Single-Employee Scheduling with Continuous Learning Effect: Algorithms and Cost Impact under Classical Performance Measures

In many real-world scheduling problems, an employee's productivity fluctuates continuously as learning occurs inAhumanmachine interactions. This paper addresses single-employee scheduling problems considering this continuous learning effect and various objectives. We derive a formula to calculate the makespan and show that the order of the jobs has no effect on the makespan, as opposed to cases where the learning effect is assumed to be discrete. In addition, we demonstrate the optimality of the Shortest Normal Processing Time first (SPT), SPT ), Earliest Due Date first (EDD), EDD ), and Shortest Weighted Normal Processing Time first ( WSPT ) rules for minimizing total completion time, weighted total completion time, and maximum lateness, respectively. A numerical example underscores the key role of the learning rate in task arrangement and provides insights for learning organizations or individuals to manage tasks effectively.