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

Umur Hasturk (), Albert H. Schrotenboer (), Evrim Ursavas () and Kees Jan Roodbergen ()
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Umur Hasturk: Department of Operations, Faculty of Economics and Business, University of Groningen, 9747 AE Groningen, Netherlands
Albert H. Schrotenboer: Operations, Planning, Accounting and Control Group, School of Industrial Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, Netherlands
Evrim Ursavas: Department of Operations, Faculty of Economics and Business, University of Groningen, 9747 AE Groningen, Netherlands
Kees Jan Roodbergen: Department of Operations, Faculty of Economics and Business, University of Groningen, 9747 AE Groningen, Netherlands

Transportation Science, 2024, vol. 58, issue 2, 315-339

Abstract: 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.

Keywords: green hydrogen; stochastic cyclic inventory routing; static and dynamic decision making (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:58:y:2024:i:2:p:315-339

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