Production scheduling with autonomous and induced learning

Abstract

The vast majority of scheduling research involving the learning effect only considers autonomous learning, i.e. learning by doing. Proactive investment in learning promotion, i.e. induced learning, is rarely considered. Nevertheless, induced learning is important for total production cost reduction and helping managers control the production systems, which can be interpreted as management or investment seeking to improve employees’ working efficiency. We consider in this paper scheduling models with both autonomous and induced learning. The objective is to find the optimal sequence and level of induced learning that optimise a scheduling criterion plus the investment cost. We propose polynomial-time algorithms to solve all the single-machine scheduling problems considered and the parallel-machine problem to minimise the total completion time plus the investment cost. We also propose an approximate algorithm for the parallel-machine problem to minimise the makespan plus the investment cost.

Keywords:

• Scheduling
• autonomous and induced learning effects
• classical scheduling criteria

Acknowledgements

We are thankful to three anonymous referees for their helpful comments and suggestions on an earlier version of our paper. Ji was supported in part by the National Natural Science Foundation of China (Grant No. 11971434) and the Contemporary Business and Trade Research Center of Zhejiang Gongshang University, which is a key Research Institute of Social Sciences and Humanities of the Ministry of Education of China. Cheng was supported in part by The Hong Kong Polytechnic University under the Fung Yiu King - Wing Hang Bank Endowed Professorship in Business Administration.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 11971434]; The Hong Kong Polytechnic University under the Fung Yiu King - Wing Hang Bank Endowed Professorship in Business Administration: [Grant Number NA].