| dc.description.abstract | The FIFA World Cup presents a complex logistical challenge, featuring an intricate
tournament schedule that requires teams to frequently move between their base camps and
match venues. In this thesis, we explore the potential of mathematical programming as a tool
to devise an optimal tournament schedule that reduces extensive travel and ensures fair travel
distribution.
We create a FIFA World Cup scheduling framework consisting of mixed integer linear
programming models. The framework consists of a series of individual optimization models,
crafted from the guidelines of the World Cup of 2014 and 2018. All models yield significantly
improved objectives relative to the historical benchmarks of 2014 and 2018. For the models
minimizing total distance traveled throughout the group stage, the results range from a
decrease of 25% to 48% in distance covered compared to historical distances. For the models
minimizing the distance between the least and most traveling teams among all teams, the
results range from a decrease of 81% to 96% for this inner range compared to historical
differences. For the models minimizing the distance between the least and most traveling
teams within each group, the results range from a decrease of 83% to 98% for the sum of the
groupwise inner ranges compared to the sum of the historical groupwise inner ranges.
We further combine the individual objectives into a multi-objective model using the 𝜖�-
constraint method, thereby showcasing a Pareto front of candidate solutions that all yield
results that surpass the historical benchmarks for both objectives simultaneously.
Our findings strongly indicate that utilizing mathematical programming for the World Cup
match scheduling process offers the potential to reduce the overall distances traveled while
concurrently ensuring a more balanced distribution of travel burdens among the participants.
We highlight the 2026 World Cup as an ideal prospect for implementing this approach. | en_US |
