Speaker: Nicolás García Aramouni, Data Science Associate Manager, Accenture
Operations research and analytics have experienced significant growth in society, but their adoption in developing countries like Argentina has been slower. This is particularly evident in sectors like public schools, where outdated pen-and-paper or spreadsheet methods persist, hindered by low budgets. In Buenos Aires, three schools manually create schedules, a process that can take weeks or months due to multiple factors. However, these schools lack the necessary skills and resources for analytics-based solutions.
This study presents a pro-bono project aimed at helping these schools develop a user-friendly scheduling solution. The project involves constructing a Mixed Integer Programming (MIP) Model that considers teacher availability and subject hours, reducing the schools’ manual work. The model incorporates basic and tailored constraints, addressing the challenge of scheduling teachers across multiple schools. By implementing this solution, the schools achieved record time efficiency in producing schedules for the 2023 year. This project showcases the power of analytics-based solutions and underscores the role of businesses in supporting their communities through pro-bono initiatives.
Abstact
The role of operations research and analytics in our society has been growing in the last few years at an increasing pace. However, in developing countries like Argentina, the adoption of these methodologies has been slower and is currently being led by businesses and universities. In this scenario, some “old-fashioned” sectors still base most of their problem solving on pen-and-paper or spreadsheet software, which makes change management more difficult. This is the case of public schools, a sector which historically has had important infrastructure issues, both from a physical and systems standpoint, generated by low budgets. Under this scenario, teachers usually don’t have high salaries, making them have jobs in multiple schools and partitioning their daily availability in multiple portions that satisfy each institution’s necessities. This generates an interesting challenge when creating the school schedule as teachers do not have exclusivity for the school in question. When talking to three schools in Buenos Aires, Argentina, they told us that currently they do the complete scheduling process manually, which usually takes up multiple weeks or even a whole month at times, which makes sense as they must consider a great number of factors and variables all at once, in addition to completing other tasks, unrelated to this process.
These schools don’t have employees with the skills necessary to help them to create an analytics-based solution that solves this problem. In addition, people with this knowledge are usually skilled workers who usually can get higher salaries working for a technology company, making the situation more difficult for the budget-constrained schools. Therefore, businesses should have a more active role in society, helping their community and sharing their specific knowledge through pro-bono activities.
The idea of this pro-bono project is just that: helping three schools in Buenos Aires create a schedule in a user-friendly way. To achieve this, we created a solution that had two very important objectives:
1) Build a Mixed Integer Programming (MIP) Model that generated a valid schedule considering the availability of each teacher, how many hours a subject needs, etc.
2) Change and reduce the manual work done by schools In this sense, our aim was to change the work made by these schools to create the schedule: instead of having to create the whole schedule, they would only need to manually create the inputs needed by the model. We asked them to create two inputs: one would
indicate how many hours each subject needs to be taught at each course, and who is the teacher of that course. The other one should reflect the availability of each teacher. These inputs were fed into our MIP model, which incorporated both basic constraints (a teacher cannot teach two different subjects simultaneously) and tailored constraints (for example, certain subjects couldn’t be taught when students just got to school at 8 AM). Considering that multiple teachers were employees of the three schools, we couldn’t partition the problem, generating the challenge of building a model that could generate the schedule for all.
Here, there is no revenue metric that we are trying to optimize. However, what we did was improve productivity as this model let these schools produce their schedules for the 2023 year in record time, as they only needed to invest a couple of days to generate these inputs and let us know the conditions and constraints that the final schedule should follow. As an additional result, we showed the power of analytics-based solutions and were able to share this with our local community.
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