A regulated many-to-one two-sided matching model for emergency teams: a university case study

In workplaces, emergency teams comprised of employees play a critical role in minimising damage during emergencies through rapid response. This study introduces a multi-objective employee-team matching model that optimally assigns employees to emergency teams, balancing stakeholder preferences and physical requirements. Using a regulated many-to-one matching framework, this model accounts for employee preferences, decision-maker evaluations, and team-specific physical standards, addressing limitations in current manual matching practices. Tested through a real case study in a university setting, the model demonstrates its efficacy by achieving stable, satisfactory matches that outperform traditional methods. Sensitivity analysis across multiple scenarios further illustrates the model’s adaptability, providing decision-makers with a systematic tool for improving team stability, performance, and flexibility in various emergency preparedness contexts. The main contribution of this study is the reconciliation of preference-based assignments with performance requirements through a tuneable objective function. By jointly considering soft (preferences) and hard (physical) constraints in a single framework, the model allows decision-makers to balance satisfaction and operational feasibility–an integration that existing approaches typically overlook. This approach presents a robust, data-driven solution to enhance organisational resilience and efficiency in critical situations.