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This paper presents an approach for selecting workers for tasks of varying complexity based on individual learning and forgetting characteristics in order to improve system productivity. The performance of a learning and forgetting-based selection (LFBS) policy is examined using simulation and compared to a baseline policy representing criteria used in practice. The effects of factors including worker redundancy and task-tenure on productivity are also examined in the environment of continuously staffed independent tasks. Results demonstrate that the LFBs policy significantly improves productivity relative to common practice and suggests that lower levels of redundancy and shorter tasktenures tend to mitigate some of the negative effects of forgetting.

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This research proposed a method to improve bending strength of RCS used in hollow core production by finding the optimal levels of factors in the mixing process. Process factors under study were temperature of sand, time to release phenolic resin, time to release hexamine solution, time to start air blowing, air blowing time duration, and time to release calcium stearate. Experiments with Central Composite Face-centered (CCF) design were performed to save the number of experimental runs. Then, backward elimination regression analysis was performed to find out the relationship equation of bending strength and significant process factors. Next, the optimization technique was applied to determine the optimal setting of those significant process factors. The comfirmatory result showed that bending strength was significantly improved.