# Robust Design Optimization for Egressing Pedestrians in Unknown Environments

## TL;DR

This paper develops a robust optimization approach for pedestrian egress in unknown environments, using agent-based modeling and particle swarm optimization to minimize exit time despite uncertainties in group size.

## Contribution

It introduces a novel combination of microscopic pedestrian modeling with robust optimization to improve egress efficiency in uncertain environments.

## Key findings

- Optimally placed obstacles reduce evacuation time.
- The model reveals key parameters influencing pedestrian flow.
- Robust optimization maintains efficiency across varying group sizes.

## Abstract

In this paper, we deal with a size-variable group of pedestrians moving in a unknown confined environment and searching for an exit. Pedestrian dynamics are simulated by means of a recently introduced microscopic (agent-based) model, characterized by an exploration phase and an egress phase. First, we study the model to reveal the role of its main parameters and its qualitative properties. Second, we tackle a robust optimization problem by means of the Particle Swarm Optimization method, aiming at reducing the time-to-target by adding in the walking area multiple obstacles optimally placed and shaped. Robustness is sought against the number of people in the group, which is an uncertain quantity described by a random variable with given probability density distribution.