[Logprofs] Special Issue in Transportation Science: Machine Learning Methods and Applications in Large-Scale Route Planning Problems

[Matthias Winkenbach]

Dear colleagues,

Please take note of the Special Issue in Transportation Science on “Machine Learning Methods and Applications in Large-Scale Route Planning Problems”. The submission deadline just got extended to January 15, 2022. Further information below.

Kind regards
Matthias Winkenbach

—
Matthias Winkenbach, PhD
Massachusetts Institute of Technology (MIT) | Center for Transportation & Logistics (CTL)
Director, MIT Megacity Logistics Lab | Director, MIT Computational and Visual Education (CAVE) Lab
http://winkenbach.mit.edu | https://www.linkedin.com/in/mwinkenbach | mwinkenb at mit.edu<mailto:mwinkenb at mit.edu>



Submission Deadline: Extended to January 15, 2022

Call for Papers: https://pubsonline.informs.org/page/trsc/calls-for-papers

About this Special Issue:

In connection with the “Last-Mile Routing Research Challenge” co-hosted by Amazon and the MIT Center for Transportation & Logistics in 2021 Amazon and the MIT Center for Transportation & Logistics in 2021, we are excited to announce a special issue focused on machine learning-based approaches to large-scale route planning problems. We invite authors independently of whether they took part in the research challenge to submit research papers that address large and complex planning problems related to vehicle routing with nonconventional, data-driven methods.

The Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems will feature novel methodological approaches to vehicle routing and related problems that up to now have predominantly been addressed using conventional optimization methods. With increasingly large and diverse sets of operational, transactional, and contextual data becoming available in the last-mile delivery domain, systematically exploiting such data is becoming more and more relevant for the planning of more efficient, more sustainable and safer last-mile delivery operations. To advance the academic discourse in this space, we welcome papers covering new work at the intersection of last-mile logistics, operations research, and data science. We are interested in papers that discuss novel methodological approaches to vehicle routing and related problems as well as their application and benefit to real-world logistics operations. The methodological contributions of the submissions to this special issue may consist in purely data-driven, machine learning-based approaches, as well as hybrid approaches that combine machine learning with conventional operations research methods. We encourage submissions that use such novel methodological approaches to incorporate non-conventional objectives or constraints in their route planning problem. Examples for such non-conventional aspects include, but are not limited to, tacit driver knowledge (e.g., of traffic conditions, parking, customer preferences), route consistency, driver safety, or social acceptance. Please note that this special issue is not limited to papers discussing work related to the specific problem tackled by the Amazon/MIT Last-Mile Routing Research Challenge.

The review of papers for this special issue will heavily weigh the innovation and methodological novelty of the work presented, as well as its real-world impact on society and the logistics industry.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.mit.edu/pipermail/logprofs/attachments/20211111/553d6f02/attachment.htm>