Joint scheduling optimization model for yard cranes and AGVs considering running routes

Abstract

The escalation in global consumer demand has prompted the continual expansion of container throughput, which drives container terminals to bolster their production capacities. Globally, ports worldwide have been adopting automated equipment and embracing digital operational environments to reinforce their logistics system efficiency and curtail transportation costs. Enhancement of the efficiency of container transportation at automated terminals depends on terminal loading and unloading operations. The efficiency of these operations directly hinges on the automated mechanical equipment deployed at terminals. Consequently, optimization of resource allocation and production scheduling of loading and unloading equipment at terminals has become an essential and effective approach. In this regard, automated yard cranes, which are responsible for rear-end handling, and automated guided vehicles (AGVs), which transport containers between the yard and the terminal quay, have been proven indispensable. This paper principally delves into the scheduling optimization of mechanical equipment in automated container terminal yards, with a unique focus on the coordinated scheduling optimization of yard cranes and AGVs while also accounting for AGV path planning. This research centers on the automated container terminal yard system and the challenge in yard crane scheduling. This work also establishes a grid-based terminal network model and validates the model and algorithm using practical examples. Optimal solutions are obtained for the joint scheduling of yard cranes and AGVs, accompanied by strategies on rational AGV path selection. The findings provide valuable references and theoretical underpinnings for operational scheduling and enhancement of the efficiency in container transport in automated container terminal yards. Furthermore, these results facilitate the overall improvement of production efficiency at automated terminals and the reduction of operational costs, thus carrying theoretical and practical significance.