Real-time transportation replanning in intermodal networks under disturbances
- Start date
Friday 4 Dec 2020, 13:00
- End date
Friday 4 Dec 2020, 14:00
- Spoken Language
Hinterland container transportation throughout Europe is woven into a complex intermodal transportation network which consists of railway, seaway and roadway connections.
Container transportation plans are often subject to disturbances upon arrival of new information in such intermodal networks and requires replanning in real-time. These disturbances can be due to arrival of new shipment orders, fluctuations in shipment quantities, delays and/or service cancellations within the network. Therefore, real-time decisions are to be made aftermath of such dynamic information changes. This study addresses real-time replanning of container transportation over intermodal networks under disturbances. The problem is formulated as a path-based multi-commodity network flow problem on a space-time network that is scalable to large transportation networks. The resulting problem is solved using a column generation algorithm where the pricing sub-problems arise to be a resource constrained shortest path problem which is solved via a label-correcting algorithm. We derive managerial insights by comparing dıfferent scenarios. To that end, we have performed a set of computational experiments on randomly generated test instances that mimic real-life. Our results are promising and provide quick solutions that can be used for practical implementations of real-time transportation replanning.
Authors: M. Hakan Akyüz, Rommert Dekker, Shadi Sharif Azadeh
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About Hakan Akyüz
M. Hakan Akyüz is a non-tenure track Assistant Professor at Erasmus University Rotterdam. He received a Ph.D. degree in Industrial Engineering from Boğaziçi University, İstanbul, in 2011. He worked as a post-doctoral fellow at Hong Kong University of Science and Technology between 2012-2013. He joined the Department of Industrial Engineering at Galatasaray University in 2014 as an Assistant Professor and worked until 2020. His research interest spans location theory, mathematical programming applications in logistics, algorithms, and interpretable learning.