Journal article
A matheuristic for workforce planning with employee learning and stochastic demand
International journal of production research, Vol.55(24), pp.7380-7397
12/17/2017
DOI: 10.1080/00207543.2017.1349950
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.
Details
- Title: Subtitle
- A matheuristic for workforce planning with employee learning and stochastic demand
- Creators
- Silviya Valeva - Applied Mathematical and Computational Sciences, University of Iowa, Iowa City, IA, USA.Mike Hewitt - Loyola University ChicagoBarrett W. Thomas - Department of Management Sciences, Tippie College of Business, Iowa City, IA, USA.
- Resource Type
- Journal article
- Publication Details
- International journal of production research, Vol.55(24), pp.7380-7397
- Publisher
- Taylor & Francis
- DOI
- 10.1080/00207543.2017.1349950
- ISSN
- 0020-7543
- eISSN
- 1366-588X
- Grant note
- CMMI-1266010 / National Science Foundation (10.13039/100000001)
- Language
- English
- Date published
- 12/17/2017
- Academic Unit
- Bus Admin College; Business Analytics
- Record Identifier
- 9984380498002771
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