港口陆域堆场调度优化模型输出数据

港口陆域堆场调度优化模型输出数据主要面向集装箱港口陆域堆场集装箱调度算法研究，旨在通过历史数据学习堆场调度管理规则，提高堆场利用率，降低人工调度的风险，节省人工成本，有利于提升港口智能化运营水平。基于堆场实际布局规则和集装箱实际堆存、周转情况，生成虚拟数据训练岸桥调度模型。主要记录了强化学习模型中场桥智能体的观测值、动作、奖励等信息。数据量19.5M。

The output data of the port land yard scheduling optimization model is primarily intended for research on container scheduling algorithms in container port land yards. It is designed to learn yard scheduling management rules from historical data, thereby enhancing yard utilization efficiency, mitigating risks from manual scheduling, reducing labor costs, and advancing the intelligent operation level of ports. Virtual data is generated based on the actual layout rules of the container yard and the actual stacking and turnover status of containers for training the quay crane scheduling model. The dataset mainly records the observations, actions, rewards, and other relevant information of the yard crane agent in the reinforcement learning model. The total size of the dataset is 19.5M.

搜集汇总

背景与挑战

背景概述

该数据集专注于集装箱港口陆域堆场的调度优化问题，通过生成虚拟数据记录强化学习模型中场桥智能体的观测值、动作和奖励信息，旨在提高堆场利用率和降低人工成本。数据来源于国家重点研发计划项目，支持港口智能化运营研究，包含39个CSV和DOCX文件，总数据量约23.55MB。