A matheuristic for workforce planning with employee learning and stochastic demand. Issue 24 (17th December 2017)

• Record Type: Journal Article
• Authors: Valeva, Silviya; Hewitt, Mike; Thomas, Barrett W.
• Is Part Of: International journal of production research. Volume 55:Issue 24(2017)
• Page Start: 7380
• Page End: 7397
• Publication Date: 2017-12-17
• Subjects: matheuristics -- learning -- workforce planning -- task assignment -- stochastic demand; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2017.1349950
• Languages: English
• ISSNs: 0020-7543

Abstract

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.