Santa Claus TSP Challenge

Call for papers: Special issue and Competition



Background

Travelling salesman problem (TSP) is classical optimization problem that has been evolved to real-life vechile routing problems (VRP). Recent development in unmanned aerial vehicles (UAV) is bringing these problems into new dimensions.

Goal

Christmas is coming and Santa Claus needs to deliver presents to the children in every family during a single day in Christmas. He can prepare his sledge and move to any start point he wants to. But when the time has come, he has to proceed fast and optimize the length of his tour. He might also use helpers and divide the tour into several sub tours. Your task is to optimize the trip.


What is it?

We organize the special issue as a challenge. We invite researchers and practitioners to help Santa and give solutions to:
  • Open-loop TSP
  • Multiple Santa tours
  • Fast processing (optional)
In real life, the task is impossible for single Santa. In the second variant, Santa therefore recruits k assistants (fake Santa's) who divive the households into smaller clusters that are solved by each fake Santa separately.

Competition Rules:

To be defined.

Data:

To keep the task reasonable, we limit the tour in Finland. We have constructed the targets to visit from OpenStreetMap buildings data. There are N=1,437,195 targets in total. Dataset is available here.

Submission:

We organize the special issue as a challenge. We invite researchers and practitioners to:
  • Submit your method in the competition
  • Submit a paper to the special issue
Submission to the competition must include the following:
  1. Source code
  2. Method description
  3. Citation (in case of using existing method) or Abstract (in case of novel method)
All valid submissions will be evaluated. Based on the results, the authors of novel methods are invited to submit their full paper to the special issue. The method description serves can serve as an Abstract of your method.

It is possible to submit your method only to the competition but we strongly recommend to submit also a paper to the special issue if the method has novelty and its performance is competitive.

Paper submissions outside the competition are allowed and can take broader view to the problem - with clear arguments how the alternative approach is relevant. All submitted papers will go through a normal review process.

Important dates:


Competition opens: 1 July 2019
Deadline for algorithm submissions: 24 December 2019
Final results: 1 March 2020
Deadline for manuscript submissions: 1 May 2020
*All deadlines are at midnight, Eastern European Time

Evaluation:

All submissions will be evaluated in terms of quality, speed and simplicity. We will evaluate all methods by running them on the same Dell R920 machine with 4 x E7-4860 (total 48 cores), 1 TB, 4 TB SAS HD.

Results:

Results will be published on the competition website by 1 May 2020, latest. Intermediate results on the training data will be available already during the competition immediately after every upload. We will provide the following outcomes from the competition:
  • Two ranking lists: quality and speed (quality <10% worse than that of the winner)
  • Results will be fully documented and published later as a paper
  • All datasets will also be published
  • The winner will be invited to visit the Machine Learning group at UEF. We cover reasonable travel and accommodation expenses, and provide VIP treatment during the visit . The winner also qualifies to be an IMPIT student in case he/she persues a MSc degree in Finland. Language proficiency will still need to be proved.

For more information contact the organizers:

Publication fees:

The journal is fully-Gold, open-access journal. Authors are therefore asked to pay a publication fee upon acceptance of their manuscript. Frontiers holds agreements with over 120 institutions globally who are committed to fully or partial support for the publication fee. If the author's institution is not able to gover the fee, authors are encouraged to apply for a partial or full fee waiver.