In this paper we present a model predictive control (MPC) approach to optimize vehicle scheduling and routing in an autonomous mobility-on-demand (AMoD) system. In AMoD systems, robotic, self-driving vehicles transport customers within an urban environment and are coordinated to optimize service throughout the entire network. Specifically, we first propose a novel discrete-time model of an AMoD system and we show that this formulation allows the easy integration of a number of real-world constraints, e.g., electric vehicle charging constraints. Second, leveraging our model, we design a model predictive control algorithm for the optimal coordination of an AMoD system and prove its stability in the sense of Lyapunov. At each optimization step, the vehicle scheduling and routing problem is solved as a mixed integer linear program (MILP) where the decision variables are binary variables representing whether a vehicle will 1) wait at a station, 2) service a customer, or 3) rebalance to another station. Finally, by using real-world data, we show that the MPC algorithm can be run in real-time for moderately-sized systems and outperforms previous control strategies for AMoD systems.

Supply chain modelling and simulation by SMEs (Small-to-Medium Enterprises) is a challenging problem. This is due both to complexity of the supply chain models required and the lack of required expertise among the SMEs. The problem is important since SMEs need to represent and modify their evolving skills and processes to be visible in electronic marketplaces and supply chain design platforms. We demonstrate how this problem can be addressed by developing a suite of novel domain-specific visual languages and a support tool. The challenging setup of our research context motivated us to trial a new approach for the design of our visual languages and to employ a collaborative development process across our distributed research team.