAGE SPACE; TRANSSHIPMENT; MODELS;

Data-driven dynamic stacking strategy for export containers in container terminals

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