Multiskilled personnel assignment problem under uncertain demand: A benchmarking analysis

Simple item page
dc.contributor.authorAugusto Henao, Cesar
dc.contributor.authorBatista, Ana
dc.contributor.authorFelipe Porto, Andres
dc.contributor.authorGonzalez, Virginia, I
dc.date.accessioned2025-01-20T21:09:45Z
dc.date.available2025-01-20T21:09:45Z
dc.date.issued2022
dc.description.abstractThe personnel assignment problem in different service industries aims to minimize the staff surplus/shortage costs. However, uncertainty in the staff demand challenges the accomplishment of that objective. This research studies the personnel assignment problem considering uncertain demand and multiskilled workforce configured through a 2-chaining strategy. We develop a two-stage stochastic optimization (TSSO) approach to calculate the multiskilling requirements that minimize the training costs and the expected costs of staff surplus/shortage. Later, we evaluate and compare the performance of the TSSO approach solutions with the solutions of two alternative optimization approaches under uncertainty -robust optimization (RO) and closed-form equation (CF). These two alternative approaches were published in Henao et al. [1] and Henao et al. [2], respectively. In addition, we compare the performance of the TSSO approach solutions with the solution of the deterministic (DT) approach and the solutions of myopic multiskilling approaches. To make performance comparisons between the different approaches, we used both real and simulated data derived from a retail store operating in Chile. The results show that, for different demand variability levels, TSSO, RO, and CF always belong to the set of approaches with the lowest average total cost. That is, in this group, there are no statistical differences from one approach to another, so these approaches are the most cost-effective. We also provide insights to retail decision-makers for addressing two key aspects. First, the methodology allows to address two fundamental multiskilling issues: how much multiskilling to add and how it should be added. Second, it is provided understanding on how to select the most suitable approach according to the balance between the conservatism and the reliability associated with the solutions delivered by each approach. Finally, we identify some methodological challenges for future research, such as the evaluation of k-chaining strategies with k >= 2.
dc.description.funderFundacion para la Promocion de la Investigacion y la Tecnologia (FPIT)
dc.fuente.origenWOS
dc.identifier.doi10.3934/mbe.2022232
dc.identifier.issn1551-0018
dc.identifier.urihttps://doi.org/10.3934/mbe.2022232
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/93543
dc.identifier.wosidWOS:000772757600006
dc.issue.numero5
dc.language.isoen
dc.pagina.final4975
dc.pagina.inicio4946
dc.revistaMathematical biosciences and engineering
dc.rightsacceso restringido
dc.subjectworkforce flexibility
dc.subjectmultiskilling
dc.subjectcross-training
dc.subject2-chaining
dc.subjectstochastic optimization
dc.subjectrobust optimization
dc.subjectretail
dc.titleMultiskilled personnel assignment problem under uncertain demand: A benchmarking analysis
dc.typeartículo
dc.volumen19
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
Files
Collections
Artículos de revistas