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A Tri-level minimum information demand estimation model to the inverse p-median problem

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  • Mehdi Zaferanieh

    (Hakim Sabzevari University)

  • Maryam Abareshi

    (Hakim Sabzevari University)

Abstract

In this paper, a novel inverse approach to the p-median problem is introduced in which the locations of p facilities and their supplies are known, while the demands of client nodes and the fraction allocated to each facility need to be estimated. To achieve this purpose, a tri-level programming problem is proposed. The primary objective of the first-level model is to minimize the sum of the squared differences between the estimated demand values and the observed target values. The second and third level problems together form a bi-level p-median model that incorporates the minimum information approach into the allocation phase. By substituting the optimality conditions of the third-level problem into the second one, a nonlinear bi-level mixed-integer model is obtained, which is addressed by using a particle swarm optimization algorithm. The added value of the tri-level model and the proposed method is verified by some small and large-sized examples.

Suggested Citation

  • Mehdi Zaferanieh & Maryam Abareshi, 2025. "A Tri-level minimum information demand estimation model to the inverse p-median problem," OPSEARCH, Springer;Operational Research Society of India, vol. 62(2), pages 877-904, June.
  • Handle: RePEc:spr:opsear:v:62:y:2025:i:2:d:10.1007_s12597-024-00815-8
    DOI: 10.1007/s12597-024-00815-8
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    References listed on IDEAS

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    1. Ravindra K. Ahuja & James B. Orlin, 2001. "Inverse Optimization," Operations Research, INFORMS, vol. 49(5), pages 771-783, October.
    2. Calvete, Herminia I. & Gale, Carmen & Mateo, Pedro M., 2008. "A new approach for solving linear bilevel problems using genetic algorithms," European Journal of Operational Research, Elsevier, vol. 188(1), pages 14-28, July.
    3. Nguyen Thanh Toan & Huy Minh Le & Kien Trung Nguyen, 2024. "The reverse selective balance center location problem on trees," OPSEARCH, Springer;Operational Research Society of India, vol. 61(1), pages 483-497, March.
    4. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    5. Behrooz Alizadeh & Somayeh Bakhteh, 2017. "A modified firefly algorithm for general inverse p-median location problems under different distance norms," OPSEARCH, Springer;Operational Research Society of India, vol. 54(3), pages 618-636, September.
    6. Maryam Abareshi & Mehdi Zaferanieh & Mohammad Reza Safi, 2019. "Origin-Destination Matrix Estimation Problem in a Markov Chain Approach," Networks and Spatial Economics, Springer, vol. 19(4), pages 1069-1096, December.
    7. Maher, M. J., 1983. "Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach," Transportation Research Part B: Methodological, Elsevier, vol. 17(6), pages 435-447, December.
    8. T. Abrahamsson, 1998. "Estimation of Origin-Destination Matrices Using Traffic Counts- A Literature Survey," Working Papers ir98021, International Institute for Applied Systems Analysis.
    9. Fahimeh Baroughi Bonab & Rainer Burkard & Elisabeth Gassner, 2011. "Inverse p-median problems with variable edge lengths," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 73(2), pages 263-280, April.
    10. Diaz, Juan A. & Fernandez, Elena, 2006. "Hybrid scatter search and path relinking for the capacitated p-median problem," European Journal of Operational Research, Elsevier, vol. 169(2), pages 570-585, March.
    11. Abareshi, Maryam & Zaferanieh, Mehdi, 2019. "A bi-level capacitated P-median facility location problem with the most likely allocation solution," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 1-20.
    12. Fisk, C. S., 1988. "On combining maximum entropy trip matrix estimation with user optimal assignment," Transportation Research Part B: Methodological, Elsevier, vol. 22(1), pages 69-73, February.
    13. Nie, Yu & Zhang, H.M. & Recker, W.W., 2005. "Inferring origin-destination trip matrices with a decoupled GLS path flow estimator," Transportation Research Part B: Methodological, Elsevier, vol. 39(6), pages 497-518, July.
    14. Maryam Abareshi & Mehdi Zaferanieh & Bagher Keramati, 2017. "Path Flow Estimator in an Entropy Model Using a Nonlinear L-Shaped Algorithm," Networks and Spatial Economics, Springer, vol. 17(1), pages 293-315, March.
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