Cross-Trained Staffing Levels With Heterogeneous Learning/Forgetting

A skilled workforce is an increasingly expensive resource for organizations. Determining minimum staffing levels to meet production requirements is critical to competitiveness. This paper investigates the effects of several exogenous and controllable factors on minimum staffing levels in parallel dual resource constrained (DRC) systems in the presence of heterogeneous individual learning and forgetting throughout a fixed-planning horizon. We examine factors of worker selection policy, task heterogeneity, individual cross-training level, time-schedule granularity, and the production requirement. We explore two cases of parallel systems in which a system consists of an equal number of workers and tasks and another with more tasks than workers. Results show that a best worker subset requires fewer workers than an average subset, greater task heterogeneity requires fewer workers with an equal number of workers and tasks. The magnitude of this effect is stronger with an average subset or less slack in the system capacity. Unlike previous studies with homogeneous workers or without considering learning and forgetting, setting a maximum allowable individual cross-training requires more workers. Further, the results suggest that the magnitude of this effect becomes greater with a best subset worker group or more slack in capacity. The time-schedule granularity does not affect the minimum staffing levels.