Dynamic configuration of QC allocating problem based on multi-objective genetic algorithm

ChengJi Liang, MiaoMiao Li, Bo Lu (), Tianyi Gu, Jungbok Jo and Yi Ding
Additional contact information
ChengJi Liang: Shanghai Maritime University
MiaoMiao Li: Shanghai Maritime University
Bo Lu: Dalian University
Tianyi Gu: Shanghai International Port Group Co., Ltd
Jungbok Jo: Dongseo University
Yi Ding: Shanghai Maritime University

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 3, No 35, 847-855

Abstract: Abstract Solving the problem of allocating and scheduling quay cranes (QCs) is very important to ensure favorable port service. This work proposes a bi-criteria mixed integer programming model of the continual and dynamic arrival of several vessels at a port. A multi-objective genetic algorithm is applied to solve the problem in three cases. The results thus obtained confirm the feasibility and effectiveness of the model and GA. Additionally, the multi-objective solution considering both the total duration for which vessels stay in the port and QCs move is the best, as determined by comparing with considering only the total time for which vessels stay in the port or QCs move, as it considers, and it balances these two objectives.

Keywords: Port facilities; Multi-objective genetic algorithm; Quay crane scheduling; Pareto optimal solution (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-015-1035-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:28:y:2017:i:3:d:10.1007_s10845-015-1035-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-015-1035-7

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().