Multi-start local search procedure for the maximum fire risk insured capital problem

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Abstract

A recently European Commission regulation requires insurance companies to determine the maximum value of insured fire risk policies of all buildings that are partly or fully located within circle of a radius of 200 m. In this work, we present the multi-start local search meta-heuristics that has been developed to solve the real case of an insurance company having more than 400 thousand insured buildings in mainland Portugal. A random sample of the data set was used and the solutions of the meta-heuristic were compared with the optimal solution of a MILP model based on the Maximal Covering Location Problem. The results show the proposed approach to be very efficient and effective in solving the problem.

Original languageEnglish
Title of host publicationCombinatorial Optimization - 5th International Symposium, ISCO 2018, Revised Selected Papers
PublisherSpringer Verlag
Pages219-227
Number of pages9
ISBN (Print)9783319961507
DOIs
Publication statusPublished - 1 Jan 2018
Event5th International Symposium on Combinatorial Optimization, ISCO 2018 - Marrakesh, Morocco
Duration: 11 Apr 201813 Apr 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher Springer Verlag
Volume10856 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Symposium on Combinatorial Optimization, ISCO 2018
CountryMorocco
CityMarrakesh
Period11/04/1813/04/18

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Keywords

  • Continuous location problem
  • Local search
  • Meta-heuristics
  • Solvency II

Cite this

Gomes, M. I., Afonso, L. B., Chibeles-Martins, N., & Fradinho, J. M. (2018). Multi-start local search procedure for the maximum fire risk insured capital problem. In Combinatorial Optimization - 5th International Symposium, ISCO 2018, Revised Selected Papers (pp. 219-227). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10856 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-96151-4_19