ASSESSING WORKFORCE ALLOCATION THROUGH DISCRETE-EVENT SIMULATION WITH MAXIMUM-DISPERSION SAMPLING: A CASE STUDY IN A DEFENSE INDUSTRIAL BASE MANUFACTURING FACILITY

This paper presents a case study in an adapted defense industrial manufacturing setting inspired by a strategic facility of the Brazilian Armed Forces, using Discrete-Event Simulation to support workforce allocation in an ammunition production line. To reduce the search space, a Maximum-Dispersion sampling strategy based on the Farthest-First algorithm selected a diverse subset from millions of feasible configurations. Results identified three operational regimes (shortage, transition, and plateau) and revealed one Production Center as the dominant bottleneck, with diminishing marginal returns from additional staffing. Several workforce configurations within the plateau produced nearly identical completion times, indicating opportunities for targeted workforce adjustment and reallocation without compromising the reference production target. The findings suggest that, in serial multi-center production lines, productivity gains depend more on interventions at the bottleneck stage than on uniform workforce expansion, providing quantitative support for capacity planning in defense industrial environments.