Airplane Seating Assignment Problem

John A. Pavlik (), Ian G. Ludden (), Sheldon H. Jacobson () and Edward C. Sewell ()
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John A. Pavlik: Department of Computer Science, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801
Ian G. Ludden: Department of Computer Science, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801
Sheldon H. Jacobson: Department of Computer Science, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801
Edward C. Sewell: Department of Mathematics and Statistics, Southern Illinois University Edwardsville, Edwardsville, Illinois 62025

Service Science, 2021, vol. 13, issue 1, 1-18

Abstract: SARS-CoV-2, the virus that causes COVID-19, began infecting humans in late 2019 and has since spread to over 57 million people and caused over 1.75 million deaths, as of December 27, 2020. In response to reduced demand and travel restrictions as a result of COVID-19, airlines experienced a 94% reduction in passenger capacity worldwide in April and an estimated 60% reduction in passengers transported for all of 2020. SARS-CoV-2 has been shown to spread on airplanes by infected passengers, so minimizing the risk of secondary infections aboard aircraft may save lives. We present the airplane seating assignment problem (ASAP) to minimize transmission risks on airplanes, and we provide two models to solve ASAP. We show that both models can be effectively solved using a standard commercial solver and that seating assignments provided by these models have lower aggregate risk than the strategy of blocking the middle seats, given the same number of passengers. The available risk models for aircraft are based on influenza data, and hence risk models based on SARS-CoV-2 should be developed to maximize the benefits of our research.

Keywords: public health; air travel; COVID-19; discrete optimization (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orserv:v:13:y:2021:i:1:p:1-18

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