A new class of Intelligent and Autonomous Vehicles (IAVs) has been designed in the framework of Intelligent Transportation for Dynamic Environment (InTraDE) project funded by the European Union. This type of vehicles has the ability of pairing/unpairing enabling a pair of units to join, transport bigger containers and disjoin again.

We study a scheduling problem associated with this new family of vehicles. This scheduling problem aims at transporting a set of different size containers among different areas in the port platform using a fleet of IAVs. In this problem, each vehicle represents a configurable processor, and moving a container from a location to other represents a job, whose processing might require the synchronized configuration of some specific processors (vehicles). This optimization problem looks for a sequence of settings for each processor, to minimize the makespan.

Previous studies have approached this problem using algorithms based on a Mixed Integer Programming model that linearizes the synchronization constraints by the big-M method. We propose an alternative model which manages the synchronization constraints by combinatorial cuts that remove minimum infeasible subsets detected by exploring its associated dual problem. Its associated branch-and-cut algorithm has shown to be competitive behavior in extensive computational experience.