About this Research Topic
The Travelling Salesman Problem (TSP) is a classical optimization problem that has been evolved to real-life vehicle routing problems (VRP). Recent development in unmanned aerial vehicles (UAV) is bringing these problems into new dimensions.
Christmas is coming and Santa Claus needs to deliver presents to the children in every family on Christmas Eve. He prepares his sledge but when the time has come, he has to proceed fast and optimize the length of his tour. He can also use helpers and divide the tour into several parts. Your task is to optimize the trip! Please see the competition website here.
What is it?:
We organize this Research Topic as a challenge. We welcome researchers and practitioners to help Santa and give solutions to:
1. Closed-Loop TSP
2. Open-Loop TSP
3. Fixed-Start TSP (open loop)
4. Multiple Tours k-TSP (open loop)
In the first variant, Santa needs to consider returning home as well. In all others this is not necessary (open loop). In the second variant Santa can leave from any location he wants. He travels to that place and waits for the start time. In the third variant, he leaves from his home, in Rovaniemi (first location in the dataset). In the fourth variant, the task is impossible for one Santa, so he recruits k assistants (drone Santas) who divide the tour into multiple parts that are solved by each drone separately.
To keep the task reasonable, we limit the tour to Finland. We have constructed the dataset from OpenStreetMap buildings data. There are N=1,437,195 targets in total. The dataset is available here.
We organize this Research Topic as a challenge. We welcome researchers and practitioners to:
• Submit your method in the competition
• Submit a paper to the Research Topic
Submission to the competition must include the following:
• Source code
• Method description
• 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 welcome to submit their full paper to the Research Topic. The method description 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 Research Topic if the method has novelty and its performance is competitive.
Paper submissions outside the competition are allowed and can take a broader view to the problem - with clear arguments on how the alternative approach is relevant. All submitted papers will go through Frontiers peer review process.
- Competition opens: 1 January 2020
- Deadline for algorithm submissions: 1 October 2020*
- Final results: 24 December 2020*
- Deadline for manuscript submissions: 1 February 2021*
*All deadlines are at midnight, Eastern European Time
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 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 <1% 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 pursues a MSc degree in Finland. Language proficiency will still need to be proved.
Keywords: Travelling Salesman Problem, Vehicle Routing Problem, Unmanned Aerial Vehicles, k-TSP
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.