During epidemics, decision-making regarding intervention measures faces complex trade-offs. Interventions targeting indoor venues can mitigate disease spread, since they are associated with higher infection risk for respiratory pathogens. However, as experienced during the COVID-19 pandemic, these measures can lead to economic losses, especially in the hospitality sector. In this study, we propose a hybrid modeling and simulation framework to provide decision support for reducing the infection risk in indoor venues while maintaining viable economic activity. Our framework integrates (i) a microscopic pedestrian model for human movement, (ii) a hybrid simulation model for virus spread and transmission, and (iii) a multi-criteria decision-making approach to identify the best service options. The framework is demonstrated for the SARS-CoV-2 infection risk. The restaurant case study results illustrate that maximizing the distance between seating groups can have a limited effect on the infection risk. Service duration and service capacity are key determinants of expected economic activity, but they constitute significant trade-offs: the former has a substantial impact on the infection risk, and the latter drives the probability of infectious introductions. Our analysis demonstrates the need for multi-criteria approaches during an outbreak and consideration of the epidemiological context for operational decision-making, even at an individual venue.