Single-machine scheduling with product category-based learning and forgetting effects

In today’s constantly changing work environment, the dynamic nature of employee skills, and the underlying learning and forgetting effects that influence production efficiency become increasingly important. As a consequence, especially during a production ramp-up, processing times benefit from learning effects when workers repeatedly perform similar tasks. To account for these skill development processes and the fact that different types of products are often processed on a single production line, we introduce a new learning and forgetting effect for single-machine scheduling. The effect assumes different product categories and considers intra-category learning effects and inter-category forgetting effects. Near-optimal or optimal solution methods for minimizing either the makespan or the total completion time are presented. For computationally intractable cases, we show promising performance and processing time-saving results utilizing 337,500 example instances to benchmark the proposed near-optimal heuristics. Further, we provide guidance to help practitioners identify production settings that benefit most from using the categorized effect.