PUBLICATIONS
While the semester is coming to a close, we are often surrounded by thoughts of implementation of our methodologies. Two of the chosen publications for this edition, provide some guidance in this direction. These articles provide valuable insight regarding the many facets of successful practice of OR professionals.

We have also chosen a third article, focused on a simulation optimization of a response surface. This article was chosen for inclusion in ORMS Tomorrow as it employs several different OR techniques that many of us will encounter as we move towards the path of graduation...


Lessons for Success in OR/MS Practice Gained from Experiences in Indian and US Steel Plants
GOUTAM DUTTA Indian Institute of Management Ahmedabad,India 380015

Abstract

I worked on modeling integrated steel plants in India (as an analyst)and in the USA (as a doctoral student).The following factors influence the success of OR/MS practice: positioning the OR/MS team in operations, not in research and development; selecting the right problems;emphasizing problem solving, not model building;learning from failures;the right academic and practical training; and experience working on the shopfloor of a factory. Universities should teach OR as research into operations and emphasize the process of implementation.

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Copyright  2000 INFORMS 0092-2102/00/3005/0023/$05.00 1526 –551X electronic ISSN INDUSTRIES —MINING/METALS PROFESSIONAL —OR/MS IMPLEMENTATION
This paper was referee:INTERFACES 30:5 September –October 2000 (pp.23 –30).
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Internal OR Consulting:Effective Practice in a Changing Environment
ROBERT FILDES Department of Management Science Lancaster University Lancaster,United Kingdom LA1 4YX
JOHN RANYARD Department of Management Science Lancaster University

Abstract

Around 1970 some 96 percent of the largest Fortune-500 companies had established OR groups. The recessions of the ’70s and ’80s led many to close their groups. In 1993,the UK OR Society,disturbed by further closures,funded a study to identify factors that influenced the success and survival of internal OR groups. The study team found three categories of factors: changes in the external environment,in the organization,and in the OR group ’s management. This research shows that to be effective, an OR group needs good consulting skills, expert project management, active marketing of its services, and responsiveness to changes in its clients’ requirements and in the surrounding organizational culture.

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Copyright  2000 INFORMS 0092-2102/00/3005/0034/$05.00 1526 –551X electronic ISSN PROFESSIONAL —OR/MS IMPLEMENTATION ORGANIZATIONAL STUDIES —EFFECTIVENESS,PERFORMANCE
This paper was referee:INTERFACES 30:5 September –October 2000 (pp.34 –50).

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A Revised Simplex Search Procedure for Stochastic Simulation Response Surface Optimization
DAVID G. HUMPHREY Nortel Networks, Operations Research Department, Research Triangle Park, NC 27713, humphre@nortelnetworks.com
JAMES R. WILSON Department of Industrial Engineering, North Carolina State University, Raleigh, NC 27695, jwilson@eos.ncsu.edu, http://www.ie.ncsu.edu/jwilson
Abstract
We develop a variant of the Nelder-Mead (NM) simplex search procedure for stochastic simulation optimization that is designed to avoid many of the weaknesses encumbering similar direct-search methods-in particular, excessive sensitivity to starting values, premature termination at a local optimum, lack of robustness against noisy responses, and computational inefficiency. The Revised Simplex Search (RSS) procedure consists of a three-phase application of the NM method in which: (a) the ending values for one phase become the starting values for the next phase; (b) the step size for the initial simplex (respectively, the shrink coefficient) decreases geometrically (respectively, increases linearly) over successive phases; and (c) the final estimated optimum is the best of the ending values for the three phases. To compare RSS versus NM and procedure RS_S9 due to Barton and Ivey, we summarize a simulation study based on four selected performance measures computed for six test problems that include additive white-noise error, with three levels of problem dimensionality and noise variability used in each problem. In the selected test problems, RSS yielded significantly more accurate estimates of the optimum than NM or RS_S9, and both RSS and RS_S9 required roughly four times as many function evaluations as NM. .

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INFORMS Journal on Computing 0899-1499 100 1204-0272 $05.00 Vol. 12, No. 4, Fall 2000 © 2000 INFORMS
 
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Last Updated: March 21, 2001