  The use of autonomous robots for delivery of goods to customers is an
exciting new way to provide a reliable and sustainable service. However, in the
real world, autonomous robots still require human supervision for safety
reasons. We tackle the realworld problem of optimizing autonomous robot timings
to maximize deliveries, while ensuring that there are never too many robots
running simultaneously so that they can be monitored safely. We assess the use
of a recent hybrid machine-learningoptimization approach COIL (constrained
optimization in learned latent space) and compare it with a baseline genetic
algorithm for the purposes of exploring variations of this problem. We also
investigate new methods for improving the speed and efficiency of COIL. We show
that only COIL can find valid solutions where appropriate numbers of robots run
simultaneously for all problem variations tested. We also show that when COIL
has learned its latent representation, it can optimize 10% faster than the GA,
making it a good choice for daily re-optimization of robots where delivery
requests for each day are allocated to robots while maintaining safe numbers of
robots running at once.
