A Mathematical Decision Framework for Strategic Workforce Planning and Resource Allocation under Organizational Uncertainty

Source abstract

Effective workforce planning remains one of the primary challenges affecting operational efficiency in service-oriented and healthcare organizations. Existing workforce planning models frequently optimize staffing levels without simultaneously considering workforce capability, project priorities, budget limitations, uncertainty, and organizational resilience. This research develops a mathematical decision framework for strategic workforce planning that integrates optimization theory, stochastic decision modeling, and project management principles. The proposed framework formulates workforce allocation as a constrained multi-objective optimization problem where organizational productivity, workforce utilization, project completion probability, operational cost, and organizational resilience are optimized simultaneously. The framework incorporates uncertainty through probabilistic modeling and evaluates alternative workforce allocation strategies using mathematical optimization and scenario analysis. Publicly available labor statistics, occupational databases, and healthcare workforce datasets can be utilized for demonstration and validation purposes without requiring proprietary organizational information. The proposed framework provides decision-makers with a systematic methodology for improving workforce allocation, reducing operational risk, and supporting long-term organizational resilience across complex service environments.