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Multiple criteria districting problems

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  • Fernando Tavares-Pereira
  • José Figueira
  • Vincent Mousseau
  • Bernard Roy

Abstract

Districting problems are of high importance in many different fields. Multiple criteria models seem a more adequate representation of districting problems in real-world situations. Real-life decision situations are by their very nature multidimensional. This paper deals with the problem of partitioning a territory into “homogeneous” zones. Each zone is composed of a set of elementary territorial units. A district map is formed by partitioning the set of elementary units into connected zones without inclusions. When multiple criteria are considered, the problem of enumerating all the efficient solutions for such a model is known as being NP-hard, which is why we decided to avoid using exact methods to solve large-size instances. In this paper, we propose a new method to approximate the Pareto front based on an evolutionary algorithm with local search. The algorithm presents a new solution representation and the crossover/mutation operators. Its main features are the following: it deals with multiple criteria; it allows to solve large-size instances in a reasonable CPU time and generates high quality solutions. The algorithm was applied to a real-world problem, that of the Paris region public transportation. Results will be used for a discussion about the reform of its current pricing system. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Fernando Tavares-Pereira & José Figueira & Vincent Mousseau & Bernard Roy, 2007. "Multiple criteria districting problems," Annals of Operations Research, Springer, vol. 154(1), pages 69-92, October.
    • Handle: RePEc:spr:annopr:v:154:y:2007:i:1:p:69-92:10.1007/s10479-007-0181-5
    DOI: 10.1007/s10479-007-0181-5
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    2. Dilip Datta & Jacek Malczewski & José Rui Figueira, 2012. "Spatial Aggregation and Compactness of Census Areas with a Multiobjective Genetic Algorithm: A Case Study in Canada," Environment and Planning B, , vol. 39(2), pages 376-392, April.
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    4. Douglas M. King & Sheldon H. Jacobson & Edward C. Sewell & Wendy K. Tam Cho, 2012. "Geo-Graphs: An Efficient Model for Enforcing Contiguity and Hole Constraints in Planar Graph Partitioning," Operations Research, INFORMS, vol. 60(5), pages 1213-1228, October.
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    6. Sebastián Moreno & Jordi Pereira & Wilfredo Yushimito, 2020. "A hybrid K-means and integer programming method for commercial territory design: a case study in meat distribution," Annals of Operations Research, Springer, vol. 286(1), pages 87-117, March.
    7. Rui Fragoso & Conceição Rego & Vladimir Bushenkov, 2016. "Clustering of Territorial Areas: A Multi-Criteria Districting Problem," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(2), pages 179-198, December.
    8. Maria da Conceição Rego & Rui Fragoso & Vladimir Bushenkov, 2014. "Clustering of Territorial Areas: A Multi-Criteria Districting Problem," ERSA conference papers ersa14p218, European Regional Science Association.
    9. 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.
    10. Carlos García-Alonso & Leonor Pérez-Naranjo & Juan Fernández-Caballero, 2014. "Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms," Annals of Operations Research, Springer, vol. 219(1), pages 187-202, August.
    11. M. Salazar-Aguilar & Roger Ríos-Mercado & José González-Velarde & Julián Molina, 2012. "Multiobjective scatter search for a commercial territory design problem," Annals of Operations Research, Springer, vol. 199(1), pages 343-360, October.
    12. Steiner, Maria Teresinha Arns & Datta, Dilip & Steiner Neto, Pedro José & Scarpin, Cassius Tadeu & Rui Figueira, José, 2015. "Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil," Omega, Elsevier, vol. 52(C), pages 53-64.

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