Jesus Sanchez-Oro

Universidad Rey Juan Carlos
Madrid, Spain

Associate Professor

Bio

Jesús Sánchez-Oro is an Associate Professor at the Computer Science Department at Universidad Rey Juan Carlos, Madrid, where he serves as one of the senior researchers of the Group for Research on Algorithms For Optimization (GRAFO). Born in Madrid on December 31, 1987, he has established himself as a leading researcher in the field of metaheuristics and combinatorial optimization.

He has published over 39 papers in renowned journals including Information Sciences, Applied Soft Computing, and Computers & Operations Research, among others. Furthermore, he has presented his research in more than 40 national and international conferences. He has also co-authored a significant chapter in the well-known Handbook of Heuristics dedicated to Variable Neighborhood Descent, collaborating with Abraham Duarte, Nenad Mladenovic, and Raca Todosijevic.

His research is focused on applying metaheuristics for solving real-life hard combinatorial optimization problems, with particular expertise in variable neighborhood search methodologies and their practical applications.

Research Focus

Variable Neighborhood Descent (VND): His expertise in VND methodology has led to significant contributions in the Handbook of Heuristics, where he explores local search heuristics that examine multiple neighborhood structures in a deterministic way. His work addresses key challenges in VND development, including neighborhood structure selection, ordering, and change rules during search.

Combinatorial Optimization: He specializes in developing hybrid heuristics for complex optimization problems, including the differential dispersion problem, combining GRASP with sampled greedy construction and variable neighborhood search for local improvement while maintaining elite solution sets.

Multi-objective Optimization: His research extends to multi-objective problems, including work on the multi-objective open vehicle routing problem and the multi-objective traveling salesman-repairman problem with profits.

Social Network Analysis: He has developed metaheuristic approaches based on Iterated Greedy methodology for detecting communities in large social networks, contributing to the intersection of optimization and social network analysis.

Target Set Selection: His recent work includes dynamic path relinking approaches for the Target Set Selection problem, demonstrating ongoing innovation in metaheuristic methodologies.

Education

PhD in Computer Science - Universidad Rey Juan Carlos (2016)
MEng in Computer Vision - Universidad Rey Juan Carlos (2011)
BSc in Computer Science - Universidad Rey Juan Carlos (2010)

Key Publications & Contributions

“Variable Neighborhood Descent” - Chapter in Handbook of Heuristics (Springer, 2018)
“Dynamic Path Relinking for the Target Set Selection problem” - Knowledge-Based Systems
“Iterated Greedy algorithm for performing community detection in social networks” - Future Generation Computer Systems
“The capacitated dispersion problem: an optimization model and a memetic algorithm” - Memetic Computing

Research Applications

Facility Location Problems - Multi-objective approaches to location optimization
Social Network Analysis - Community detection in large networks
Vehicle Routing - Multi-objective open vehicle routing problems
Graph Theory - Vertex separation and dispersion problems
Scheduling - Traveling salesman-repairman problems with profits

Academic Recognition

Research Funding - Supported by Spanish “Ministerio de Economía y Competitividad” and “Comunidad de Madrid” (grants TIN2012-35632-C02 and S2013/ICE-2894)
International Collaboration - Co-authored work with leading researchers including Nenad Mladenovic
GRAFO Leadership - Senior researcher in one of Spain’s leading optimization research groups

Current Research Projects

HOMERO Project - Development of holistic methodology for configuration, comparison, and evaluation of metaheuristics
Advanced Multi-start Methods - Innovative approaches to metaheuristic initialization
Hybrid Optimization - Combining multiple metaheuristic approaches for enhanced performance