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Staffing decisions for heterogeneous workers with turnover

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  • Hyun-Soo Ahn
  • Rhonda Righter
  • J. Shanthikumar

Abstract

In this paper we consider a firm that employs heterogeneous workers to meet demand for its product or service. Workers differ in their skills, speed, and/or quality, and they randomly leave, or turn over. Each period the firm must decide how many workers of each type to hire or fire in order to meet randomly changing demand forecasts at minimal expense. When the number of workers of each type can by continuously varied, the operational cost is jointly convex in the number of workers of each type, hiring and firing costs are linear, and a random fraction of workers of each type leave in each period, the optimal policy has a simple hire- up-to/fire-down-to structure. However, under the more realistic assumption that the number of workers of each type is discrete, the optimal policy is much more difficult to characterize, and depends on the particular notion of discrete convexity used for the cost function. We explore several different notions of discrete convexity and their impact on structural results for the optimal policy. Copyright Springer-Verlag 2005

Suggested Citation

  • Hyun-Soo Ahn & Rhonda Righter & J. Shanthikumar, 2005. "Staffing decisions for heterogeneous workers with turnover," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 62(3), pages 499-514, December.
    • Handle: RePEc:spr:mathme:v:62:y:2005:i:3:p:499-514
    DOI: 10.1007/s00186-005-0033-5
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    Cited by:

    1. N. Llort & A. Lusa & C. Martínez-Costa & M. Mateo, 2019. "A decision support system and a mathematical model for strategic workforce planning in consultancies," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 497-523, June.
    2. Fernandes, Rui & Gouveia, Borges & Pinho, Carlos, 2013. "A real options approach to labour shifts planning under different service level targets," European Journal of Operational Research, Elsevier, vol. 231(1), pages 182-189.
    3. Mincsovics, Gergely & Tan, Tarkan & Alp, Osman, 2009. "Integrated capacity and inventory management with capacity acquisition lead times," European Journal of Operational Research, Elsevier, vol. 196(3), pages 949-958, August.
    4. de la Torre, R. & Lusa, A. & Mateo, M., 2016. "A MILP model for the long term academic staff size and composition planning in public universities," Omega, Elsevier, vol. 63(C), pages 1-11.
    5. Andreas Drexl & Martin Mundschenk, 2008. "Long-term staffing based on qualification profiles," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 68(1), pages 21-47, August.
    6. Keumseok Kang & J. George Shanthikumar & Kemal Altinkemer, 2016. "Postponable Acceptance and Assignment: A Stochastic Dynamic Programming Approach," Manufacturing & Service Operations Management, INFORMS, vol. 18(4), pages 493-508, October.
    7. Song, Haiqing & Huang, Huei-Chuen, 2008. "A successive convex approximation method for multistage workforce capacity planning problem with turnover," European Journal of Operational Research, Elsevier, vol. 188(1), pages 29-48, July.
    8. Özlük, Özgür & Elimam, Abdelghani A. & Interaminense, Eduardo, 2010. "Optimum service capacity and demand management with price incentives," European Journal of Operational Research, Elsevier, vol. 204(2), pages 316-327, July.

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