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The Inmate Assignment and Scheduling Problem and Its Application in the Pennsylvania Department of Corrections

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  • Mohammad Shahabsafa

    (Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015)

  • Tamás Terlaky

    (Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015)

  • Naga Venkata Chaitanya Gudapati

    (Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015)

  • Anshul Sharma

    (Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015)

  • George R. Wilson

    (Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015)

  • Louis J. Plebani

    (Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015)

  • Kristofer B. Bucklen

    (Pennsylvania Department of Corrections, Mechanicsburg, Pennsylvania 17050)

Abstract

The inmate assignment project, in close collaboration with the Pennsylvania Department of Corrections (PADoC), took five years from start to successful implementation. In this project we developed the Inmate Assignment Decision Support System (IADSS), for which the primary goal is simultaneous and system-wide optimal assignment of inmates to correctional institutions (CIs). We develop a novel hierarchical, multiobjective mixed-integer linear optimization (MILO) model, which accurately describes the inmate assignment problem (IAP). The IAP is the mathematical optimization formulation of the problem every correctional system faces, which is to assign inmates to CIs and schedule their programs, while considering all legal restrictions and best practice constraints. By using actual inmate data sets from the PADoC, we also demonstrate that the MILO model can be solved efficiently. IADSS enables the PADoC to significantly reduce the inmate population management costs and enhance public safety and security of the CIs. To the best of our knowledge this is the first time that operations research methodologies have been incorporated into the routine business practice of a correctional system and used to optimize its operations. This successful project opens a rich and untouched area for the application of operations research and optimization methodology. The new model and methodology can be utilized for the assignment of inmates in any correctional system.

Suggested Citation

  • Mohammad Shahabsafa & Tamás Terlaky & Naga Venkata Chaitanya Gudapati & Anshul Sharma & George R. Wilson & Louis J. Plebani & Kristofer B. Bucklen, 2018. "The Inmate Assignment and Scheduling Problem and Its Application in the Pennsylvania Department of Corrections," Interfaces, INFORMS, vol. 48(5), pages 467-483, October.
    • Handle: RePEc:inm:orinte:v:48:y:2018:i:5:p:467-483
    DOI: 10.1287/inte.2018.0962
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    References listed on IDEAS

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    1. J. P. Arabeyre & J. Fearnley & F. C. Steiger & W. Teather, 1969. "The Airline Crew Scheduling Problem: A Survey," Transportation Science, INFORMS, vol. 3(2), pages 140-163, May.
    2. Alberto Caprara & Paolo Toth & Daniele Vigo & Matteo Fischetti, 1998. "Modeling and Solving the Crew Rostering Problem," Operations Research, INFORMS, vol. 46(6), pages 820-830, December.
    3. H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
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