Heuristic approach for assigning workers to tasks based on individual learning rates

A heuristic worker-task assignment based on individual worker learning rates is examined for two tasks, one with a long production run, the other with a short production run. Simulations of total productivity are performed under several experimental conditions based on empirical industrial worker productivity measures. Results indicate that the heuristic method significantly improves overall productivity under empirically observed conditions and under many experimental conditions. The heuristic policy performs best in conditions where workers learn more gradually. Conditions are discussed where the heuristic policy provides the greatest potential for improvement based on factors including the mixture of production cycles, mean learning rate, mean forgetting rate, mean prior expertise, variance of prior expertise and variance of steady-state productivity.