Optimizing community-led post-disaster recovery in flood-affected areas: A resilience model for volunteer deployment

Effective post-disaster recovery relies heavily on volunteers, yet coordinating their deployment over extended recovery horizons remains operationally challenging and costly. This paper presents a mixed-integer linear programming (MILP) model for planning volunteer deployment during flood recovery, integrating mobilization, team formation, operational deployment, and outsourcing costs within a single optimization framework. The formulation schedules volunteer teams to disaster-affected regions across multiple days, prioritizes higher-urgency areas through severity-weighted service timing, and enforces practical operational constraints including daily team capacity limits, group activation logic, and precedence relations among regions, while retaining an explicit choice between internal deployment and external outsourcing under limited volunteer capacity. The model is instantiated for the Hawkesbury floodplain in Greater Western Sydney and evaluated under small-, medium-, and large-scale scenarios derived from representative flood extents. Computational experiments indicate that optimized volunteer coordination can reduce reliance on expensive outsourcing while maintaining timely coverage of priority regions, and sensitivity analyses highlight that increased volunteer availability and lower outsourcing costs yield the largest improvements in overall performance.