Resilient Synchromodal Transport through Learning Assisted Hybrid Simulation Optimization Model
M. Muhammad Satrya Dewantara (TU Delft - Civil Engineering & Geosciences)
B. Atasoy – Mentor (TU Delft - Transport Engineering and Logistics)
Mahnam Saeednia – Mentor (TU Delft - Transport, Mobility and Logistics)
Lorant Tavaszzy – Graduation committee member (TU Delft - Transport, Mobility and Logistics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
The increasing volume of global freight trade, coupled with economic growth, necessitates ongoing innovation in optimizing freight operations. Over the past decade, the concept of synchromodality has been explored to encourage a modal shift from unimodal to multimodal transport. Synchromodality, with its flexibility feature, can create more resilient freight transport systems. Various models employing different techniques have been proposed to establish a resilient synchromodal framework capable of reacting to disruptions. However, there are only few studies addressing the unknown duration of disruptions. This research proposes a learning-based modular framework comprising to capture the dynamics of disruptions in multimodal transport and learn to make more effective decisions, thus addressing the challenge of limited prior knowledge about disruptions and enabling fast responses to disruptions.