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Scheduling Workforce and Workflow in a High Volume Factory

Author

Listed:
  • Oded Berman

    (University of Toronto, Toronto, Ontario, Canada)

  • Richard C. Larson

    (Center for Advanced Educational Services, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Edieal Pinker

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

We define a high volume factory to be a connected network of workstations, at which assigned workers process work-in-progress that flows at high rates through the workstations. A high rate usually implies that each worker processes many pieces per hour, enough so that work can be described as a deterministic hourly flow rate rather than, say, a stochastic number of discrete entities. Examples include mail processing and sorting, check processing, telephoned order processing, and inspecting and packaging of certain foods. Exogenous work may enter the factory at any workstation according to any time-of-day profile. Work-in-progress flows through the factory in discrete time according to Markovian routings. Workers, who in general are cross-trained, may work part time or full time shifts, may start work only at designated shift starting times, and may change job assignments at mid shift. In order to smooth the flow of work-in-progress through the service factory, work-in-progress may be temporarily inventoried (in buffers) at work stations. The objective is to schedule the workers (and correspondingly, the workflow) in a manner that minimizes labor costs subject to a variety of service-level, contractual and physical constraints. Motivated in part by analysis techniques of discrete time linear time-invariant (LTI) systems, an object-oriented linear programming (OOLP) model is developed. Using exogenous input work profiles typical of large U.S. mail processing facilities, illustrative computational results are included.

Suggested Citation

  • Oded Berman & Richard C. Larson & Edieal Pinker, 1997. "Scheduling Workforce and Workflow in a High Volume Factory," Management Science, INFORMS, vol. 43(2), pages 158-172, February.
    • Handle: RePEc:inm:ormnsc:v:43:y:1997:i:2:p:158-172
    DOI: 10.1287/mnsc.43.2.158
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    Cited by:

    1. Ağralı, Semra & Taşkın, Z. Caner & Ünal, A. Tamer, 2017. "Employee scheduling in service industries with flexible employee availability and demand," Omega, Elsevier, vol. 66(PA), pages 159-169.
    2. Maria R. Ibanez & Jonathan R. Clark & Robert S. Huckman & Bradley R. Staats, 2018. "Discretionary Task Ordering: Queue Management in Radiological Services," Management Science, INFORMS, vol. 64(9), pages 4389-4407, September.
    3. Sunder Kekre & Nicola Secomandi & Erkut Sönmez & Kenneth West, 2009. "OM Practice--Balancing Risk and Efficiency at a Major Commercial Bank," Manufacturing & Service Operations Management, INFORMS, vol. 11(1), pages 160-173, December.
    4. Michael J. Brusco & Larry W. Jacobs, 2000. "Optimal Models for Meal-Break and Start-Time Flexibility in Continuous Tour Scheduling," Management Science, INFORMS, vol. 46(12), pages 1630-1641, December.
    5. Noah Gans & Yong-Pin Zhou, 2002. "Managing Learning and Turnover in Employee Staffing," Operations Research, INFORMS, vol. 50(6), pages 991-1006, December.
    6. Techawiboonwong, Atthawit & Yenradee, Pisal & Das, Sanchoy K., 2006. "A master scheduling model with skilled and unskilled temporary workers," International Journal of Production Economics, Elsevier, vol. 103(2), pages 798-809, October.
    7. Jonathan F. Bard & Lin Wan, 2008. "Workforce Design with Movement Restrictions Between Workstation Groups," Manufacturing & Service Operations Management, INFORMS, vol. 10(1), pages 24-42, November.
    8. Jonathan Bard & David Morton & Yong Wang, 2007. "Workforce planning at USPS mail processing and distribution centers using stochastic optimization," Annals of Operations Research, Springer, vol. 155(1), pages 51-78, November.
    9. Seyed M. Iravani & Mark P. Van Oyen & Katharine T. Sims, 2005. "Structural Flexibility: A New Perspective on the Design of Manufacturing and Service Operations," Management Science, INFORMS, vol. 51(2), pages 151-166, February.
    10. Richard Charles Larson, 2002. "Public Sector Operations Research: A Personal Journey," Operations Research, INFORMS, vol. 50(1), pages 135-145, February.
    11. X Zhang & A Chakravarthy & Q Gu, 2009. "Equipment scheduling problem under disruptions in mail processing and distribution centres," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 598-610, May.
    12. Xue Bai & Ram Gopal & Manuel Nunez & Dmitry Zhdanov, 2012. "On the Prevention of Fraud and Privacy Exposure in Process Information Flow," INFORMS Journal on Computing, INFORMS, vol. 24(3), pages 416-432, August.
    13. L Wan & J F Bard, 2007. "Weekly staff scheduling with workstation group restrictions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(8), pages 1030-1046, August.
    14. Jaime Miranda & Pablo A. Rey & Antoine Sauré & Richard Weber, 2018. "Metro Uses a Simulation-Optimization Approach to Improve Fare-Collection Shift Scheduling," Interfaces, INFORMS, vol. 48(6), pages 529-542, November.
    15. Menezes, Mozart B.C. & Kim, Seokjin & Huang, Rongbing, 2006. "Optimal workforce size and allocation for urban retail chains," European Journal of Operational Research, Elsevier, vol. 175(2), pages 1151-1163, December.
    16. Wallace J. Hopp & Eylem Tekin & Mark P. Van Oyen, 2004. "Benefits of Skill Chaining in Serial Production Lines with Cross-Trained Workers," Management Science, INFORMS, vol. 50(1), pages 83-98, January.
    17. Zhilan Lou & Wanchen Jie & Shuzhu Zhang, 2020. "Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations," Sustainability, MDPI, vol. 12(19), pages 1-17, September.
    18. Dalia Attia & Reinhard Bürgy & Guy Desaulniers & François Soumis, 2019. "A decomposition-based heuristic for large employee scheduling problems with inter-department transfers," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 7(4), pages 325-357, December.
    19. Sayin, Serpil & Karabati, Selcuk, 2007. "Assigning cross-trained workers to departments: A two-stage optimization model to maximize utility and skill improvement," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1643-1658, February.
    20. Eylem Tekin & Wallace J. Hopp & Mark P. Van Oyen, 2002. "Benefits of Skill Chaining in Production Lines with Cross-Trained Workers: An Extended Abstract," Manufacturing & Service Operations Management, INFORMS, vol. 4(1), pages 17-20.

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