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 inter-dependencies 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.