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Learning strategies for heuristic search in combinatorial optimization problems

Grant number: 19/22067-6
Support type:Scholarships abroad - Research
Effective date (Start): March 05, 2020
Effective date (End): October 04, 2020
Field of knowledge:Engineering - Production Engineering - Operational Research
Principal researcher:Mariá Cristina Vasconcelos Nascimento Rosset
Grantee:Mariá Cristina Vasconcelos Nascimento Rosset
Host: Jean-François Cordeau
Home Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
Research place: École des Hautes Études Commerciales (HEC Montréal), Canada  
Associated research grant:13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

Deep learning (DL) has been successfully applied to a variety of signal and information processing tasks with outstanding results. It consists of computational models composed by multiple processing layers whose goal is to learn data representation considering multiple levels of abstraction. Several recent attempts to address combinatorial optimization problems (COPs) by deep networks can be observed in the literature. In spite of the efforts, to develop models competitive with state-of-the-art heuristic methods remains a challenge. One of the major problems of DL for COPs, mainly in the end-to-end learning solutions, not yet overcome is the scalability. Therefore, this project proposes incorporating deep learning in a local search-based metaheuristic. For such, a linear-time deep learning strategy based on neighborhoods in graphs will be investigated. The goal is to achieve scalability and obtain competitive results in tackling routing problems. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MAXIMO, VINICIUS R.; NASCIMENTO, V, MARIA C. A hybrid adaptive iterated local search with diversification control to the capacitated vehicle routing problem. European Journal of Operational Research, v. 294, n. 3, p. 1108-1119, NOV 1 2021. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.