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Multiperiod Stochastic Resource Planning in Professional Services Organizations
Author(s) -
Solomon Stanislaus,
Li Haitao,
Womer Keith,
Santos Cipriano
Publication year - 2019
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12370
Subject(s) - computer science , profitability index , workforce planning , operations research , stochastic programming , markov decision process , resource allocation , time horizon , benchmark (surveying) , outsourcing , resource (disambiguation) , flexibility (engineering) , workforce , markov process , mathematical optimization , business , economics , marketing , mathematics , computer network , statistics , management , geodesy , finance , geography , economic growth
ABSTRACT Resource planning (RP) in a professional service organization matches workforce resources with project tasks while considering a myriad of factors such as skill requirements, service delivery role, skill type, workforce proficiency level, and geographical location. The multiperiod stochastic resource planning studied in this article extends the one‐period deterministic resource planning by explicitly coping with both internal resource attrition and project demand uncertainty in a sequential decision‐making framework. It allows resource managers to make effective use of their internal resources and identify the need to outsource to external contingent resources. We model the multiperiod stochastic resource planning as a Markov decision process and implement an approximate dynamic programming algorithm to obtain dynamic and adaptive solutions in reasonable computation times. A comprehensive computational study shows that our approximate dynamic programming algorithm achieves higher profitability and internal resource utilization compared to the rolling horizon approach used as a benchmark.