arrow
返回

Target-guided algorithms for the container pre-marshalling problem

delete2015-06-01
delete52
PRE
AI
N
Ning Wang
B
Bo Jin *
A
Andrew Lim
DOI:10.1016/j.omega.2014.12.002delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
The container pre-marshalling problem (CPMP) aims to rearrange containers in a bay with the least movement effort; thus, in the final layout, containers are piled according to a predetermined order. Previous researchers, without exception, assumed that all the stacks in a bay are functionally identical. Such a classical problem setting is reexamined in this paper. Moreover, a new problem, the CPMP with a dummy stack (CPMPDS) is proposed. At terminals with transfer lanes, a bay includes a row of ordinary stacks and a dummy stack. The dummy stack is actually the bay space that is reserved for trucks. Therefore, containers can be shipped out from the bay. During the pre-marshalling process, the dummy stack temporarily stores containers as an ordinary stack. However, the dummy stack must be emptied at the end of pre-marshalling. In this paper, target-guided algorithms are proposed to handle both the classical CPMP and new CPMPDS. All the proposed algorithms guarantee termination. Experimental results in terms of the CPMP show that the proposed algorithms surpass the state-of-the-art algorithm. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Container pre-marshalling problem
Target-guided algorithm
Dummy stack
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

O
Omega-International Journal of Management Science
IF:
7.2
论文数:
3.7K
被引数:
1.4W

机构

N
Nanyang Technological University
学者数:
4.9W
论文数: 4.8W
被引数: 8.1W
C
City University of Hong Kong
学者数:
2.3W
论文数: 3.0W
被引数: 6.1W
S
shanghai university
学者数:
3.9W
论文数: 2.7W
被引数: 52
学者 查看更多机构
引用论文

引用论文

err分享
err收藏
err
IF0
err
err0
PREAI
err
err分享
err收藏
err分享
err收藏
Randomized Trial Testing the Effects of Eating Frequency on Two Hormonal Biomarkers of Metabolism and Energy Balance
err2016-12-05
err0
errOAAI
errMartine M. Perrigue; Adam Drewnowski; Ching-Yun Wang; Xiaoling Song; Mario Kratz; Marian L. Neuhouser
err分享
err收藏
学者 查看更多内容