The Covid-19 pandemic introduces new challenges and constraints for return to work business planning. We describe a space allocation problem that incorporates social distancing constraints while optimising the number of available safe workspaces in a return to work scenario. We propose and demonstrate a graph based approach that solves the optimisation problem via modelling as a bipartite graph of disconnected components over a graph of constraints. We compare results obtained with a constrained random walk and a linear programming approach.
@article{arxiv.2105.05017,
title = {Optimal Seat Allocation Under Social Distancing Constraints},
author = {Michael Barry and Claudio Gambella and Fabio Lorenzi and John Sheehan and Joern Ploennigs},
journal= {arXiv preprint arXiv:2105.05017},
year = {2021}
}