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A multi-period capacitated school location problem with modular equipment and closest assignment considerations

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  • Eric Delmelle
  • Jean-Claude Thill
  • Dominique Peeters
  • Isabelle Thomas

Abstract

In rapidly growing urban areas, it is deemed vital to expand (or contract) an existing network of public facilities to meet anticipated changes in the level of demand. We present a multi-period capacitated median model for school network facility location planning that minimizes transportation costs, while functional costs are subject to a budget constraint. The proposed Vintage Flexible Capacitated Location Problem (ViFCLP) has the flexibility to account for a minimum school-age closing requirement, while the maximum capacity of each school can be adjusted by the addition of modular units. Non-closest assignments are controlled by the introduction of a parameter penalizing excess travel. The applicability of the ViFCLP is illustrated on a large US school system (Charlotte-Mecklenburg, North Carolina) where high school demand is expected to grow faster with distance to the city center. Higher school capacities and greater penalty on travel impedance parameter reduce the number of non-closest assignments. The proposed model is beneficial to policy makers seeking to improve the provision and efficiency of public services over a multi-period planning horizon. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Eric Delmelle & Jean-Claude Thill & Dominique Peeters & Isabelle Thomas, 2014. "A multi-period capacitated school location problem with modular equipment and closest assignment considerations," Journal of Geographical Systems, Springer, vol. 16(3), pages 263-286, July.
    • Handle: RePEc:kap:jgeosy:v:16:y:2014:i:3:p:263-286
    DOI: 10.1007/s10109-013-0195-2
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    Cited by:

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    2. Correia, Isabel & Melo, Teresa, 2016. "A computational comparison of formulations for a multi-period facility location problem with modular capacity adjustments and flexible demand fulfillment," Technical Reports on Logistics of the Saarland Business School 11, Saarland University of Applied Sciences (htw saar), Saarland Business School.
    3. Mayerle, Sérgio F. & Rodrigues, Hidelbrando F. & Neiva de Figueiredo, João & De Genaro Chiroli, Daiane M., 2022. "Optimal student/school/class/teacher/classroom matching to support efficient public school system resource allocation," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    4. Linlin Zhang & Xiaobin Zhang & Huiling Huang & Liang Zhang & Huan Li, 2022. "Spatial Accessibility of Multiple Facilities for Affordable Housing Neighborhoods in Harbin, China," Land, MDPI, vol. 11(11), pages 1-19, October.
    5. Allman, Andrew & Zhang, Qi, 2020. "Dynamic location of modular manufacturing facilities with relocation of individual modules," European Journal of Operational Research, Elsevier, vol. 286(2), pages 494-507.
    6. Wei, Ran & Feng, Xin & Rey, Sergio & Knaap, Elijah, 2022. "Reducing racial segregation of public school districts," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    7. Correia, Isabel & Melo, Teresa, 2019. "Dynamic facility location problem with modular capacity adjustments under uncertainty," Technical Reports on Logistics of the Saarland Business School 17, Saarland University of Applied Sciences (htw saar), Saarland Business School.
    8. Chris Jacobs-Crisioni & Ana I. Moreno-Monroy & Mert Kompil & Lewis Dijkstra, 2024. "Estimating school provision, access and costs from local pupil counts under decentralised governance," Journal of Geographical Systems, Springer, vol. 26(1), pages 7-46, January.
    9. Liao, Cong & Scheuer, Bronte & Dai, Teqi & Tian, Yuan, 2020. "Optimizing the spatial assignment of schools to reduce both inequality of educational opportunity and potential opposition rate through introducing random mechanism into proximity-based system," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).

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    More about this item

    Keywords

    Dynamic school location; Non-closest assignment; Modular capacity; R53; C60; C611; C63;
    All these keywords.

    JEL classification:

    • R53 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Public Facility Location Analysis; Public Investment and Capital Stock
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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