Balancing flexibility and inventory in workforce planning with learning

Silviya Valeva, Mike Hewitt, Barrett W. Thomas and Kenneth G. Brown

International Journal of Production Economics, 2017, vol. 183, issue PA, 194-207

Abstract: We examine the problem of assigning workers to tasks, seeking to maximize profits, while taking in consideration learning through experience and stochasticity in demand. As quantitative descriptions of human learning are non-linear, we employ a reformulation technique that uses binary and continuous variables and linear constraints and is mathematically equivalent in nearly all cases. Similarly, as demand is not assumed to be known with certainty, we embed this mixed integer representation of how experience translates to productivity in a stochastic workforce assignment model. With an extensive computational study and analysis of (near-)optimal solutions, we demonstrate that modeling both learning and uncertainty in demand leads to improved task assignments. Furthermore, we formulate and test hypotheses based on these solutions that yield insights into how best to manage practice, cross training, and inventory. We show that cross training increases as demand uncertainty increases, worker practice increases as inventory holding costs increase, and workers with less initial experience receive more practice than workers with higher initial experience.

Keywords: Workforce planning; Learning; Stochastic programming (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S092552731630295X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:183:y:2017:i:pa:p:194-207

DOI: 10.1016/j.ijpe.2016.10.026

Access Statistics for this article

International Journal of Production Economics is currently edited by Stefan Minner

More articles in International Journal of Production Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().