Container batch grouping for automated container terminals

What the paper says

Purpose In automated container terminals, mutual waiting between yard cranes (YCs) and automated guided vehicles (AGVs) causes resource wastage, extended task execution times and traffic congestion. This study proposes a strategy called batch grouping, which classifies containers by size, weight and destination for interchangeable handling, effectively reducing waiting times. Design/methodology/approach A mixed-integer programming model is developed to minimize YC costs by incorporating constraints on the number of similar containers to be handled at each time point into the classic YC scheduling optimization model. Additionally, a mixed-integer programming model is constructed to minimize AGV costs, which includes AGV capacity constraints based on the classic AGV scheduling optimization model. A hybrid heuristic algorithm and GUROBI software are used to solve these models. Findings Experimental results show that integrating classification and interchangeability into the joint scheduling of YCs and AGVs achieves a cost reduction of up to 8.9%. Practical implications Terminal operators can implement these findings to streamline their scheduling processes, leading to improved efficiency and reduced traffic congestion within the terminal environment. Originality/value This study contributes to the field of terminal operations by introducing an innovative batch grouping strategy that addresses the critical issue of mutual waiting between YCs and AGVs.