Conference Agenda

‘Green hydrogen’ is a promising alternative to fossil fuels that can potentially decarbonize the future transport and energy sectors. But a sustainable energy economy based on green hydrogen is limited by the availability of resources (feedstock) and production capacities in many countries. The discrepancy between high demand and low self-supply leads to the necessity of a compensation through imports from other regions of the world. However, an overreliance on a single exporting nation should be avoided since this can lead to geopolitical, financial, and moral complications. The sustainable import of green hydrogen should be diversified instead. Rather than only relying on parallel bilateral imports, a multinational transportation network offers the potential for cost reduction through consolidation in production, conversion, and transportation. We propose an optimization model for such a multinational import network for green hydrogen, that considers import diversification as well as factors like transportation losses and conversion efficiencies. Using Germany as a case study, we demonstrate how enforced diversification impacts sourcing decisions and overall costs to further emphasize the trade-off between resilience and cost-effectiveness. Furthermore, we highlight how the optimal transport network changes and hydrogen prices can be further reduced with smaller losses of liquified hydrogen during transportation.

Renewable fuels aim at reducing greenhouse gas emissions in transportation applications that cannot be electrified (such as long-haul trucks, aviation, and shipping). Besides the challenges posed by the long-term planning horizon as required for net zero emissions scenarios and the seasonal availability of the required resources like biomass and electricity generated by solar and wind power, renewable fuel supply chain planning is further complicated by uncertainties related to supply and demand fluctuations or uncertain technological developments. Thus, these renewable fuel supply chains need to be flexible and robust so that they can cope with uncertainties.

This study seeks to develop a robust model for optimizing the renewable fuel supply chain network regarding uncertain future fuel demand. Using Bertsimas and Sim's approach, a robust reformulation is applied to minimize the total cost of the supply chain. A number of factors are taken into account in this model, including long-term horizons, multiple periods, seasonality, multiple stages, and multiple conversion technologies. To evaluate the performance of the model, several random demand scenarios are considered over a long-term horizon, along with strategic investment and operational decisions. An analysis of the EU transport sector's transition to climate neutrality from 2020 to 2060 illustrates the effectiveness of this approach.

In Supply Chain Network Design (SCND), the decision maker’s task is to decide upon locations for production or distribution facilities, product allocations to warehouses and factories, customer allocations and the choice of the transport modes. These decisions do not only have an impact on the economic performance of the supply chain but also on environmental indicators like the emission of CO2. To account for trade-offs, multi criteria SCND-modelling approaches are discussed increasingly.

In this paper, we will present a bi-objective SCND modelling approach which explicitly considers the selection of the mode of transport, as one possible leaver in the reduction of CO2. We model specific characteristics of transport modes, like the lead time and the shipment size and their influence on safety stock and cycle stocks. Through this, we are able to model trade-offs, which so far are not considered in the literature on multi criteria SCND-models. We describe a solution procedure, which includes the conversion into a mixed integer conic quadratic optimisation model, demonstrate the applicability within a case study and discuss computational and managerial insights. Further we give an outlook on the possible error supply chain decision makers can make, when ignoring those effects in the application of multi criteria SCND models.