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 under-lying 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 cate-gories 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 re-sults 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.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

In today's dynamic work environment, employee skills and the effects of learning and forgetting have a significant impact on production efficiency. This study presents a new learning and forgetting effect model for single-machine scheduling, considering different product categories and optimizing processing times.