Optimizing a Car Patrolling Application by Iterated Local Search-Reference-Cited by-同舟云学术

Optimizing a Car Patrolling Application by Iterated Local Search

Published:2024-07-14 Issue: Volume:34 Page:1201-1209
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Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Corrêa Victor Hugo Vidigal¹²^ORCID,Alves de Queiroz Thiago³^ORCID,Iori Manuel²^ORCID,Dos Santos André Gustavo¹^ORCID,Yagiura Mutsunory⁴^ORCID,Zucchi Giorgio⁵^ORCID

Affiliation:

1. Federal University of Viçosa, Viçosa, Brazil

2. University of Modena and Reggio Emilia, Reggio Emilia, Italy

3. Institute of Mathematics and Technology, Federal University of Catalão, Catalão, Goiás, Brazil

4. University of Nagoya, Nagoya, Japan

5. Coopservice scpa, Reggio Emilia, Italy

Funder

National Recovery and Resilience Plan - Italy

Publisher

ACM

Reference35 articles.

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3. Subhodip Biswas, Fanglan Chen, Andreea Sistrunk, Sathappan Muthiah, Zhiqian Chen, Nathan Self, Chang-Tien Lu, and Naren Ramakrishnan. 2020. Geospatial clustering for balanced and proximal schools. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 13358--13365.

4. Paul S Bradley, Kristin P Bennett, and Ayhan Demiriz. 2000. Constrained k-means clustering. Microsoft Research, Redmond 20 (2000).

5. John Gunnar Carlsson and Erick Delage. 2013. Robust partitioning for stochastic multivehicle routing. Operations research 61, 3 (2013), 727--744.