Call for papers

Mauricio G. C. Resende, Ph.D. (UNIFESP-ITA PPG-PO and University of Washington) – S.J. dos Campos, Brazil and Seattle, USA

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Short Bio: Mauricio G. C. Resende is and an Affiliate Professor of Industrial and Systems Engineering at the University of Washington and a Senior Professor of the Graduate Program in Operations Research of UNIFESP-ITA.

He grew up in Rio de Janeiro (BR), West Lafayette (IN-US), and Amherst (MA-US). He did his undergraduate training in electrical engineering (systems engineering concentration) at the Pontifical Catholic U. of Rio de Janeiro.  He obtained an MS in operations research from Georgia Tech and a PhD in operations research from the U. of California, Berkeley.

He is most known for his work with metaheuristics, in particular GRASP and biased random-key genetic algorithms, as well as for his work with interior point methods for linear programming and network flows.  Dr. Resende has published over 200 papers on optimization and holds 15 U.S. patents.  He edited the Handbook of Heuristics (Springer, 2018), the Handbook of Optimization in Telecommunications (Springer, 2006), the Handbook of Massive Data Sets (Kluwer, 2002), and the Handbook of Applied Optimization (Oxford, 2002), and is coauthor of the book Optimization by GRASP (Springer, 2016). He has a Google Scholar h-index of 87 (over 33,500 Google Scholar citations) and is on the List of Top 2% Scientists of the World (2020-23). He sits on editorial boards of several optimization journals.

He worked at Amazon Research as a Principal Research Scientist in the logistics area from 2014 to 2022. Prior to Amazon, Resende was a Lead Inventive Scientist at the Mathematical Foundations of Computing Department of AT&T Bell Labs and at the Algorithms and Optimization Research Department of AT&T Labs Research from 1988 to 2014. Since 2016, he is Affiliate Professor of Industrial and Systems Engineering at the University of Washington in Seattle and since 1994, a Permanent Member of the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) at Rutgers University as well as a Senior Professor in the UNIFESP-ITA graduate program in operations research since 2023.  He is a 2016 Fellow of INFORMS and was awarded the 2017 Constantine Carathéodory Prize.

TITLE: RANDOM-KEY OPTIMIZERS (RKO): PROBLEM INDEPENDENT COMBINATORIAL OPTIMIZATION

ABSTRACT: This talk introduces Random-Key Optimizers (RKO), a problem-independent approach for solving combinatorial optimization problems. Random keys are randomly generated real numbers in the interval (0,1]. A random-key vector is a vector of n random keys and corresponds to a point in n-dimensional unit hypercube. Solutions of combinatorial optimization problems can be encoded as random-key vectors. By using a decoder, a solution can be retrieved from a random-key vector. For example, by sorting a random-key vector, a permutation will result from the indices of the sorted vector. In a TSP these indices correspond to the cities in a tour. In the random-key optimization problem we seek a point in the n-dimensional unit hypercube that optimizes the decoder function. For a TSP, what is the point in the n-dimensional unit hypercube that decoded with the sorting decoder results in the shortest tour on the n cities. We illustrate this concept describing several decoders for different combinational optimization problems, and by presenting several RKOs, including a hybrid system that combines several RKOs into one intelligent system.