An improved moss growth optimization algorithm for multi-equipment cooperative scheduling in automated container terminals under AGV failures

The automated guided vehicle (AGV) failures cause operational interruptions in automated container terminals. This study investigates the multi-equipment cooperative scheduling problem involving quay cranes, AGVs, and yard cranes. A new mathematical programming model is formulated to minimize the operational makespan and energy consumption. Considering AGV failures, the deviation of rescheduling plan is concerned to propose a rescheduling optimization model. An improved moss growth optimization (IMGO) algorithm is designed with the Logistic-Tent mapping, adaptive disturbance strategy, and parallel computing method. Experimental results demonstrate the models could solve small-scale cases optimally. By comparison, the IMGO algorithm exhibits significantly faster solving time, and the average deviations from exact solutions are quite small. For medium/large-scale cases, the IMGO algorithm outperformed the standard MGO algorithm and co-learning imperial competition algorithm in solution quality and efficiency. Furthermore, the impacts of different operation modes and equipment configuration ratios on the operational efficiency and energy consumption are analyzed.