The Influence of Intelligent Guided Vehicle Configuration on Equipment Scheduling in the Railway Yards of Automated Container Terminals

• Published in: Journal of Marine Science and Engineering
• Main Authors: Hongbin Chen, Wei Liu
• Format: Article
• Language: English
• Published: MDPI AG 2024-09-01
• Subjects: IGV; ARMG; adaptive large neighborhood search; railway yards of automated container terminals
• Online Access: https://www.mdpi.com/2077-1312/12/10/1713

The efficiency of collecting and distributing goods has been improved by establishing railway lines that serve new automated container terminals (ACTs) and by constructing central railway stations close to ports. To aid in this process, intelligent guided vehicles (IGVs), which are renowned for their flexibility and for the convenience with which one can adjust their number and speed, have been developed to be used as horizontal transport vehicles that can transport goods between the railway yard and the front of the port. However, they also introduce some difficulties and complexities that affect terminal scheduling. Therefore, we took the automated rail-mounted container gantry crane (ARMG) scheduling problem as our main research object in this study. We established a mixed-integer linear programming (MILP) model to minimize the makespan of ARMGs, designed an adaptive large neighborhood search (ALNS) algorithm, and explored the influence of IGV configuration on ARMG scheduling through a series of experiments applied to a series of large-scale numerical examples. The experimental results show that increasing the number of IGVs can improve the operational efficiency of railway yards, but this strategy reduces the overall time taken for the ARMG to complete various tasks. Increasing or decreasing the speed of the IGVs within a given range has a clear effect on the problem at hand, while increasing the IGV travel speed can effectively reduce the time required for the ARMG to complete various tasks. Operators must properly adjust the IGV speed to meet the requirements of the planned operation.