Multi-objective and prioritized berth allocation in container ports

Cheong, CY; Tan, KC; Liu, DK; Lin, CJ

Multi-objective and prioritized berth allocation in container ports

Cheong, CY Tan, KC Liu, DK

Lin, CJ

Permalink

Publication Type:: Journal Article
Citation:: Annals of Operations Research, 2010, 180 (1), pp. 63 - 103
Issue Date:: 2010-01-01

Closed Access

	Filename	Description	Size
	2009006531OK.pdf		2.51 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Cheong, CY	en_US
dc.contributor.author	Tan, KC	en_US
dc.contributor.author	Liu, DK https://orcid.org/0000-0002-1581-5582	en_US
dc.contributor.author	Lin, CJ	en_US
dc.date.issued	2010-01-01	en_US
dc.identifier.citation	Annals of Operations Research, 2010, 180 (1), pp. 63 - 103	en_US
dc.identifier.issn	0254-5330	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13498
dc.description.abstract	This paper considers a berth allocation problem (BAP) which requires the determination of exact berthing times and positions of incoming ships in a container port. The problem is solved by optimizing the berth schedule so as to minimize concurrently the three objectives of makespan, waiting time, and degree of deviation from a predetermined priority schedule. These objectives represent the interests of both port and ship operators. Unlike most existing approaches in the literature which are single-objective-based, a multi-objective evolutionary algorithm (MOEA) that incorporates the concept of Pareto optimality is proposed for solving the multi-objective BAP. The MOEA is equipped with three primary features which are specifically designed to target the optimization of the three objectives. The features include a local search heuristic, a hybrid solution decoding scheme, and an optimal berth insertion procedure. The effects that each of these features has on the quality of berth schedules are studied. © 2008 Springer Science+Business Media, LLC.	en_US
dc.relation.ispartof	Annals of Operations Research	en_US
dc.relation.isbasedon	10.1007/s10479-008-0493-0	en_US
dc.subject.classification	Operations Research	en_US
dc.title	Multi-objective and prioritized berth allocation in container ports	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	180	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	180	en_US

Abstract:

This paper considers a berth allocation problem (BAP) which requires the determination of exact berthing times and positions of incoming ships in a container port. The problem is solved by optimizing the berth schedule so as to minimize concurrently the three objectives of makespan, waiting time, and degree of deviation from a predetermined priority schedule. These objectives represent the interests of both port and ship operators. Unlike most existing approaches in the literature which are single-objective-based, a multi-objective evolutionary algorithm (MOEA) that incorporates the concept of Pareto optimality is proposed for solving the multi-objective BAP. The MOEA is equipped with three primary features which are specifically designed to target the optimization of the three objectives. The features include a local search heuristic, a hybrid solution decoding scheme, and an optimal berth insertion procedure. The effects that each of these features has on the quality of berth schedules are studied. © 2008 Springer Science+Business Media, LLC.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/13498