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Approximation schemes for districting problems with probabilistic constraints

Author

Listed:
  • Diglio, Antonio
  • Peiró, Juanjo
  • Piccolo, Carmela
  • Saldanha-da-Gama, Francisco

Abstract

In this work a districting problem with stochastic demand is investigated. Chance-constraints are used to model the balancing requirements. Explicit contiguity constraints are also considered. After motivating the problem and discussing several modeling aspects, an approximate deterministic counterpart is proposed which is the core of new solution algorithms devised. The latter are based upon a location-allocation scheme, whose first step consists of considering either a problem with a sample of scenarios or a sample of single-scenario problems. This leads to two variants of a new heuristic. The second version calls for the use of a so-called attractiveness function as a means to find a good trade-off between the (approximate) solutions obtained for the single-scenario problems. Different definitions of such functions are discussed. Extensive computational tests were performed whose results are reported.

Suggested Citation

  • Diglio, Antonio & Peiró, Juanjo & Piccolo, Carmela & Saldanha-da-Gama, Francisco, 2023. "Approximation schemes for districting problems with probabilistic constraints," European Journal of Operational Research, Elsevier, vol. 307(1), pages 233-248.
  • Handle: RePEc:eee:ejores:v:307:y:2023:i:1:p:233-248
    DOI: 10.1016/j.ejor.2022.09.005
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    1. Bruce L. Miller & Harvey M. Wagner, 1965. "Chance Constrained Programming with Joint Constraints," Operations Research, INFORMS, vol. 13(6), pages 930-945, December.
    2. Kiya, Farhad & Davoudpour, Hamid, 2012. "Stochastic programming approach to re-designing a warehouse network under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(5), pages 919-936.
    3. Shakiba Enayati & Osman Y. Özaltın & Maria E. Mayorga, 2020. "Designing Ambulance Service Districts Under Uncertainty," International Series in Operations Research & Management Science, in: Roger Z. Ríos-Mercado (ed.), Optimal Districting and Territory Design, chapter 0, pages 153-170, Springer.
    4. María Salazar-Aguilar & Roger Ríos-Mercado & Mauricio Cabrera-Ríos, 2011. "New Models for Commercial Territory Design," Networks and Spatial Economics, Springer, vol. 11(3), pages 487-507, September.
    5. John Gunnar Carlsson, 2012. "Dividing a Territory Among Several Vehicles," INFORMS Journal on Computing, INFORMS, vol. 24(4), pages 565-577, November.
    6. Djordje Dugošija & Aleksandar Savić & Zoran Maksimović, 2020. "A new integer linear programming formulation for the problem of political districting," Annals of Operations Research, Springer, vol. 288(1), pages 247-263, May.
    7. Seda Yanık & Burcin Bozkaya, 2020. "A Review of Districting Problems in Health Care," International Series in Operations Research & Management Science, in: Roger Z. Ríos-Mercado (ed.), Optimal Districting and Territory Design, chapter 0, pages 31-55, Springer.
    8. Nemirovski, Arkadi, 2012. "On safe tractable approximations of chance constraints," European Journal of Operational Research, Elsevier, vol. 219(3), pages 707-718.
    9. Camacho-Collados, M. & Liberatore, F. & Angulo, J.M., 2015. "A multi-criteria Police Districting Problem for the efficient and effective design of patrol sector," European Journal of Operational Research, Elsevier, vol. 246(2), pages 674-684.
    10. Bruno, Giuseppe & Genovese, Andrea & Piccolo, Carmela, 2017. "Territorial amalgamation decisions in local government: Models and a case study from Italy," Socio-Economic Planning Sciences, Elsevier, vol. 57(C), pages 61-72.
    11. Federica Ricca & Andrea Scozzari & Bruno Simeone, 2013. "Political Districting: from classical models to recent approaches," Annals of Operations Research, Springer, vol. 204(1), pages 271-299, April.
    12. Dinçer Konur & Joseph Geunes, 2019. "Integrated districting, fleet composition, and inventory planning for a multi-retailer distribution system," Annals of Operations Research, Springer, vol. 273(1), pages 527-559, February.
    13. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    14. Ríos-Mercado, Roger Z. & Bard, Jonathan F., 2019. "An exact algorithm for designing optimal districts in the collection of waste electric and electronic equipment through an improved reformulation," European Journal of Operational Research, Elsevier, vol. 276(1), pages 259-271.
    15. Antonio Diglio & Stefan Nickel & Francisco Saldanha-da-Gama, 2020. "Towards a stochastic programming modeling framework for districting," Annals of Operations Research, Springer, vol. 292(1), pages 249-285, September.
    16. Alexander Butsch & Jörg Kalcsics & Gilbert Laporte, 2014. "Districting for Arc Routing," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 809-824, November.
    17. S. W. Hess & J. B. Weaver & H. J. Siegfeldt & J. N. Whelan & P. A. Zitlau, 1965. "Nonpartisan Political Redistricting by Computer," Operations Research, INFORMS, vol. 13(6), pages 998-1006, December.
    18. Nikzad, Erfaneh & Bashiri, Mahdi & Abbasi, Babak, 2021. "A matheuristic algorithm for stochastic home health care planning," European Journal of Operational Research, Elsevier, vol. 288(3), pages 753-774.
    19. Mourão, Maria Cândida & Nunes, Ana Catarina & Prins, Christian, 2009. "Heuristic methods for the sectoring arc routing problem," European Journal of Operational Research, Elsevier, vol. 196(3), pages 856-868, August.
    20. Bender, Matthias & Kalcsics, Jörg & Meyer, Anne, 2020. "Districting for parcel delivery services – A two-Stage solution approach and a real-World case study," Omega, Elsevier, vol. 96(C).
    21. B. K. Pagnoncelli & S. Ahmed & A. Shapiro, 2009. "Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications," Journal of Optimization Theory and Applications, Springer, vol. 142(2), pages 399-416, August.
    22. F Caro & T Shirabe & M Guignard & A Weintraub, 2004. "School redistricting: embedding GIS tools with integer programming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(8), pages 836-849, August.
    23. Bender, Matthias & Kalcsics, Jörg & Nickel, Stefan & Pouls, Martin, 2018. "A branch-and-price algorithm for the scheduling of customer visits in the context of multi-period service territory design," European Journal of Operational Research, Elsevier, vol. 269(1), pages 382-396.
    24. Diglio, Antonio & Peiró, Juanjo & Piccolo, Carmela & Saldanha-da-Gama, Francisco, 2021. "Solutions for districting problems with chance-constrained balancing requirements," Omega, Elsevier, vol. 103(C).
    25. Santoso, Tjendera & Ahmed, Shabbir & Goetschalckx, Marc & Shapiro, Alexander, 2005. "A stochastic programming approach for supply chain network design under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 96-115, November.
    26. Juan, Angel A. & Faulin, Javier & Grasman, Scott E. & Rabe, Markus & Figueira, Gonçalo, 2015. "A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems," Operations Research Perspectives, Elsevier, vol. 2(C), pages 62-72.
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