Airport workforce optimization

October-November 2019

About the company

This Challenge was proposed by one of the major Latin American airline companies. It operates all around the world and covers more than 100 countries. This challenge dealt with workforce scheduling in El Dorado airport (Bogotá), one of the company's most important hubs.

Image by Bao Menglong

The Challenge

This workforce management problem combines two optimization problems.

On one hand, there is a scheduling problem, where many decisions have to be determined: how to design the shifts (start, finish, break time) to fulfill the demand and all the constraints related to corporate policies, legislation and staff preferences? How to assign the agents according to their skills?

On a second hand, there is a transportation problem. The airport operation being 24/7, the company hires taxicabs for the transportation of it’s staff during night time. To minimize costs, the employees who have the same schedule would share the same cabs. However, this cab sharing has to be done effectively in order not to increase individual travel times. The company used to solve these two problems independently. However, as the Challenge showed, this is far from optimal and huge gains can be made by solving both problems at the same time.

Julio Mejía at el Dorado Airport presenting to the company

50%

decrease in
non-satisfied demand

50%

decrease in number of required vehicles

30%

decrease in transportation travel distances

Winners of the Challenge

Julio Mejía Vera

Daniela Betancourt

Daniela and Julio's software cuts the unsatisfied demand by 50%. Transportation wise their solution reduces by 50% the number of required vehicles and by 30% the overall traveled distances. Their solution manages also to make starting times 40% less variable which is very important for the well-being of the agents. All these results are obtained while maintaining running time below 15 minutes.

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David Leonardo Beltrán

Angie Natalia Pineda

David and Angie's software reduces by 30% unsatisfied demand and improves transportation effectiveness by 77%. Part of their heuristic is inspired by a data science algorithm known as K-mean clustering. They also adapted their algorithms to optimize the alignment between demand and agent skills.

María Angélica Piñeros

Daniela Ramírez Alfonso

In just a few seconds their software finds a solution that reduces non-satisfied demand by 30% and requires 60% less cab trips. Their algorithms are designed to narrow the focus on a limited set of fixed schedules so the results are more practical to implement.

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Angie Vanessa Barahona

Angie's software focuses on improving the well-being of the agents. It creates a solution that makes space for 11 more rest days. It also reduces the number of required cab trips by 36% with a running time of about 5 minutes. Angie also analyzed the company's solution and proposed organizational changes to reduce unsatisfied demand by 66% while generating 56 more rest days.

A word from the professor


This was a fascinating problem combining transportation and scheduling optimization. Solving these two problems separately inevitably leads to less than optimal solutions. Indeed, travel times will necessarily be longer if people who live far from each other are assigned to the same schedules because they would be more likely to share the same cab. In addition to this difficulty, our students had to balance their algorithms to optimize many contradictory objectives at the same time : maximize service level, minimize transportation costs, minimize individual travel times from home to work and work to home, minimize the change in schedules for each agent and give as many additional rest days as possible.

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Prof. Rabie Nait-Abdallah

Leader of the Challenge

Javeriana University