An agent-based approach to modeling yard cranes at seaport container terminals

Abstract

Due to environmental concerns, terminal operators at seaport container terminals are increasingly looking to reduce the time a truck spends at the terminal to complete a transaction. For terminals that stack their containers, the solution may seem obvious: add more yard cranes to reduce trucks' wait time in the yard. However, the high cost of these cranes often prohibits terminal operators from freely buying more. Another reason is because there is no clear understanding of how the yard cranes' availability and service strategy affect truck turn time. This study introduces an agent-based approach to model yard cranes for the analysis of truck turn time with respect to service strategy. It is accomplished by modeling the cranes as utility-maximizing agents. This study has identified a set of utility functions that properly capture the essential decision making process of crane operators in choosing the next truck to provide service to. The agent-based model is implemented using NetLogo, a cross-platform multi-agent programmable modeling environment. Simulation results show that the distance-based service strategy produces the best results in terms of average waiting time and the maximum waiting time of any truck.