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Title: From metaheuristics to learnheuristics: Applications to logistics, finance, and computing
Author: Calvet Liñan, Laura  
Director: Juan Pérez, Ángel Alejandro
Keywords: metaheuristics
combinatorial optimization
statistics
simheuristics
logistics
Issue Date: 12-Jul-2017
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing.
Language: English
URI: http://hdl.handle.net/10609/98646
Appears in Collections:Doctoral Thesis

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