A genetic algorithm for the integrated scheduling model of a container-handling system in a maritime terminal

In this paper, a genetic algorithm (GA) is proposed for the integrated scheduling problem of loading and unloading operations on handling and transportation equipment in a container terminal. This problem is a class of hybrid flow shop scheduling with unrelated parallel machines, precedence relationships, sequence-dependent set-up times, blocking, and machine eligibility as additional constraints. Considering these constraints, the proposed GA assigns jobs to machines at every stage through the evaluation function of the chromosomes. In addition to assignment, sequencing, timing, and makespan, the blocking times between stages and the congestion rate of routes are calculated in this model. A full factorial experimental design, using the analysis of variance (ANOVA) technique, is developed to calibrate different parameters and operators of the algorithm. The results indicate that some levels of parameters and operators lead to considerably better solutions. The experiments are based on data collected from Shaheed Rajaee Port (a port in the south of Iran) in order to analyse the performance of the algorithm.