Scheduling Optimization for Twin ASC in an Automated Container Terminal Based on Graph Theory

Abstract

Aiming at the twin automated stacking cranes (ASCs) scheduling problem in a single block of an automated container terminal, resolving conflicts between ASCs is an important point that needs to be considered. To solve this problem, a two-stage adaptive genetic algorithm (AGA) based on a graph theory model is proposed. The first stage of the algorithm reduced conflicts as much as possible by adjusting the order of container operations. In the second stage, when conflicts are unavoidable, conflicts are resolved by transforming conflicts into obstacle diagrams. Solving the completion time is to find the shortest distance in the diagram. The effectiveness of the algorithm is verified by applying the proposed algorithm to different scales of container numbers. Compared with the traditional seaside priority strategy, the graph theory model can shorten the completion time to varying degrees. For the strategy of setting the handshake area to reduce conflicts, the results show that the graph theory model makes ASC more efficient.