Mauricio Resende

Mauricio Resende	Amazon Inc, USA
Mauricio G.C. Resende did his undergraduate work in Electrical Engineering at the Pontifical Catholic University of Rio de Janeiro (1978). He obtained an MS in Operations Research from Georgia Tech (1979) and a PhD in Operations Research from the University of California, Berkeley (1987). 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. Mauricio has published over 200 papers on combinatorial optimization and holds 15 U.S. patents. He has edited five handbooks, including the Handbook of Applied Optimization and the Handbook of Optimization in Telecommunications, and is on the editorial boards of several optimization journals, including Networks, Journal of Global Optimization, RAIRO, and International Transactions in Operational Research. Prior to joining Amazon.com in December 2014 as a Principal Research Scientist, Mauricio 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.

Amazon Inc, USA

Mauricio G.C. Resende did his undergraduate work in Electrical Engineering at the Pontifical Catholic University of Rio de Janeiro (1978). He obtained an MS in Operations Research from Georgia Tech (1979) and a PhD in Operations Research from the University of California, Berkeley (1987). 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. Mauricio has published over 200 papers on combinatorial optimization and holds 15 U.S. patents. He has edited five handbooks, including the Handbook of Applied Optimization and the Handbook of Optimization in Telecommunications, and is on the editorial boards of several optimization journals, including Networks, Journal of Global Optimization, RAIRO, and International Transactions in Operational Research. Prior to joining Amazon.com in December 2014 as a Principal Research Scientist, Mauricio 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.

Logistics Optimization at Amazon: Big Data & Operational Research in Action
We consider optimization problems at Amazon Logistics. Amazon.com is the world’s largest e-commerce company, selling millions of units of merchandise worldwide on a typical day. To achieve this complex operation requires the solution of many classical operational research problems. Furthermore, many of these problems are NP-hard, stochastic, and inter-related, contributing to make Amazon Logistics a stimulating environment for research in optimization and algorithms.

Logistics Optimization at Amazon: Big Data & Operational Research in Action

We consider optimization problems at Amazon Logistics. Amazon.com is the world’s largest e-commerce company, selling millions of units of merchandise worldwide on a typical day. To achieve this complex operation requires the solution of many classical operational research problems. Furthermore, many of these problems are NP-hard, stochastic, and inter-related, contributing to make Amazon Logistics a stimulating environment for research in optimization and algorithms.