Mathematical modelling and efficient algorithms for autonomous straddle carriers planning at automated container terminals

Yuan, S

Mathematical modelling and efficient algorithms for autonomous straddle carriers planning at automated container terminals

Yuan, S

Permalink

Publication Type:: Thesis
Issue Date:: 2013

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download contents and abstractAdobe PDF (128.56 kB)

Adobe PDF

Download thesisAdobe PDF (3.89 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Yuan, S
dc.date.accessioned	2013-11-21T03:57:06Z
dc.date.available	2013-11-21T03:57:06Z
dc.date.issued	2013
dc.identifier.uri	http://hdl.handle.net/10453/24095
dc.description	University of Technology, Sydney. Faculty of Engineering and Information Technology.	en_US
dc.description.abstract	In the past several decades, automation of handling equipment has been a worldwide trend in seaport container terminals. Increasing automation of yard handling vehicles not only reduces the cost of terminal operation, but also increases the efficiency of container transport. However, the primary loss of performance in the transhipment process is caused by the uncoordinated allocation and scheduling of quay cranes, yard vehicles and land-side operations. Hence, integrating transhipment processes is imperative for a fully automated container terminal. This thesis aims to study an integrated process and develop practically deployable strategies and algorithms, with the practical example of the Patrick AutoStrad container terminal, located in Brisbane, Australia. The thesis first formulates two mathematical models: The Comprehensive Model is an analytical optimisation model which integrates the quay-side, yard and land-side operational sub-problems of the Patrick AutoStrad container terminal. Derived from the comprehensive model, the Job Scheduling Model is formulated to focus on the optimisation of job scheduling, as job scheduling plays a more important role than path planning, and resource utilisation and port operation are more dependent on job scheduling. To solve the Comprehensive Model, a job grouping approach is proposed for solving the integrated problem, and experimental results show that the job grouping approach can effectively improve the time related performance of planning container transfers. Solving the Job Scheduling Model using a global optimisation approach is expected to provide higher productivity in automated container terminals. Hence, a modified genetic algorithm is proposed for solving the job scheduling problem derived from the integrated mathematical model of container transfers. Moreover, the live testing results show that the proposed algorithm can effectively reduce the overall time-related cost of container transfers at the automated container terminal. Last but not least, a new crossover approach is proposed in order to further improve the solution quality based on the modified genetic algorithm, and it can also be directly applied in solving the generic multiple travelling salesmen problem using the two-part chromosome genetic algorithm. The experimental results also show that the proposed crossover approach statistically outperforms the existing approaches when solving the job scheduling problem and the standard multiple travelling salesmen problem.	en_US
dc.format	Thesis (PhD)	en_US
dc.language.iso	en	en_US
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/24095/2/02whole.pdf
dc.rights	au.edu.uts.lib/ppc
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Modelling.	en
dc.subject	Genetic algorithms.	en
dc.subject	Autonomous straddle carriers.	en
dc.subject	Container terminals.	en
dc.subject	Automation.	en
dc.subject	Brisbane, Qld.	en
dc.title	Mathematical modelling and efficient algorithms for autonomous straddle carriers planning at automated container terminals	en_US
dc.type	Thesis
utslib.copyright.status	open_access

Abstract:

In the past several decades, automation of handling equipment has been a worldwide trend in seaport container terminals. Increasing automation of yard handling vehicles not only reduces the cost of terminal operation, but also increases the efficiency of container transport. However, the primary loss of performance in the transhipment process is caused by the uncoordinated allocation and scheduling of quay cranes, yard vehicles and land-side operations. Hence, integrating transhipment processes is imperative for a fully automated container terminal. This thesis aims to study an integrated process and develop practically deployable strategies and algorithms, with the practical example of the Patrick AutoStrad container terminal, located in Brisbane, Australia. The thesis first formulates two mathematical models: The Comprehensive Model is an analytical optimisation model which integrates the quay-side, yard and land-side operational sub-problems of the Patrick AutoStrad container terminal. Derived from the comprehensive model, the Job Scheduling Model is formulated to focus on the optimisation of job scheduling, as job scheduling plays a more important role than path planning, and resource utilisation and port operation are more dependent on job scheduling. To solve the Comprehensive Model, a job grouping approach is proposed for solving the integrated problem, and experimental results show that the job grouping approach can effectively improve the time related performance of planning container transfers. Solving the Job Scheduling Model using a global optimisation approach is expected to provide higher productivity in automated container terminals. Hence, a modified genetic algorithm is proposed for solving the job scheduling problem derived from the integrated mathematical model of container transfers. Moreover, the live testing results show that the proposed algorithm can effectively reduce the overall time-related cost of container transfers at the automated container terminal. Last but not least, a new crossover approach is proposed in order to further improve the solution quality based on the modified genetic algorithm, and it can also be directly applied in solving the generic multiple travelling salesmen problem using the two-part chromosome genetic algorithm. The experimental results also show that the proposed crossover approach statistically outperforms the existing approaches when solving the job scheduling problem and the standard multiple travelling salesmen problem.

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

http://hdl.handle.net/10453/24095