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A Comparison of Local Search Methods for the Multicriteria Police Districting Problem on Graph

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  • F. Liberatore
  • M. Camacho-Collados

Abstract

In the current economic climate, law enforcement agencies are facing resource shortages. The effective and efficient use of scarce resources is therefore of the utmost importance to provide a high standard public safety service. Optimization models specifically tailored to the necessity of police agencies can help to ameliorate their use. The Multicriteria Police Districting Problem (MC-PDP) on a graph concerns the definition of sound patrolling sectors in a police district. The objective of this problem is to partition a graph into convex and continuous subsets, while ensuring efficiency and workload balance among the subsets. The model was originally formulated in collaboration with the Spanish National Police Corps. We propose for its solution three local search algorithms: a Simple Hill Climbing, a Steepest Descent Hill Climbing, and a Tabu Search. To improve their diversification capabilities, all the algorithms implement a multistart procedure, initialized by randomized greedy solutions. The algorithms are empirically tested on a case study on the Central District of Madrid. Our experiments show that the solutions identified by the novel Tabu Search outperform the other algorithms. Finally, research guidelines for future developments on the MC-PDP are given.

Suggested Citation

  • F. Liberatore & M. Camacho-Collados, 2016. "A Comparison of Local Search Methods for the Multicriteria Police Districting Problem on Graph," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-13, March.
  • Handle: RePEc:hin:jnlmpe:3690474
    DOI: 10.1155/2016/3690474
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    Cited by:

    1. Liberatore, Federico & Camacho-Collados, Miguel & Quijano-Sánchez, Lara, 2023. "Towards social fairness in smart policing: Leveraging territorial, racial, and workload fairness in the police districting problem," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    2. Rasul Kochkarov & Azret Kochkarov, 2022. "Introduction to the Class of Prefractal Graphs," Mathematics, MDPI, vol. 10(14), pages 1-17, July.
    3. Rasul Kochkarov, 2022. "Multicriteria Optimization Problem on Prefractal Graph," Mathematics, MDPI, vol. 10(6), pages 1-17, March.
    4. Shixiang Zhu & He Wang & Yao Xie, 2022. "Data-Driven Optimization for Atlanta Police-Zone Design," Interfaces, INFORMS, vol. 52(5), pages 412-432, September.

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