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Flow Shop Scheduling with Partial Resource Flexibility

Author

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  • Richard L. Daniels

    (Terry College of Business, University of Georgia, Athens, Georgia 30602.)

  • Joseph B. Mazzola

    (McDonough School of Business, Georgetown University, Washington, D.C. 20057.)

  • Dailun Shi

    (IBM T. J. Watson Research Center, 19 Skyline Drive, Hawthorne, New York 10532.)

Abstract

Resource flexibility refers to the ability to dynamically reallocate units of resource from one stage of a production process to another in response to shifting bottlenecks. Recent research has demonstrated that substantial improvements in operational performance can be realized in both serial- and parallel-machine production environments through the effective utilization of resource flexibility. In these contexts the resource was assumed to exhibit complete flexibility. This research explores the extent to which the operational benefits associated with resource flexibility can be achieved in a flow shop environment using a partially flexible resource. Focusing on labor flexibility, we propose corresponding metrics for partial flexibility and formulate a model for flow shop scheduling with partial resource flexibility. On the basis of computational experiments, we explore properties pertaining to the relative amounts as well as the allocation of partial resource flexibility as it is distributed across the workforce. The conclusions drawn from this research provide significant insight into the management of flow shops with a workforce that is crosstrained to achieve partial flexibility. Moreover, we extend the principles developed by Jordan and Graves (1995) for partially flexible manufacturing plants to the flow shop scheduling environment, and we link these principles in a novel way to recent research on self-buffering flow lines.

Suggested Citation

  • Richard L. Daniels & Joseph B. Mazzola & Dailun Shi, 2004. "Flow Shop Scheduling with Partial Resource Flexibility," Management Science, INFORMS, vol. 50(5), pages 658-669, May.
    • Handle: RePEc:inm:ormnsc:v:50:y:2004:i:5:p:658-669
    DOI: 10.1287/mnsc.1040.0209
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    References listed on IDEAS

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    1. Richard L. Daniels & Joseph B. Mazzola, 1994. "Flow Shop Scheduling with Resource Flexibility," Operations Research, INFORMS, vol. 42(3), pages 504-522, June.
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    Cited by:

    1. Zhenzhen Yan & Sarah Yini Gao & Chung Piaw Teo, 2018. "On the Design of Sparse but Efficient Structures in Operations," Management Science, INFORMS, vol. 64(7), pages 3421-3445, July.
    2. Geurtsen, M. & Didden, Jeroen B.H.C. & Adan, J. & Atan, Z. & Adan, I., 2023. "Production, maintenance and resource scheduling: A review," European Journal of Operational Research, Elsevier, vol. 305(2), pages 501-529.
    3. Atif Açıkgöz & Ayşe Günsel & Cemil Kuzey & Halil Zaim, 2016. "Team Foresight in New Product Development Projects," Group Decision and Negotiation, Springer, vol. 25(2), pages 289-323, March.
    4. Miloš Milenković & Susana Val & Nebojša Bojović, 2023. "Simultaneous lot sizing and scheduling in the animal feed premix industry," Operational Research, Springer, vol. 23(2), pages 1-40, June.
    5. Gultekin, Hakan, 2012. "Scheduling in flowshops with flexible operations: Throughput optimization and benefits of flexibility," International Journal of Production Economics, Elsevier, vol. 140(2), pages 900-911.
    6. Nigel Wadeson, 2013. "The Division of Labour under Uncertainty," Journal of Institutional and Theoretical Economics (JITE), Mohr Siebeck, Tübingen, vol. 169(2), pages 253-274, June.
    7. Ma, Ran & Guo, Sainan & Miao, Cuixia, 2021. "A semi-online algorithm and its competitive analysis for parallel-machine scheduling problem with rejection," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    8. Olivella, Jordi & Nembhard, David, 2016. "Calibrating cross-training to meet demand mix variation and employee absence," European Journal of Operational Research, Elsevier, vol. 248(2), pages 462-472.
    9. Pei, Jun & Liu, Xinbao & Fan, Wenjuan & Pardalos, Panos M. & Lu, Shaojun, 2019. "A hybrid BA-VNS algorithm for coordinated serial-batching scheduling with deteriorating jobs, financial budget, and resource constraint in multiple manufacturers," Omega, Elsevier, vol. 82(C), pages 55-69.

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