Multiperiod Stochastic Resource Planning in Professional Services Organizations
研究了专业服务组织中多周期随机资源规划问题，通过马尔可夫决策过程和近似动态规划算法，在合理时间内获得动态自适应方案，相比滚动时域方法提高了盈利能力和内部资源利用率。
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.