Optimal Seat Allocation Under Social Distancing Constraints

TL;DR

This paper addresses the challenge of optimizing seat allocation in workplaces under social distancing constraints during the Covid-19 pandemic, proposing a graph-based solution to maximize safe workspaces.

Contribution

It introduces a novel graph-based method for space allocation that incorporates social distancing constraints and compares it with other optimization techniques.

Findings

Graph-based approach effectively models social distancing constraints.

Linear programming and random walk methods provide comparable solutions.

Optimized seat allocation increases available safe workspaces.

Abstract

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.