A matheuristic for workforce planning with employee learning and stochastic demand

Silviya Valeva, Mike Hewitt and Barrett W. Thomas

International Journal of Production Research, 2017, vol. 55, issue 24, 7380-7397

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.

Date: 2017
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DOI: 10.1080/00207543.2017.1349950

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