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Hydrogen can be produced from water, using electricity. The hydrogen cansubsequently be kept in inventory in large quantities, unlike the electricityitself. This enables solar and wind energy generation to occur asynchronouslyfrom its usage. For this reason, hydrogen is expected to be a key ingredientfor reaching a climate-neutral economy. However, the logistics for hydrogen arecomplex. Inventory policies must be determined for multiple locations in thenetwork, and transportation of hydrogen from the production location tocustomers must be scheduled. At the same time, production patterns of hydrogenare intermittent, which affects the possibilities to realize the plannedtransportation and inventory levels. To provide policies for efficienttransportation and storage of hydrogen, this paper proposes a parameterizedcost function approximation approach to the stochastic cyclic inventory routingproblem. Firstly, our approach includes a parameterized mixed integerprogramming (MIP) model which yields fixed and repetitive schedules for vehicletransportation of hydrogen. Secondly, buying and selling decisions in case ofunderproduction or overproduction are optimized further via a Markov decisionprocess (MDP) model, taking into account the uncertainties in production anddemand quantities. To jointly optimize the parameterized MIP and the MDP model,our approach includes an algorithm that searches the parameter space byiteratively solving the MIP and MDP models. We conduct computationalexperiments to validate our model in various problem settings and show that itprovides near-optimal solutions. Moreover, we test our approach on anexpert-reviewed case study at two hydrogen production locations in theNetherlands. We offer insights for the stakeholders in the region and analyzethe impact of various problem elements in these case studies.

Stochastic Cyclic Inventory Routing with Supply Uncertainty: A Case in Green-Hydrogen Logistics