Kindergarten admissions are a time-consuming process for Norwegian municipalities. The fact that it is so time-consuming is also one of the main challenges with the process itself. Of course, how long each municipality spends varies a lot in terms of size and population, but we are talking about everything from days, to weeks, and even months in some places, before the process is considered complete.
Optimized kindergarten admissions allocate space to all applicants in seconds–thereby freeing up time for both kindergarten directors and employees in the municipality.
The kindergarten admissions process is not as straightforward as placing one child in kindergarten A and one child in kindergarten B. To fully ensure that the result meets all municipal and private kindergartens’ regulations and that as many as possible get their wishes fulfilled, many considerations must be taken into account.
In addition, there is a jungle of regulations that determines what kind of priority different children have during admissions, which makes the process extra difficult.
So, how can optimization solutions, or artificial intelligence, help solve these challenges?
Also read: How can robotics lift HR processes?
Optimization involves searching for the best possible solution to a complex issue, within certain limitations. These issues are found everywhere in everyday life, whether planning patient visits in the home care service, rotating staff at a hospital, or distributing children to daycare centers in a municipality.
Common to such problems is that they have an end goal that cannot be achieved by simply replicating the techniques from previous projects.
Optimization solutions, or artificial intelligence, are well suited when there are several ways to solve a problem, and you are looking for the best possible solution that meets the requirements and goals. Whereas Norwegian municipalities have until now been allocating children to kindergartens manually, there is now an alternative method.
Optimized Admission is an add-on module to the kindergarten system Visma Flyt Barnehage. The module allocates spots in the kindergarten to all applicants in seconds, and therefore frees up time for both kindergarten directors and employees in the municipality. In addition, parents find out the result in a shorter time.
But the goal of admissions optimization is not just to perform the task as quickly as possible. It is about ensuring a fair distribution and fulfilling the wishes of as many applicants as possible. And based on advanced algorithms, the optimizer will always find the optimal allocation of children to kindergartens.
Read more about Visma Optimization Technologies and the projects they have worked on to better solve societal challenges within the health and social care sector.
Optimized Admission ensures that we can produce the optimal allocation of children to the kindergartens in a municipality. To do this, the solution’s algorithms take into account the children’s “ranking” in each kindergarten and ensure that those with a higher ranking are always given the highest priority.
This ranking is based on the following criteria:
- Regulatory requirements
- The kindergarten’s articles of association
- The kindergarten’s capacity
- The applicant’s wishes
- Number of full-time and part-time places
- Travel time
- Start date of each child
The optimization also supports the possibility of entering a specific capacity reserved for small or large children, who have different daycare preferences.
With this in mind, Optimized Admission allocates daycare places based on applicants’ priority of daycare centers and daycare days, and ensures that as many children as possible receive their desired daycare placement.
Verifying the result
Using advanced algorithms, Optimized Admission thus returns an overview of which children should be placed in which kindergarten, based on a number of criteria that ensure that all laws and regulations are followed.
A challenge with the previous manual method has been to test the result. What happens if parents complain to the municipality for example? Several municipalities have highlighted this challenge, and the difficulties in having to explain to parents why a given place has been allocated.
For some municipalities, this has required them to go over the work they had done, looking at all aspects of the allocation in detail, to verify their allocations were correct.
And in such a complicated process as kindergarten admissions, it is not necessarily the case that the optimal solution has been found, despite hard work, meaning that the parents may have a good case.
Optimized Admission ensures an objective assessment of all applications. Perhaps the biggest advantage is that it always finds the optimal distribution of children to kindergartens, where all municipal and private kindergartens’ regulations are maintained.
Also, one can easily generate an automatic justification for why a given allocation has been made, which makes it easy for the municipality to verify the result.
Want to learn more about AI and machine learning?