Integrated quay crane and automated guided vehicle scheduling with charging decisions

Charging decisions for automated guided vehicles (AGVs) significantly impact operational efficiency in automated container terminals, yet most integrated scheduling studies neglect them or focus only on AGV-yard crane coordination with predetermined quay crane (QC) sequences. Thus, a notable deficiency persists in research on integrated QC and AGV scheduling owing to the failure to take into consideration charging requirements. This article addresses the gap by proposing a mixed integer programming (MIP) model that jointly optimizes QC-AGV scheduling and AGV charging while considering QC non-crossing constraints. An improved particle swarm optimization (IPSO) algorithm that solves large-scale instances has been developed, and it outperforms existing PSO methods in both makespan and energy consumption. Additionally, the proposed IPSO can quickly obtain feasible solutions in small instances with an average optimality gap of 3%. Sensitivity analysis is also conducted on several parameters, providing valuable insights for port managers wishing to develop efficient AGV charging and equipment scheduling strategies.