Abstract
Small and medium-sized enterprises (SMEs) constitute a substantial share of industrial production. However, their operational performance is frequently constrained by delivery delays caused by inefficiencies in workforce scheduling and task sequencing. These limitations reduce overall competitiveness, particularly in project-based manufacturing environments where task heterogeneity and multi-skill variability are prominent. To address this challenge, this study develops an artificial intelligence based workforce planning framework tailored to capital-constrained manufacturing settings. The new proposed hybrid system integrates a Genetic Algorithm (GA), Monte Carlo Simulation (MCS), and Taguchi methodology to generate robust, uncertainty-aware labor assignments. The framework is validated through 18-month deployments in two manufacturing facilities with differing levels of technological maturity, demonstrating consistent improvements in operational outcomes. Furthermore, specific weekly examples were validated against the solutions of exact mixed integer linear programming solvers on the deterministic core to assess the optimality gap and ensure constant solution quality. Across the deployments, the system achieved 13% and 15% reduction in task completion times. The resulting GA–MCS–Taguchi pipeline operates efficiently on standard SMEs hardware, requires only short historical performance windows for calibration, and exhibits high user adoption in real industrial settings, which indicates strong operational viability and practical deployability.