A bilevel optimization approach of energy transition in freight transport: SOS1 method and application to the Ecuadorian case

Daniel Villamar () and Didier Aussel ()
Additional contact information
Daniel Villamar: Université de Perpignan Via Domitia
Didier Aussel: Université de Perpignan Via Domitia

Computational Management Science, 2024, vol. 21, issue 2, No 2, 30 pages

Abstract: Abstract In this work, we propose a new model to evaluate the impact of potential governmental policy as a green transition to decarbonise road freight transport. Contrary to the classical literature on the topic, our approach employs a bilevel structure optimization scheme. More precisely, a single-leader-multi-follower (SLMF) model is developed, where the state represents the leader and the freight companies act as the followers. A carbon tax is the selected public policy to foster the shift to less polluting freight technologies. The model responds to an exogenously determined demand that must be satisfied by a fleet that remains constant in size through time. The aim is thus to determine to what extent the tax policy will generate an evolution of the fleets of the different companies towards greener vehicles. An additional scientific contribution of the paper is that the SLMF model is solved thanks to a new numerical approach based on the so-called SOS1 technique. The implementation is applied in the case of the Ecuadorian freight transport and the results highlight an interesting evolution of the fleets.

Keywords: Multi-agent decision-making; Single-leader-multi-follower game; Carbon tax; Freight transport; SOS1 method; 91B76; 91A10; 91A65; 49J52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10287-024-00520-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:comgts:v:21:y:2024:i:2:d:10.1007_s10287-024-00520-3

Ordering information: This journal article can be ordered from
http://www.springer. ... ch/journal/10287/PS2

DOI: 10.1007/s10287-024-00520-3

Access Statistics for this article

Computational Management Science is currently edited by Ruediger Schultz

More articles in Computational Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().