A job grouping approach for planning container transfers at automated seaport container terminals

Authors:
S. Yuan;B. T. Skinner;S. Huang;D. K. Liu;G. Dissanayake;H. Lau;D. Pagac
Affiliations:
ARC Centre of Excellence for Autonomous Systems (CAS), University of Technology, Sydney, P.O. Box 123, Sydney, NSW 2007, Australia;ARC Centre of Excellence for Autonomous Systems (CAS), University of Technology, Sydney, P.O. Box 123, Sydney, NSW 2007, Australia;ARC Centre of Excellence for Autonomous Systems (CAS), University of Technology, Sydney, P.O. Box 123, Sydney, NSW 2007, Australia;ARC Centre of Excellence for Autonomous Systems (CAS), University of Technology, Sydney, P.O. Box 123, Sydney, NSW 2007, Australia;ARC Centre of Excellence for Autonomous Systems (CAS), University of Technology, Sydney, P.O. Box 123, Sydney, NSW 2007, Australia;Patrick Technology Systems, 4b Lord Street, Botany, NSW 2019, Australia;Patrick Technology Systems, 4b Lord Street, Botany, NSW 2019, Australia
Venue:
Advanced Engineering Informatics
Year:
2011

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Abstract

This paper proposes a practical job grouping approach, which aims to enhance the time related performance metrics of container transfers in the Patrick AutoStrad container terminal, located in Brisbane, Australia. It first formulates a mathematical model of the automated container transfers in a relatively complex environment. Apart from the consideration on collision avoidance of a fleet of large vehicles in a confined area, it also deals with many other difficult practical challenges such as the presence of multiple levels of container stacking and sequencing, variable container orientations, and vehicular dynamics that require finite acceleration and deceleration times. The proposed job grouping approach aims to improve the makespan of the schedule for yard jobs, while reducing straddle carrier waiting time by grouping jobs using a guiding function. The performance of the current sequential job allocation method and the proposed job grouping approach are evaluated and compared statistically using a pooled t-test for 30 randomly generated yard configurations. The experimental results show that the job grouping approach can effectively improve the schedule makespan and reduce the total straddle carrier waiting time.