  This paper addresses the challenge of planning a sequence of tasks to be
performed by multiple robots while minimizing the overall completion time
subject to timing and precedence constraints. Our approach uses the Timed
Partial Orders (TPO) model to specify these constraints. We translate this
problem into a Traveling Salesman Problem (TSP) variant with timing and
precedent constraints, and we solve it as a Mixed Integer Linear Programming
(MILP) problem. Our contributions include a general planning framework for TPO
specifications, a MILP formulation accommodating time windows and precedent
constraints, its extension to multi-robot scenarios, and a method to quantify
plan robustness. We demonstrate our framework on several case studies,
including an aircraft turnaround task involving three Jackal robots,
highlighting the approach's potential applicability to important real-world
problems. Our benchmark results show that our MILP method outperforms
state-of-the-art open-source TSP solvers OR-Tools.
