Dynamic resource allocation and coordination for high-load crisis volunteer management

Purpose: Every year volunteers play a crucial role in disaster responses around the world. Volunteer management is known to be more complex than managing a paid workforce, and this is only made worse by the uncertainty of rapidly changing conditions of crisis scenarios. The purpose of this paper is to address the critical problem of assigning tasks to volunteers and other renewable and non-renewable resources simultaneously, particularly under high-load conditions. These conditions are described by a significant mismatch between available volunteer resources and demands or by frequent changes in requirements. Design/methodology/approach: Through a combination of literature reviews and interviews with managers from several major volunteer organizations, six key characteristics of crisis volunteer resource allocation problems are identified. These characteristics are then used to develop a general mixed integer programming framework for modeling these problems. Rather than relying on probabilistic resource or demand characterizations, this framework addresses the constantly changing conditions inherent to this class of problems through a dynamic resource reallocation-based approach that minimizes the undesirable impacts of changes while meeting the desired and changing objectives. The viability of this approach for solving problems of realistic size and scale is demonstrated through a large set of computational experiments. Findings: Using a common commercial solver, optimal solutions to the allocation and reallocation problems were consistently obtained in short timespans for a wide variety of problems that have realistic sizes and characteristics. Originality/value: The proposed approach has not been previously addressed in the literature and represents a computationally tractable method to allocate volunteer, renewable and non-renewable resources to tasks in highly volatile crisis scenarios without requiring probabilistic resource or demand characterizations.