Optimisation for job scheduling at automated container terminals using genetic algorithm

  • Authors:

  • Bradley Skinner;Shuai Yuan;Shoudong Huang;Dikai Liu;Binghuang Cai;Gamini Dissanayake;Haye Lau;Andrew Bott;Daniel Pagac

  • Affiliations:

  • School of Electrical, Mechanical and Mechatronic Systems, Faculty of Engineering and Information Technology (FEIT), University of Technology, PO Box 123, Sydney, NSW 2007, Australia;School of Electrical, Mechanical and Mechatronic Systems, Faculty of Engineering and Information Technology (FEIT), University of Technology, PO Box 123, Sydney, NSW 2007, Australia;School of Electrical, Mechanical and Mechatronic Systems, Faculty of Engineering and Information Technology (FEIT), University of Technology, PO Box 123, Sydney, NSW 2007, Australia;School of Electrical, Mechanical and Mechatronic Systems, Faculty of Engineering and Information Technology (FEIT), University of Technology, PO Box 123, Sydney, NSW 2007, Australia;School of Electrical, Mechanical and Mechatronic Systems, Faculty of Engineering and Information Technology (FEIT), University of Technology, PO Box 123, Sydney, NSW 2007, Australia;School of Electrical, Mechanical and Mechatronic Systems, Faculty of Engineering and Information Technology (FEIT), University of Technology, PO 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;Patrick Technology Systems, 4b Lord Street, Botany, NSW 2019, Australia

  • Venue:
  • Computers and Industrial Engineering
  • Year:
  • 2013

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Abstract

This paper presents a genetic algorithm (GA)-based optimisation approach to improve container handling operations at the Patrick AutoStrad container terminal located in Brisbane Australia. In this paper we focus on scheduling for container transfers and encode the problem using a two-part chromosome approach which is then solved using a modified genetic algorithm. In simulation experiments, the performance of the GA-based approach and a sequential job scheduling method are evaluated and compared with different scheduling scenarios. The experimental results show that the GA-based approach can find better solutions which improve the overall performance. The GA-based approach has been implemented in the terminal scheduling system and the live testing results show that the GA-based approach can reduce the overall time-related cost of container transfers at the automated container terminal.