Social distancing in airplane seat assignments

Area of Science:

• Operations Research
• Public Health
• Aviation Management

Background:

• The COVID-19 pandemic highlighted the need for effective social distancing measures in public transportation.
• Airlines implemented policies like middle-seat blocking to mitigate virus transmission.
• Existing policies may not fully optimize passenger safety or operational efficiency.

Purpose of the Study:

• To develop and evaluate a mixed integer programming model for airplane seat assignments that incorporates social distancing.
• To analyze two distinct types of social distancing: passenger-to-passenger proximity and proximity to the aisle.
• To compare the model's effectiveness against the current middle-seat blocking policy.

Main Methods:

• Formulation of a mixed integer programming model for seat assignment.
• Inclusion of objective functions to quantify and minimize infection risk from passenger proximity and aisle proximity.
• Simulation experiments with defined scenarios to test model performance.
• Reformulation of the model to maximize passenger capacity while maintaining social distancing.

Main Results:

• The proposed model generates seating assignments that effectively achieve social distancing goals across various scenarios.
• The model demonstrates comparable passenger-to-passenger distancing to middle-seat blocking.
• The model successfully reduces the number of passengers seated near the aisle, lowering associated health risks.

Conclusions:

• The developed mixed integer programming model offers an effective alternative to traditional social distancing policies in air travel.
• Optimized seat assignments can enhance passenger and crew safety by managing proximity risks.
• This approach provides a data-driven method for airlines to balance safety and operational capacity.