  This paper addresses the problem of planning time-optimal trajectories for
multiple cooperative agents along specified paths through a static road
network. Vehicle interactions at intersections create non-trivial decisions,
with complex flow-on effects for subsequent interactions. A globally optimal,
minimum time trajectory is found for all vehicles using Mixed Integer Linear
Programming (MILP). Computational performance is improved by minimising binary
variables using iteratively applied targeted collision constraints, and
efficient goal constraints. Simulation results in an open-pit mining scenario
compare the proposed method against a fast heuristic method and a reactive
approach based on site practices. The heuristic is found to scale better with
problem size while the MILP is able to avoid local minima.
