  Hydrogen can be produced from water, using electricity. The hydrogen can
subsequently be kept in inventory in large quantities, unlike the electricity
itself. This enables solar and wind energy generation to occur asynchronously
from its usage. For this reason, hydrogen is expected to be a key ingredient
for reaching a climate-neutral economy. However, the logistics for hydrogen are
complex. Inventory policies must be determined for multiple locations in the
network, and transportation of hydrogen from the production location to
customers must be scheduled. At the same time, production patterns of hydrogen
are intermittent, which affects the possibilities to realize the planned
transportation and inventory levels. To provide policies for efficient
transportation and storage of hydrogen, this paper proposes a parameterized
cost function approximation approach to the stochastic cyclic inventory routing
problem. Firstly, our approach includes a parameterized mixed integer
programming (MIP) model which yields fixed and repetitive schedules for vehicle
transportation of hydrogen. Secondly, buying and selling decisions in case of
underproduction or overproduction are optimized further via a Markov decision
process (MDP) model, taking into account the uncertainties in production and
demand quantities. To jointly optimize the parameterized MIP and the MDP model,
our approach includes an algorithm that searches the parameter space by
iteratively solving the MIP and MDP models. We conduct computational
experiments to validate our model in various problem settings and show that it
provides near-optimal solutions. Moreover, we test our approach on an
expert-reviewed case study at two hydrogen production locations in the
Netherlands. We offer insights for the stakeholders in the region and analyze
the impact of various problem elements in these case studies.
