  In case of a threat in a public space, the crowd in it should be moved to a
shelter or evacuated without delays. Risk management and evacuation planning in
public spaces should also take into account uncertainties in the traffic
patterns of crowd flow. One way to account for the uncertainties is to make use
of safety staff, or guides, that lead the crowd out of the building according
to an evacuation plan. Nevertheless, solving the minimum time evacuation plan
is a computationally demanding problem. In this paper, we model the evacuating
crowd and guides as a multi-agent system with the social force model. To
represent uncertainty, we construct probabilistic scenarios. The evacuation
plan should work well both on average and also for the worst-performing
scenarios. Thus, we formulate the problem as a bi-objective scenario
optimization problem, where the mean and conditional value-at-risk (CVaR) of
the evacuation time are objectives. A solution procedure combining numerical
simulation and genetic algorithm is presented. We apply it to the evacuation of
a fictional passenger terminal. In the mean-optimal solution, guides are
assigned to lead the crowd to the nearest exits, whereas in the CVaR-optimal
solution the focus is on solving the physical congestion occurring in the
worst-case scenario. With one guide positioned behind each agent group near
each exit, a plan that minimizes both objectives is obtained.
