Robotics technology has recently matured sufficiently to deploy autonomous robotic systems for daily use in several applications: from disaster response to environmental monitoring and logistics. In such applications, robots must establish collaborative interactions so to achieve their individual and collective goals and a key problem is for robots to make individual decisions so to optimize a system wide objective function. This problem is typically referred to as coordination. In this paper, we first describe modern optimization techniques for coordination in multiRobot systems. Specifically, we focus on approaches that are based on algorithms widely used to solve graphical models and constraint optimization problems, such as the max-sum algorithm. We then analyze the coordination problem faced by a set of robots operating in a warehouse logistic application. In this context robots must transport items from loading to unloading bays so to complete packages to be delivered to customers. Robots must cooperate to maximize the number of packages completed in the unit of time. To this end a crucial component is to avoid interferences when moving in the environment. We show how such problem can be formalized as a Distributed Constrained Optimization problem and we provide a solution based on the binary max-sum algorithm. Finally, we provide a quantitative evaluation of our approach in a simulated scenario using standard robotics tools (ROS and Gazebo).