Abstract
This paper examines the behavior of several single machine scheduling problems when presented with times that are random and potentially dependent. A position-based learning effect model is revisited and optimal schedules are derived under several typical performance measures. A sum-of-processing-time based model is proposed to incorporate the learning effects and deteriorations in one unified framework. Optimal schedules are derived to minimize the maximum lateness or jointly minimize the completion times under the proposed model, and the model is used to solve the optimal issuing problem.
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