Biased-randomized algorithms and simheuristics in finance & insurance

• Jana Doering ^[1] ; Armando Nieto ^[1] ; Angel A. Juan ^[2] ; Elena Perez-Bernabeu ^[2]
  1. [1] Universitat Oberta de Catalunya

     Universitat Oberta de Catalunya

     Barcelona, España

     
  2. [2] Universitat Polit`ecnica de Val`encia
• Localización: BEIO, Boletín de Estadística e Investigación Operativa, ISSN 1889-3805, Vol. 38, Nº. 1, 2022
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • Managerial decisions in the area of finance and insurance can often be modeled as combinatorial optimization problems. It is also frequent that these optimization problems fall into the category of NP-hard ones, which justifies the need for using metaheuristic algorithms when tackling large-sized instances. In addition, decision-making in real-life financial & insurance activities is usually performed in scenarios under uncertainty. Hence, stochastic versions of the aforementioned NP-hard problems have to be considered, and simulation-optimization methods are required in order to obtain high-quality solutions. This paper analyzes how biased-randomized techniques (which transform greedy heuristics into probabilistic algorithms) and simheuristics (hybridization of simulation with metaheuristics) can be employed to efficiently cope with a variety of challenging optimization problems, even those under uncertainty scenarios.

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