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Stochastic Optimization Models for Workforce Planning, Operations, and Risk Management

Author

Listed:
  • Michael J. Davis

    (Chief Analytics Office, IBM Corporation, Armonk, New York 10504)

  • Yingdong Lu

    (Mathematical Sciences, AI Science, IBM Research, Yorktown Heights, New York 10598)

  • Mayank Sharma

    (AI and Blockchain Solutions, IBM Research, Yorktown Heights, New York 10598)

  • Mark S. Squillante

    (Mathematical Sciences, AI Science, IBM Research, Yorktown Heights, New York 10598)

  • Bo Zhang

    (Mathematical Sciences, AI Science, IBM Research, Yorktown Heights, New York 10598)

Abstract

A framework for unified decision making under uncertainty that supports financial planning, operations management, and risk management for workforce applications is proposed and analyzed. The management of enterprise workforce is conducted at the granularity of cohorts of individuals with similar attributes of interest. A time inhomogeneous Markov chain is developed to model the evolution of these cohorts over time. Stochastic control problems based on versions of the controlled Markov chain are formulated to maximize profit under a set of workforce decisions. Extensive data analysis and innovative computational approaches enable us to solve these stochastic control problems for large-scale systems, with real-world business case studies demonstrating the use of this unified decision support capability for large-scale enterprises.

Suggested Citation

  • Michael J. Davis & Yingdong Lu & Mayank Sharma & Mark S. Squillante & Bo Zhang, 2018. "Stochastic Optimization Models for Workforce Planning, Operations, and Risk Management," Service Science, INFORMS, vol. 10(1), pages 40-57, March.
    • Handle: RePEc:inm:orserv:v:10:y:2018:i:1:p:40-57
    DOI: 10.1287/serv.2017.0199
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    References listed on IDEAS

    as
    1. Rick Lawrence & Claudia Perlich & Saharon Rosset & Ildar Khabibrakhmanov & Shilpa Mahatma & Sholom Weiss & Matt Callahan & Matt Collins & Alexey Ershov & Shiva Kumar, 2010. "Operations Research Improves Sales Force Productivity at IBM," Interfaces, INFORMS, vol. 40(1), pages 33-46, February.
    2. Heng Cao & Jianying Hu & Chen Jiang & Tarun Kumar & Ta-Hsin Li & Yang Liu & Yingdong Lu & Shilpa Mahatma & Aleksandra Mojsilović & Mayank Sharma & Mark S. Squillante & Yichong Yu, 2011. "OnTheMark: Integrated Stochastic Resource Planning of Human Capital Supply Chains," Interfaces, INFORMS, vol. 41(5), pages 414-435, October.
    Full references (including those not matched with items on IDEAS)

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