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3466. A stochastic mixed-integer programming approach to optimize a renewable fuel supply chain under uncertainty of renewable electricity supply
Invited abstract in session MA-24: Sustainable Supply Chain Design, stream Sustainable Supply Chains.
Monday, 8:30-10:00Room: 83 (building: 116)
Authors (first author is the speaker)
| 1. | Mina Farajiamiri
|
| Chair of Operations Management, RWTH Aachen University | |
| 2. | Jörn Meyer
|
| Chair of Operations Mangement, RWTH Aachen University | |
| 3. | Grit Walther
|
| School of Business and Economics, Chair of Operations Management, RWTH Aachen University |
Abstract
Addressing climate change requires the transportation sector to adopt renewable hydrocarbon fuels, particularly in aviation, maritime, and heavy-duty road transport, where electrification faces challenges. This transition necessitates the design of new production networks and supply chains, yet it faces uncertainties related to resources, technologies, and demand. It is vital to ensure that these renewable fuel supply chains are flexible and robust to uncertainties, such as supply and demand fluctuations or technological changes. We explore the impact of uncertainty on the design of renewable fuel supply chains, focusing on the variable capacity factors of renewable energy sources and their influence on electricity supply. We propose a multi-stage stochastic programming approach to optimize the renewable fuel supply chain design, taking into account seasonal and geographical variations in resource availability. We aim to minimize the expected total system cost while accommodating the uncertainty in the capacity factor of renewable electricity which will affect its supply. Scenario generation is based on 30 years of hourly data, using Latin Hypercube Sampling and Iman-Conover transformation to incorporate correlation among uncertain parameters. A fast-forward heuristic algorithm is developed to solve the model efficiently. Applied to a case study on the EU, results show promise for this approach in designing multi-period supply chain networks that work well under each scenario
Keywords
- Stochastic Optimization
- Supply Chain Management
- OR in Energy
Status: accepted
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