A matheuristic for workforce planning with employee learning and stochastic demand

Abstract

This paper focuses on the opportunity to direct the development of responsive capacity by recognising that individuals learn through experience when designing workforce plans. We focus on the operations of a product manufacturer that seeks to maximise profit by selling multiple products, while recognising that demands for each product is uncertain. As such, we study a stochastic integer program wherein an organisation can hedge against uncertainty in demand both by holding inventory (at a cost) and building a more responsive production process. Solving this stochastic program presents many computational difficulties, including the fact that quantitative models of human learning are non-linear and the explosion of instance size that result from modelling uncertainty with scenarios. As a result, we propose a matheuristic for this problem and with an extensive computational study demonstrate its ability to produce high-quality solutions in little time.

Keywords:

• matheuristics
• learning
• workforce planning
• task assignment
• stochastic demand

Acknowledgements

The authors also would like to thank an anonymous Associate Editor and three anonymous reviewers for their valuable contributions to this paper.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This material is based upon work supported by the National Science Foundation [grant number CMMI-1266010].