Optimisation for job scheduling at automated container terminals using genetic algorithm

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.

► We present a modified genetic algorithm (GA)-based optimisation approach.
► We describe and modify the problem model for container transfers.
► The GA-based approach outperforms the sequential job scheduling method.
► The GA-based approach improves the actual scheduling performance.