Combinatorial Optimization Challenge: Delivery route optimization is organized by ZTE as a part of ITU Artificial Intelligence/Machine Learning in 5G Challenge 2021.
Find the Chinese version here.
In 5G era where everything is connected, rapid growth of network traffic poses great challenge to traditional network. In this context, intelligent route planning of network service is essential for efficient network management. In particular, how to achieve balance between low latency, high data bandwidth of network service and better utilization of network resources is the key issue for network planning and optimization.
This challenge is aimed to optimize the route planning in telecommunication network. Here the network is simplified as a high-speed transportation network due to its complexity, and then the problem is about how to optimize the delivery route planning in the transportation network.
For a given transportation network, where some goods are to be delivered between certain starting and ending points, competitors is required to figure out the route for delivery with the lowest transportation cost.
The features of the transportation network are as follows:
For example, each lane between adjacent nodes A and B can transport 10 tons of goods in total at most, regardless of the direction of the transportation. If 4 tons of goods is being delivered on a lane, then this lane can only carry another 6 tons of goods during the following deliveries.
For example, if the delivery uses lane 1 between nodes A and B, it can only use lane 1 between other nodes.
It should be noted that:
It should take less than 30 minutes for the solutions to figure out the results.
Participants must create a private Github repository to contain their work and submit by adding ITU-AIChallenge-ZTE as a collaborator. The repository will be made open to the judges after submission deadline and should be accessible till the end of the final event of the ITU challenge.
Participants are required to submit runnable algorithms which plans the route for delivery with the lowest transportation cost, along with corresponding description document.
These algorithms will be ranked by their total transportation cost on the datasets and duration of the calculation.
Datasets:
The dataset can be downloaded here: samples.zip.
Each sample in the dataset is composed of 2 element:
{
nodes:[0,1,2] ,# id list of the transportation nodes
edges:[
(0,1):{ # (starting point, end point)
lanes: [0,1,2], # id list of the lanes,the length of the array is the number of the lanes between the two adjacent nodes
lane_weights:[10, 10, 20] # The weight that each lane can carry
}
]
}
Participants are required to submit runnable algorithms which plan the route for delivery with the lowest transportation cost, along with corresponding description document. Basically, these algorithms will be ranked by their total transportation cost on the datasets and duration of the calculation.
Performance of algorithm (60%): Both total cost and running time will be taken into account.
Solution advantage (30%): Whether the solution is reasonable and whether the solution has enough practicability, innovation and universality.
Completeness (10%): Whether the requirements of the challenge are fulfilled according to the proposed scheme and design.
ITU contact: ai5gchallenge@itu.int
Combinatorial Optimization Challenge contact: yuan.liya@zte.com.cn
You can also visit our Slack Channel to find more guidance.