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Synthetic Demand Flow Generation Using the Proximity Factor

Author

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  • Ekin Yalvac

    (Edward P. Fits Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC 27606, USA
    These authors contributed equally to this work.)

  • Michael G. Kay

    (Edward P. Fits Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC 27606, USA
    These authors contributed equally to this work.)

Abstract

One of the biggest challenges in designing a logistics network is predicting the demand flows between all pairs of points in the network. Currently, the gravity model is mainly used for estimating the demand flow between points. However, the gravity model uses historical data to estimate values for its multiple parameters and distance between pairs to forecast the demand flow. Distance values close to zero and unprecedented changes in demand flow data create numerical instability for the gravity model’s output. Hence, the proximity factor, a single parameter model that uses the relative ordering of pairs instead of distance, was developed. In this paper, we systematically compare the proximity factor and the gravity model. It is shown that the proximity factor is a robust in terms of reliability and competitive alternative to the gravity model. According to our analysis, the proximity factor model can replace the gravity model in some applications when no historical data are available to adjust the parameters of the latter.

Suggested Citation

  • Ekin Yalvac & Michael G. Kay, 2025. "Synthetic Demand Flow Generation Using the Proximity Factor," Forecasting, MDPI, vol. 7(1), pages 1-20, March.
    • Handle: RePEc:gam:jforec:v:7:y:2025:i:1:p:14-:d:1615516
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    References listed on IDEAS

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    1. Melo, M.T. & Nickel, S. & Saldanha-da-Gama, F., 2009. "Facility location and supply chain management - A review," European Journal of Operational Research, Elsevier, vol. 196(2), pages 401-412, July.
    2. Markus Schläpfer & Lei Dong & Kevin O’Keeffe & Paolo Santi & Michael Szell & Hadrien Salat & Samuel Anklesaria & Mohammad Vazifeh & Carlo Ratti & Geoffrey B. West, 2021. "The universal visitation law of human mobility," Nature, Nature, vol. 593(7860), pages 522-527, May.
    3. Jean-François Cordeau & Federico Pasin & Marius Solomon, 2006. "An integrated model for logistics network design," Annals of Operations Research, Springer, vol. 144(1), pages 59-82, April.
    4. Kalahasthi, Lokesh & Holguín-Veras, José & Yushimito, Wilfredo F., 2022. "A freight origin-destination synthesis model with mode choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    5. Lenormand, Maxime & Bassolas, Aleix & Ramasco, José J., 2016. "Systematic comparison of trip distribution laws and models," Journal of Transport Geography, Elsevier, vol. 51(C), pages 158-169.
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