The efficiency of yard operations is critical to the overall productivity of a container terminal because the yard serves as the interface between the landside and waterside operations.Most container terminals use yar...The efficiency of yard operations is critical to the overall productivity of a container terminal because the yard serves as the interface between the landside and waterside operations.Most container terminals use yard cranes to transfer containers between the yard and trucks(both external and internal).To facilitate vessel operations,an efficient work schedule for the yard cranes is necessary given varying work volumes among yard blocks with different planning periods.This paper investigated an agent-based approach to assign and relocate yard cranes among yard blocks based on the forecasted work volumes.The goal of our study is to reduce the work volume that remains incomplete at the end of a planning period.We offered several preference functions for yard cranes and blocks which are modeled as agents.These preference functions are designed to find effective schedules for yard cranes.In addition,we examined various rules for the initial assignment of yard cranes to blocks.Our analysis demonstrated that our model can effectively and efficiently reduce the percentage of incomplete work volume for any real-world sized problem.展开更多
文摘The efficiency of yard operations is critical to the overall productivity of a container terminal because the yard serves as the interface between the landside and waterside operations.Most container terminals use yard cranes to transfer containers between the yard and trucks(both external and internal).To facilitate vessel operations,an efficient work schedule for the yard cranes is necessary given varying work volumes among yard blocks with different planning periods.This paper investigated an agent-based approach to assign and relocate yard cranes among yard blocks based on the forecasted work volumes.The goal of our study is to reduce the work volume that remains incomplete at the end of a planning period.We offered several preference functions for yard cranes and blocks which are modeled as agents.These preference functions are designed to find effective schedules for yard cranes.In addition,we examined various rules for the initial assignment of yard cranes to blocks.Our analysis demonstrated that our model can effectively and efficiently reduce the percentage of incomplete work volume for any real-world sized problem.
