Division Seminars & Faculty Short Courses

Speaker: Abdel M. Zellou is a PhD candidate in Mineral and Energy Economics at the Colorado School of Mines. His areas of interest include oil and gas, energy economics and commodity price modeling. Abdel has more than 15 year experience in the oil and gas industry on the geoscience side with major and services companies, including Total, Schlumberger and Veritas. He holds a BSc in mechanical engineering from ENSAM, in Paris, a M.Sc. in Petroleum Engineering from New Mexico Tech and a M.Sc. in mineral and energy economics from CSM.

 

January 24 - EH 211 4:00 - 5:15 p.m.
Topic:    An Integer-Programming-Based Approach to the Close-Enough Traveling Salesman Problem. 
Abstract: This talk discusses a variant of the Traveling Salesman Problem known as the Close-Enough Traveling Salesman Problem (CETSP), where the traveler visits a node if it enters a compact neighborhood set of that node. This problem has roots in monitoring a given set of sites by a vehicle, where a site can be monitored if the vehicle passes within a certain radius of the site. We discuss a mixed-integer programming model based on a discretization scheme for the problem. Our approach first develops valid inequalities that enhance the bound and solvability of this formulation. We then provide two alternative formulations, one which yields an improved lower bound on the optimal CETSP tour length, and one which greatly improves the solvability of the original formulation by recasting it as a two-stage problem amenable to decomposition. Computational results demonstrate the effectiveness of the proposed method.
Speaker: Cole Smith is a Professor of Industrial and Systems Engineering at the University of Florida. His research on integer programming and combinatorial optimization has been recognized with various awards, and his record in undergraduate teaching was recognized by the Institute of Industrial Engineers' Operations Research division. Dr. Smith's research group has been supported by various national agencies, including current support by NSF, AFOSR, and DTRA.

 

January 31 -

EH 211 4:00 - 5:15 p.m.
Speaker: Hidemichi Yonezawa is a PhD Candidate in Mineral and Energy Economics. He received his B.S. in Chemical Engineering from Tohoku University, Japan and his M.S. in Engineering and Technology Management from the Colorado School of Mines. Hidemichi's research is focused on carbon-policy simulations using computable general equilibrium models. He is currently involved in the Alcoa research project. Specifically, Hidemichi is investigating the impact of carbon pricing on aluminum production and recycling. Topic:   The suboptimal nature of applying Pigouvian rates as border adjustments
Abstract: We consider a North-South trade model with cross-border environmental damage where the North imports the relatively dirty good. The North sets domestic production taxes according to each industry’s contribution to environmental degradation (Pigouvian taxes), but this exacerbates cross-border damages. It is well understood that a large economy in this situation can use border taxes to mitigate the damage, but a large economy also has an incentive to use trade policy to extract rents (Markusen, 1975). We formulate a model that neutralizes the rent-seeking incentives, through an endogenous transfer, to focus only on environmental incentives. We find that setting the North’s import tariff at the Pigouvian rate is above the optimal, because it indirectly reduces the North’s exports, favoring consumption of the dirty good in the South. Even in the case of full border tax adjustment, where the import tariff is partially canceled out by an export subsidy set at the Pigouvian rate for the export industry, trade is taxed too much. Considering the inherent general equilibrium nature of trade policy, the North’s optimal border adjustment to mitigate the cross-border damage is a net import tariff set below the Pigouvian rate.


March 27 -EH 211 4:00 - 5:15 p.m.
Topic: Introduction to Project Finance
Abstract: Project finance is a structured financing of a cash flowing, stand-alone asset that is designed to achieve mutually acceptable allocation of risks and rewards among a diverse group of project stakeholders. Project assets are separated from project sponsors/owners, typically structured as a bankruptcy-remote, special purpose entity.  Projected project cash flows generally have a high degree of certainty, allowing for relatively high amounts of leverage. Projects that are project financed have a finite life and are not designed to be a “going concern”; examples include power plants, mines, toll roads, airports, oil & gas assets.
Speaker:   Brent Lewis is responsible for energy and infrastructure investment banking at MLV & Co, a full service boutique investment bank. Mr. Lewis has fifteen years of senior-level corporate and project finance experience in utilities, energy and infrastructure, closing on dozens of transactions with an aggregate value in excess of $10 billion. His renewable and traditional energy and power experience includes solar, wind, carbon capture, gas, waste coal and biomass fired power, upstream E & P and ethanol. Mr. Lewis also has extensive experience in infrastructure finance, having advised on and raised capital for several large-scale toll road and airport privatizations domestically and overseas. Mr. Lewis has worked as a senior banker at Marathon Capital, and served as a Managing Director with Cantor Fitzgerald's investment banking group in New York, where he led the firm's global energy and infrastructure investment banking business. Brent received his BA in Economics from the University of Western Ontario and his MBA at the Kellogg Graduate School of Management.


April 10 - EH 211 4:00 - 5:15 p.m.
Topic: Incorporating greenhouse gas emissions externalities into material-specific recycling rates targets; what products should be recycled?
Abstract: Recycling rates of municipal solid waste have grown substantially in the U.S. over the past 30 years, increasing from less than 10% in 1980 to 34% in 2010. The rise in recycling occurred largely as a result of policies that were adopted to address growing concerns over declining landfill capacity and waste externalities. More recently, however, interest in expanding recycling policies to further increase recycling rates has emerged in response to concerns over greenhouse gas emissions externalities from energy-intensive manufacturing processes. Thus, several key questions emerge: what is the socially optimal recycling rate, how different is this rate from the observed level of recycling, and how does this rate vary among materials? This research examines how the existence of upstream production externalities and downstream disposal externalities will affect the optimal recycling rate of consumer goods. The results provide insights to policymakers determining state and municipal-level recycling rate targets. For materials with low quantities of greenhouse gas emissions from production activity - such as newspapers, paperboard, glass, and plastics - little change to the observed recycling rates is justified. By contrast, significant increases in recycling rates are justified for steel, aluminum and office papers, whose production process requirements are more energy, and thus emissions, intensive. Ultimately, the results indicate that governments should avoid policies that encourage recycling of the aggregate waste stream and instead focus on increasing recycling of target materials.
Speaker: Kaylee Acuff is a PhD candidate in Mineral and Energy Economics at the Colorado School of Mines. She received her B.A. in Environmental Studies specializing in Climate and Energy Policy with minors in Geography and Math from the University of Colorado at Boulder in 2004. In 2009, she received her M.S. in Engineering and Technology Management from the Colorado School of Mines. Her research focuses on the economics of recycling policy and in particular, optimal policy design to account for the greenhouse gas emissions reductions benefits provided by recycling.

Topic: Modeling and Solving Real World Optimization Problems - Are Educational, Scienti c and Industrial Needs Compatible ?
Speaker: Josef Kallrath obtained his PhD in astrophysics from Bonn University (Germany) in 1989, after which he joined BASF’s group Scientific Computing in Ludwigshafen, Germany. He is professor at the University of Florida (Gainesville, FL, www.astro.ufl.edu\~kallrath), and solves real-world problems in industry using a broad spectrum of methods in scientific computing, from modeling physical systems to supporting decisions processes by mathematical optimization. He has written review articles on the subject, about 70 research papers in astronomy and applied mathematics, and several books on mixed integer optimization, as well as one on eclipsing binary stars. Josef Kallrath is an experienced consultant and course-instructor with in-depth knowledge related to modeling and optimization systems. Josef Kallrath has been providing consulting services to a wide range of industries, including energy, metals, paper, process, refineries, and telecommunication. He leads the Real World Optimization Working Group of the German Operations Research Society. His current research interests are polylithic modeling approaches to solve large-scale or difficult optimization problems, for instance, by decomposition techniques such as column generation, or hybrid methods.
Abstract: This lecture will transmit a sense of the requirements to solve real world decision or optimization problems using mathematical optimization methods. Among them are advanced modeling skills, familiarity with algebraic modeling languages and the ability to develop tailor-made methods. Proper organization, planning and design of production, storage locations, transportation and scheduling are vital to retain the competitive edge of companies in the global economy. In supply chain optimization large-scale multi-stage production planning problems with several sites, products and periods can be solved as monolithic mixed integer linear programming problems. Other problems such as process design, process synthesis and multi-component blended-ow problems leading to nonlinear or even mixed integer nonlinear models. Many industrial or societal problems are, however, so complicated that they need tailor-made methods. Therefore, we put a focus on the importance of modeling and the development of tailor-made methods. Based on the Greek term monolithos (stone consisting of one single block) we introduce the term polylithic for modeling and solution approaches in which mixed integer or non-convex nonlinear optimization problems are solved by tailor-made methods involving several models and/or algorithmic components, in which the solution of one model is input to another one. This can be exploited to initialize certain variables, or to provide bounds on them (problem-specific preprocessing). Mathematical examples of polylithic approaches are decomposition techniques, or hybrid methods in which constructive heuristics and local search improvement methods are coupled with exact mixed integer programming algorithms. In this talk we present illustrative examples from the paper and metals industries, an energy portfolio problem with an embedded unit commitment problem, and a scheduling problem in the energy industry and demonstrate that modeling and the development of tailor-made methods require a lot of experience. This experience is difficult to obtain in only a few years students spend at the university. In-dustrial problem owner are usually impatient when it comes to the solution of their problems. Difficult problems need time to be analyzed and solved properly. Thus, modeling and solving real world optimization problems is not only an art regarding the mathematical techniques but also a very challenging mission in the triangle conict of nowadays educational, scientic and industrials constraints and interests.