Kalyanmoy Deb

College of Engineering, Michigan State University,

The visit of Dr. Deb is partly funded by the "Programa de Visitas de Profesores Distinguidos 2014-2015"
of the Mexican Academy of Sciences (AMC) and the United States-Mexico Foundation for Science (FUMEC).


Arturo Hernandez

Arturo Hernández Aguirre

Computer Science Department, Center for Research in Mathematics (CIMAT),Guanajuato 


Leonardo Trujillo

Leonardo Trujillo Reyes

Tree Lab, Instituto Technologico de Tijuana



Honggang Wang

Rudgers University


Francisco Javier Zaragoza Martínez


UAM Azcapotzalco  Mexico


Kalyanmoy Deb,
Koenig Endowed Chair Professor, Michigan State
University, USA

Title: How Useful Theoretical Optimality Conditions Are in Assessing Numerical and Evolutionary Optimization Algorithms?
Theoretical optimality conditions, such as Karush-Kuhn-Tucker (KKT) conditions, are often used to assess whether a solution obtained by a numerical or evolutionary ptimization (NEO) algorithm is truly an optimum or not. However, recent studies have revealed that these conditions cannot provide a consistent evaluation of near-optimal solutions, hence making KKT conditions a `singular' event at the true optimum.  In this invited talk, we argue that the KKT optimality conditions are not amenable to provide any neighborhood property of an optimal solution, thereby causing difficulties in using direct violation of KKT conditions as a measure of convergence property. Thereafter, we develop a KKT proximity metric that is able to provide a measure of closeness of a solution to the true optimal solution, which can be used as a termination condition for both numerical and evolutionary
optimization algorithms in both single and multi-objective optimization applications. Results using standard EO and EMO methods will be presented and their further use will be suggested.

Kalyanmoy Deb is Koenig Endowed Chair Professor at Electrical and Computer Engineering in Michigan State University, USA. Prof. Deb's research interests are in evolutionary optimization and their application in optimization, modeling, and machine learning, particularly in evolutionary multi-criterion optimization (EMO). He was awarded `Infosys Prize', `TWAS Prize' in Engineering Sciences, `CajAstur Mamdani Prize', `Distinguished Alumni Award' from IIT Kharagpur, 'Edgeworth-Pareto' award, and Bhatnagar Prize in Engineering Sciences for his pioneering studies in EMO. He is fellow of IEEE, ASME, and three science academies in India. He has published 370+ research papers with Google Scholar citation of 60,000+ with h-index 83. He is in the editorial board on 18 major international journals. More information about his research contribution can be found from http://www.egr.msu.edu/~kdeb.

Honggang Wang
Rudgers University, USA

Title: "Stochastic Modeling and Optimization for Oil/Gas Field Development Under Uncertainty"

Abstract: In this talk, I will discuss three major research topics: (i) optimal well placement under geological uncertainty. I will introduce a sequential uncertainty sampling and optimization framework, retrospective optimization (RO), for well placement optimization under uncertainty. Embedded in RO, a variety of sampling methods and nonlinear optimizers can be used to enhance modeling and optimization efficiency. Numerical results based on some field cases suggest that RO provides efficient solution techniques for practical problems. (ii) joint
optimization of well placement and well controls. We propose a new numerical optimization method for mixed integer simulation optimization, based on subspace simplex interpolation search. This new approach can effectively handle poor-scaling problems such as joint optimization problems in oil/gas projects. (iii) multi-objective optimization (MOO)
using zigzag search. Zigzag search method is first developed by me and is the current state-of-the-art for continuous MOO problems. Recently I designed the direct zigzag search for integer or mixed integer simulation optimization problems. I apply direct zigzag search to optimal development of carbon dioxide sequestration project cases considering multiple decision criteria, such as secure storage volume and leakage risk. The promising results suggest that this new algorithm outperforms the existing methods significantly.

Honggang Wang is an assistant professor in Industrial and Systems Engineering at Rutgers University. He received his Bachelor of Science degree in Power Engineering from Shanghai Jiao Tong University, Shanghai, China, in 1996, Master of Science in Manufacturing Engineering from University of Missouri-Rolla, in 2004, and Ph.D. in Operations
Research from Purdue University, West Lafayette IN, in 2009. He has worked as a Postdoctoral Scholar in Energy Resources Engineering at Stanford University for two years before he joined Rutgers, NJ in 2011.

Dr. Wang's research and teaching interests lie in system uncertainty modeling and analysis, stochastic optimization, operations research, and their applications in energy production, sustainable energy, and manufacturing systems. Dr. Wang has won IBM faculty award 2012 for his work in oil/gas projects.

Francisco Javier Zaragoza Martínez
UAM Azcapotzalco

Title: Linear programming relaxations of arc routing problems

Abstract: Routing problems are combinatorial optimization problems in which one must traverse a graph at minimum cost following certain restrictions. Perhaps the best studied vertex routing problem is the traveling salesman problem, in which one must find a minimum cost tour of a graph visiting each vertex at least once. On the other hand, it is possible that the best studied arc routing problem is the chinese postman problem, in which one must find a minimum cost tour of a graph traversing each edge at least once. It is no coincidence that both problems have been modelled using integer programming and then solved using their linear programming relaxations. In this talk we will sketch the use of this technique in order to propose good algorithms for some NP hard arc routing problems.