Multi-AGV scheduling for conflict-free path planning in automated container terminals

Abstract

Path planning and integrated scheduling are two important problems to be resolved in the design of any automated container terminal. There has been relatively little research, however, on automated guided vehicles (AGVs) conflict-free path planning with quay cranes (QCs) and rail-mounted gantry (RMG) cranes. This paper combines the two problems to realize the integrated scheduling of multi-AGV with conflict-free path planning. A mixed integer programming model based on path optimization, integrated scheduling, and conflicts and deadlocks is established to minimize AGVs delay time under the condition that the task allocation is known. A series of small-scale and large-scale experiments are conducted to validate the availability of Hybrid Genetic Algorithm-Particle Swarm Optimization (HGA-PSO) with fuzzy logic controller to adaptive auto tuning. Dynamic simulation of the path nodes indicates that the proposed model indeed can resolve the AGV conflict and deadlock problem and may be practically applicable to existing automated container terminals.