运筹学
计算机科学
转运(资讯保安)
调度(生产过程)
容器(类型理论)
收入
地铁列车时刻表
动态优先级调度
作业车间调度
数学优化
工程类
运营管理
机械工程
数学
会计
业务
操作系统
作者
Xincheng Xiang,Loo Hay Lee,Ek Peng Chew
出处
期刊:IEEE Transactions on Automation Science and Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-15
被引量:1
标识
DOI:10.1109/tase.2023.3267448
摘要
This study considers the multi-period dynamic integrated optimization problem in automated transshipment hubs, which includes berth allocation, quay crane assignment, and yard assignment problems. The planner needs to determine the berth and storage schedule in real-time. In the previous literature, the scholars always assume that all of the required information, especially the arrival time and operation time of vessels, is accessible prior to making any design decisions, and formulate the problem into an integrated model. However, the information of vessels is updated dynamically and changed frequently. Therefore, we consider a problem in a dynamic setting that decisions are made in each period and the scenario at each period depends on the decisions in previous periods. We formulate the problem into a multi-objective model, which aims to maximize total revenue, saved time deviation, saved transportation distance, and service quality. An efficient adaptive dynamic scheduling policy is developed, which adaptively balances the trade-offs between multiple objectives. Through numerical experiments, we demonstrate that our approach presents solutions with a delicate balance between multiple objectives and brings value to the automated container terminals compared to benchmark policies. Note to Practitioners —Automated technologies have brought great opportunities for container terminals. While the construction of an automated container terminal requires enormous investment. Utilizing the resources of container terminals reasonably and efficiently can improve the operation efficiency and economic performance of automated container terminals. In practice, the scheduling of resources in container terminals always encounters uncertain factors. Therefore, this study proposes a dynamic integrated optimization policy for resources in automated container terminals to balance the trade-offs between multiple objectives given any pre-determined target in a dynamic environment.
科研通智能强力驱动
Strongly Powered by AbleSci AI