BNSF is hiring Sr. Operations Research Specialists (multiple positions)

The Operations Research group at BNSF is growing and we would like to invite fresh and experienced Operations Research professionals to be a part of our growth story. Below is a description of the position. You must apply online at www.bnsf.com/careers<http://www.bnsf.com/careers> (search posting reference SeniorResearchSpecialist1).

Please forward this job position if you know someone who is in the job market. If you have any questions, please forward them to Pooja.Dewan@BNSF.com<mailto:Pooja.Dewan@BNSF.com>.

Thanks
Homarjun Agrahari, PhD
Operations Research
BNSF Railway

------------------------------------
Senior Operations Research Specialist


BNSF Railway operates one of the nation's largest rail networks, with approximately 32,000 route miles operating through 28 states across the western United States. BNSF is headquartered in Fort Worth, Texas. For more than 160 years we have proudly served our customers by safely and efficiently delivering commodities such as coal, grain, steel and consumer products. The dedication, talent and creativity of our 38,000 employees have helped distinguish BNSF as an innovative and progressive leader within the transportation industry. To learn more about our company, our culture and our opportunities, please visit us online at www.bnsf.com/careers<http://www.bnsf.com/careers>.


APPLICATION DEADLINE: November 30, 2011 at midnight (CST)

POSITIONS AVAILABLE: multiple
LOCATION: Fort Worth, TX

SALARY BAND 29/30


REPORTS TO: General Director Decision Systems

START DATE: 4th Quarter 2011 (Subject to change)

DUTIES & RESPONSIBILITIES:

As a member of the BNSF Operations Research Group, you will be responsible for finding solutions to some of the many challenging problems facing the railroad.

Duties include:

*Interfacing with internal customers to understand the business and identify opportunities for improvement

*Interfacing with BNSF Technology Services personnel to understand existing data structures and IT processes

*Identifying solution techniques and implementing them independently, with external vendors, through academic alliances, or with BNSF Technology Services teams

*Working with end-users to validate and enhance the tools

*Identifying and initiating new projects part of technology initiatives

*Communicating status and findings to senior management and multiple teams

Extracting and cleaning large volumes of data to derive insights that can be used for process improvements.  Developing models to solve the business problems.

QUALIFICATIONS:

*A Masters or Ph.D. degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science or a related field.  Ph.D. is preferred.  Master's degree with experience will be considered.

*Strong programming skills in an object-oriented programming language, such as Java, C++, or C#

*Excellent written, verbal, and interpersonal communication skills

*Ability to identify underlying problems and appropriate techniques for solving them

*Ability to manipulate and extract information from very large, complex data sets

*Expertise in using commercial solver software, such as CPLEX, Gurobi, or Frontline solver

*Practical experience applying quantitative techniques to solve real-world problems

BACKGROUND INVESTIGATION ELEMENTS:
*Verification of last 7 years of criminal, driving and employment history to include military service.
*Social Security number verification
*Academic and Education verification

DRUG TEST: BNSF is committed to a safe and drug free work place. All new hires are required to undergo a hair drug test which detects the presence of illegal drugs for months prior to testing. We appreciate your cooperation in keeping BNSF safe and drug free.

The duties and responsibilities in this posting are representative categories to be used in deciding whether to apply for the position. These general categories do not necessarily constitute an exhaustive list of duties of the position.
We are proud to be an EEO/AA employer M/F/D/V. We maintain a drug-free workplace and perform pre-employment substance abuse testing.

Transportation Worker Identification Credential (TWIC): Federal authority requires BNSF employees, whose work requires unescorted access to secure areas of port facilities, to obtain a TWIC. A TWIC is a condition of employment for such positions and requires candidates to those positions to submit to a TSA security assessment (to include, but not limited to, providing: biographic information; identity documents; fingerprints; digital photograph). More information is available at www.tsa.gov/twic<http://www.tsa.gov/twic>.

2012 Wilkinson Fellowship in Scientific Computing at Argonne National Laboratory

WILKINSON FELLOWSHIP IN SCIENTIFIC COMPUTING
Mathematics and Computer Science Division
Argonne National Laboratory

The Mathematics and Computer Science (MCS) Division of Argonne National Laboratory invites outstanding candidates to apply for the J. H. Wilkinson Fellowship in Scientific Computing. The appointment is for one year and may be renewed for another year.

This fellowship was created in memory of Dr. James Hardy Wilkinson, F.R.S., who had a close association with the Mathematics and Computer Science Division as a consultant and guiding spirit for the EISPACK and LINPACK projects. The Wilkinson Fellowship is intended to encourage scientists actively engaged in state-of-the-art research in scientific computing. Candidates must have received a recent Ph.D. prior to the beginning of the appointment. The benefits of the appointment include a highly competitive salary, moving expenses, and a generous professional travel allowance. For additional details, including past recipients, see http://www.mcs.anl.gov/research/opportunities/wilkinsonfellow/

The appointment will be in the MCS Division, which has strong programs in scientific computing, software tools, and computational mathematics. Of special interest are algorithms and software for linear algebra, optimization, differential equations, computational differentiation, stochastic systems, and unstructured mesh computations; software tools for parallel computing; and numerical methods for computational science problems. For further information, see http://www.mcs.anl.gov/LANS/ .

Internationally recognized for innovative research in high-performance computing, the MCS Division supports an excellent computational environment that includes large Linux clusters, a distributed systems laboratory, and a virtual environments laboratory. Researchers also have access to a Blue Gene/P supercomputer. For more information, see www.mcs.anl.gov .

Argonne is located in the southwestern Chicago suburbs, offering the advantages of affordable housing, good schools, and easy access to the cultural attractions of the city.

Interested candidates should consult the website http://recruit.mcs.anl.gov/wilkinson for details on how to apply. The application must include a curriculum vitae; statement of research interests; a list of publications, abstracts, and significant presentations; and three letters of recommendation. Applications will be accepted starting August 31, 2011. Applications received before December 15, 2011, are assured maximum consideration. The closing date for applications is January 15, 2012. Application material will be reviewed by a selection committee and a candidate announced in March 2012.

Seminar : Optimal Newton-type Methods for Nonconvex Smooth Optimization

INDUSTRIAL AND SYSTEMS ENGINEERING (ISE) SEMINAR

Speaker:

Coralia Cartis

Assistant Professor

School of Mathematics

University of Edinburgh

Date, Time, Location:

Thursday, October 27, 2011

2:30pm - 3:30pm

Mohler Laboratory, Room 453

Title:

Optimal Newton-type Methods for Nonconvex Smooth Optimization

Abstract:

We show that the steepest-descent and Newton's methods for unconstrained nonconvex optimization under standard assumptions may both require a number of iterations and function evaluations arbitrarily close to the steepest-descent's global worst-case complexity bound. This shows that the latter upper bound is essentially tight for steepest descent and that Newton's method may be as slow as the steepest-descent method in the worst case. Then the cubic regularization of Newton's method (Griewank (1981), Nesterov & Polyak (2006)) is considered and extended to large-scale problems, while preserving the same order of its improved worst-case complexity (by comparison to that of steepest-descent); this improved worst-case bound is also shown to be essentially tight. We further show that the cubic regularization approach is, in fact, optimal from a worst-case complexity point of view amongst a class of second-order methods. The worst-case problem-evaluation complexity of constrained optimization will also be discussed, time permitting. This is joint work with Nick Gould (Rutherford Appleton Laboratory, UK) and Philippe Toint (Namur University, Belgium).

Biography:

Coralia Cartis has been a tenured assistant professor at Edinburgh University in Scotland, United Kingdom since 2007. Previously, she held postdoctoral appointments within the numerical analysis groups at Rutherford Appleton Laboratory and Oxford University. She pursued her PhD research in optimization under the supervision of Prof Mike Powell at Cambridge University (2005).

Coralia's research addresses the development, convergence and complexity analyses and implementation of algorithms for linear and nonlinear nonconvex smooth optimization problems, suitable for large-scale problems. She is also interested in the interconnections between dynamical systems and continuous optimization; and optimization aspects of compressed sensing and sparse approximation.

Diagnosing ill conditioning at CPLEX

http://www-01.ibm.com/support/docview.wss?uid=swg21399993

Seminar : Adaptive and Robust Radiation Therapy

Speaker:

Timothy C. Y. Chan

Assistant Professor

Mechanical & Industrial Engineering

University of Toronto

Date, Time, Location:

Monday, October 17, 2011

3:00pm - 4:00pm

Mohler Laboratory, Room 451

Title:

ARRT: Adaptive and Robust Radiation Therapy

Abstract:

The traditional approach to robust intensity-modulated radiation therapy treatment planning involves creating an appropriate uncertainty set to model the uncertain effect, solving a single treatment planning problem, and then delivering the same treatment over multiple weeks. In this talk, I will present an adaptive robust optimization approach to IMRT optimization, where information gathered in previous treatment sessions is used to update a model of uncertainty and guide treatment plan re-optimization for the next session. Such an approach allows for the estimate of the uncertain effect to improve as the treatment progresses. This approach involves solving a sequence of linear programs, and is therefore highly tractable. I will present computational results for a lung cancer case where the dominant uncertainty is in the patient’s breathing motion. Using this adaptive robust method, I demonstrate that it is possible to attain significant and simultaneous improvement in both tumor coverage and organ sparing over the non-adaptive approach. I also show that it is possible to closely approximate “prescient” solutions, and provide some theoretical insight as to why this occurs.

Biography:

Timothy C. Y. Chan is an Assistant Professor in the department of Mechanical and Industrial Engineering at the University of Toronto.  His primary research interests are in optimization under uncertainty and the application of optimization methods to radiation therapy, health care operations and sustainability.  He received his B.Sc. in Applied Mathematics from the University of British Columbia, and his Ph.D. in Operations Research from the Massachusetts Institute of Technology.  Before coming to Toronto, he was an Associate in the Chicago office of McKinsey and Company, a global management consulting firm.  During that time, he advised leading companies in the fields of medical device technology, travel and hospitality, telecommunications, and energy on issues of strategy, organization, technology and operations.

Seminar : Improved Inventory Targets in the Presence of Limited Historical Demand Data

Speaker:

Bahar Biller

Associate Professor of Operations Management

Tepper School of Business

Carnegie Mellon University

Date, Time, Location:

Friday, October 7, 2011

11:00am - 12:00pm

Mohler Laboratory, Room 453

Abstract:

Most of the literature on inventory management assumes that the demand distribution and the values of its parameters are known with certainty. We consider a repeated newsvendor setting where this is not the case and study the problem of setting inventory targets when there is a limited amount of historical demand data. Consequently, we achieve the following objectives:  (1) to quantify the inaccuracy in the inventory-target estimation as a function of the length of the historical demand data, the critical fractile, and the shape parameters of the demand distribution; and (2) to determine the inventory target that minimizes the expected cost and accounts for the uncertainty around the demand parameters estimated from limited historical data. We achieve these objectives by using the concept of expected total operating cost and representing the demand distribution with the highly flexible Johnson translation system. We further consider the demand data that may be auto-correlated or intermittent as well as the data sets that may contain sales rather than demand realizations. Our procedures require no restrictive assumptions about the first four moments of the demand random variables, and they can be easily implemented in practical settings with reduced expected total operating costs.

Biography:

Bahar Biller is an Associate Professor in the Tepper School of Business at Carnegie Mellon University. Her research focuses on three distinct yet related areas: (1) developing a comprehensive input modeling framework for stochastic system simulations with the ability to represent, fit, and generate multivariate time-series input processes with arbitrary marginal distributions and dependence structures; (2) accounting for the uncertainty of multivariate input-distribution parameters which are estimated from finite historical input data on simulation outputs; and (3) testing and proving the efficacy of the developed methods on a wide range of industrial applications in Operations Management and Finance. She received a National Science Foundation CAREER award in 2006 and the Presidential Early Career Award for Scientists and Engineers in 2007. She is a member of INFORMS and currently the Vice President and President-elect of the INFORMS Simulation Society.

Automated Configuration of MIP solvers

Here is an interesting link about automating configuration  of MIP solvers

http://www.cs.ubc.ca/labs/beta/Projects/MIP-Config/