An optimization model to assign seats in long distance trains to minimize SARS-CoV-2 diffusion.

The unprecedented spread of SARS-CoV-2 has pushed governmental bodies to undertake stringent actions like travel regulations, localized curfews, curb activity participation, etc. These restrictions assisted in controlling the proliferation of the virus; however, they severely affected major economies. This compels policymakers and planners to devise strategies that restrain virus spread as well as operationalize economic activities. In this context, we discuss some of the potential implications of seat inventory management in long-distance passenger trains and create a balance between operators' operational efficiency and passengers' safety. The paper introduces a novel seat assignment policy that aims to mitigate virus diffusion risk among passengers by reducing interaction among them. A mixed-integer linear programming problem has been formulated that concomitantly maximizes the operator's revenue and minimizes virus diffusion. The validity of the model has been tested using real-life data obtained from Indian Railways. The computational results show that a mere 50% capacity utilization may distress operators' economics and prove ineffectual in controlling SARS-CoV-2 transmission. The proposed model produces encouraging results in restricting virus diffusion and improving revenue even under 100% capacity utilization.