Apologies for cross-posting.
This Special Issue invites authors to submit articles focusing on
optimization methods that rely on learning techniques to address problems
in logistics and transportation. Theoretical papers are acceptable,
provided that they have case studies/numerical examples in the
logistics/transportation field; models and algorithms that utilize learning
to better understand the problem structure, physics, and behavior fall in
the scope of the special session. We are particularly interested in
contributions that are comprehensive enough to also cover or address
problems in logistics and supply chains, that consider sustainability, IoT,
electric vehicles, energy efficiency, and other relevant areas. We welcome
both original research and review articles. Possible contributions may
include, but are not limited to, the following topics:
§ Enhancing classical methods via ML
§ Markov Decision Process
§ Neural methods
§ Learning for primal-dual techniques
§ Reinforcement learning based methods,
§ Novel classes of methods.
*Manuscript submission information:*
Submission process and papers must adhere to the standard author guidelines
of *Transportation Research Part E: Logistics and Transportation Review*,
which can be found at:
https://www.elsevier.com/journals/transportation-research-part-e-logistics-and-transportation-review/1366-5545/guide-for-authors
Submitted articles must not have been previously published or currently
submitted for journal publication elsewhere. Please follow the submission
guidelines, which can be found from the journal website:
https://www.editorialmanager.com/tre/default1.aspx
All submissions to the Special section should be submitted via the
*Transportation
Research Part E* online submission system. When you submit your paper to
the Special section, please choose article type "MLOPT23" Otherwise, your
submission will be handled as a regular manuscript. Papers submitted to the
Special section will be subjected to normal thorough double-blind review
process.
*Keywords:*
Logistics, transportation systems, machine learning, optimization,
combinatorial optimization, solution methods
Learn more about the benefits of publishing in a special issue:
https://www.elsevier.com/authors/submit-your-paper/special-issues
*Guest editors:*
- Shahin Gelareh - Universite d'Artois, Bethune, France (
shahin.gelareh@univ-artois.fr )
- Nelson Maculan - Federal University of Rio de Janiero (
maculan@cos.ufrj.br)
- Rahimeh Neamatian Monemi - Predictim Globe (contact@predictim-globe.com
)
- Pedro Henrique González - Federal University of Rio de Janeiro (
pegonzalez@cos.ufrj.br)
- Xiaopeng Li - University of Wisconsin-Madison, Madison, WI, United
States ( xli2485@wisc.edu )
- Fatmah Almazkoor - University of Kuwait ( fatmah.almazkoor@ku.edu.kw)
- Ran Yan - School of Civil and Environmental Engineering, Nanyang
Technological University ( ran.yan@ntu.edu.sg )
*Deadline: Feb 1, 2024*
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