In this paper, we are interested in the synthesis of schedulers in double-weighted Markov decision processes, which satisfy both a percentile constraint over a weighted reachability condition, and a quantitative constraint on the expected value of a random variable defined using a weighted reachability condition. This problem is inspired by the modelization of an electric-vehicle charging problem. We study the cartography of the problem, when one parameter varies, and show how a partial cartography can be obtained via two sequences of opimization problems. We discuss completeness and feasability of the method.
@article{arxiv.1809.03107,
title = {Multi-weighted Markov Decision Processes with Reachability Objectives},
author = {Patricia Bouyer and Mauricio González and Nicolas Markey and Mickael Randour},
journal= {arXiv preprint arXiv:1809.03107},
year = {2018}
}