A priority-driven constructive heuristic for assigning and scheduling spontaneous volunteers in disaster response

Abstract

Large-scale disaster response operations frequently involve spontaneous volunteers who arrive independently at disaster sites and must be coordinated under severe time pressure. Assigning such volunteers to relief activities constitutes a complex workforce assignment and scheduling problem with heterogeneous capabilities, dynamic arrivals, and operational constraints. Recent work formulated the spontaneous volunteer coordination problem (SVCP) as a lexicographic multi-objective mixed-integer optimization model. However, solving this model to optimality becomes computationally challenging in large-scale and rolling-horizon disaster response settings. This paper proposes a problem-specific constructive heuristic for the SVCP that explicitly leverages the lexicographic objective hierarchy, capability scarcity among volunteers, and workload balancing across activities. A large-scale computational study based on empirically grounded disaster response scenarios derived from the 2013 flood response in Halle (Germany) evaluates the proposed approach. Across 3200 simulated instances with up to 10000 volunteers and more than 4000 activity-time combinations, the heuristic closely approximates optimal solutions for the primary objectives while achieving a median runtime speedup of approximately 28x. Whereas the exact solver exceeds operational decision time limits in more than 60% of instances, the heuristic consistently produces solutions within minutes, enabling real-time decision support for spontaneous volunteer coordination.