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Assessment of the Influence of Grid Resolution on CO2 Reduction in Route Optimization Services Using Reinforcement Learning

In: Smart Services Summit

Author

Listed:
  • Mohammad Hossein Moradi

    (Karlsruhe Institute of Technology)

  • Martin Brutsche

    (Winterthur Gas & Diesel Ltd.)

  • Markus Wenig

    (Winterthur Gas & Diesel Ltd.)

  • Uwe Wagner

    (Karlsruhe Institute of Technology)

  • Thomas Koch

    (Karlsruhe Institute of Technology)

Abstract

Following the Paris Climate Agreement, the maritime industry has committed to reducing its GHG emissions by 50% by 2050 (compared to 2008). In this sense, the present work pursues this goal by focusing on an improved route optimization method using Reinforcement Learning (RL). A detailed comparison between RL and the conventional approach is carried out in this study. Besides RL, Dynamic Programming (DP) is also used to establish the benchmark. The influence of different grid resolutions and dynamic weather on effective CO2 reduction is analyzed. It is observed that these two aspects can play a significant role in the route optimization results. Furthermore, the results show that RL as a model-free approach offers a great advantage for these considerations.

Suggested Citation

  • Mohammad Hossein Moradi & Martin Brutsche & Markus Wenig & Uwe Wagner & Thomas Koch, 2023. "Assessment of the Influence of Grid Resolution on CO2 Reduction in Route Optimization Services Using Reinforcement Learning," Progress in IS, in: Jürg Meierhofer & Shaun West & Thierry Buecheler (ed.), Smart Services Summit, pages 65-73, Springer.
    • Handle: RePEc:spr:prochp:978-3-031-36698-7_7
    DOI: 10.1007/978-3-031-36698-7_7
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