Multi-agent Based Truck Scheduling Using Ant Colony Intelligence in a Cross-Docking Platform

The management of trucks in a cross-docking platform is a process under five steps: the arrival, the control, the unloading, the transfer and finally the loading. In each of these steps, a sequence of decisions arise. To achieve an optimal and robust solutions, the interdependencies between the different planning functions should be taken into account, and scheduling decisions must be made simultaneously. The truck scheduling should incorporate a real-time information regarding the resource availability and truck arrival and departure times which are crucial in a cross-docking platform. In this work, we present how the autonomous, distributed, and dynamic nature of the multi-agent paradigm by introducing ant colony intelligence (ACI) can provide a framework for the cooperation of various functions of the cross-dock to develop a robust schedule. The goal of this paper is to find an optimal dynamic scheduling system related to the parking lot and dock operations at the cross-dock facility. The proposed approach represents ACI integrated with both truck agents and resource agents to solve the truck scheduling problem in a dynamic environment.