Suresh P. Sethi

Suresh P. Sethi is Eugene McDermott Chair Professor of Operations Management and Director of the Center for Intelligent Supply Networks (C4ISN) at The University of Texas at Dallas.[1] He has contributed significantly in the fields of manufacturing and operations management, finance and economics, marketing, industrial engineering, operations research, and optimal control.[2][3] He is well known for his developments of the Sethi advertising model and DNSS Points, and for his textbook on optimal control.[4]

Suresh P. Sethi
Alma materCarnegie Mellon University
Washington State University
IIT Bombay
AwardsSIAM Fellow (2009)
IIT Bombay Distinguished Alum (2008)
POMS Fellow (2005)
POMS Wickham-Skinner Best Paper Award (2004)
INFORMS Fellow (2003)
AAAS Fellow (2003)
IEEE Fellow (2001)
New York Academy of Sciences Fellow (1999)
Canadian Operational Research Society Award of Merit (1996)
Royal Society of Canada Fellow (1994)
Scientific career
FieldsOperations Management
Optimal control
InstitutionsUniversity of Texas at Dallas
Doctoral advisorGerald L. Thompson

He has received several prestigious honors and awards for his contributions. Two conferences have been organized in his honor: in Aix en Provence in 2005[5] and at UT Dallas in 2006[6] with Harry M. Markowitz, a 1990 Nobel Laureate in Economics, as the keynote speaker. Also, two books have been edited in his honor.[7][8]

His past and present editorial positions include Departmental Editor of Production and Operations Management, Corresponding Editor of SIAM Journal on Control and Optimization, and Associate Editor of Operations Research, M&SOM, and Automatica.

Education

Suresh Sethi received his Ph.D. in Operations Research from Carnegie Mellon University and was Post-doctoral Fellow at Stanford University under the supervision of George B. Dantzig. He has B. Tech. with Honors in Mechanical Engineering from Indian Institute of Technology Bombay, M.S. in Industrial Administration from Carnegie Mellon University, and MBA from Washington State University.[1]

Academic career

Sethi is Eugene McDermott Chair Professor of Operations Management and Director of the Center for Intelligent Supply Networks (C4ISN) at The University of Texas at Dallas. He has also taught at Rice University, University of Toronto, and Carnegie Mellon University. At University of Toronto, he was General Motors Research Professor (1988–92) and Connaught Senior Research Fellow (1984–85).[1]

Honors and awards
  • Alumni Achievement Award, Tepper School, Carnegie Mellon University (2015)
  • President, Production and Operations Management Society (2012)
  • Honored by POMS in its journal POM(2011)
  • Charter Fellow of the Society for Industrial and Applied Mathematics (2009)
  • Distinguished Alum of IIT Bombay (2008)
  • Conferences organized in his honor in 2005 and 2006[5][6]
  • Books edited in his honor in 2005 and 2006[7][8]
  • Fellow of Production and Operations Management Society (2005)
  • Wickham-Skinner Best Paper Award at The 2nd World Conference on POM, 15th Annual Production and Operations Management Conference held in Cancun, Mexico (2004)
  • INFORMS Fellow (2003), AAAS Fellow (2003)
  • IEEE Fellow (2002)
  • Outstanding Contribution in Education, Greater Dallas Indo-American Chamber of Commerce (2001)
  • Fellow of the IC² Institute at the University of Texas at Austin (2000)
  • Fellow of the New York Academy of Sciences (1999)
  • C.Y. O’Connor Fellow, Curtin University, Perth, Australia (1998)
  • Award of Merit, Canadian Operational Research Society (1996)
  • Fellow of the Royal Society of Canada (1994)
  • Listed in Canadian Who's Who, Marquis Who's Who in the World and Marquis Who's Who in the America
  • Erdős Number 3: List of people by Erdős number[9][10][11]

Selected contributions

Sethi advertising model

The Sethi advertising model or simply the Sethi model was developed by Suresh P. Sethi in 1981, and it describes the process of how sales evolve over time in response to advertising. The rate of change in sales depend on three effects: response to advertising that acts positively on the unsold portion of the market, the loss due to forgetting or possibly due to competitive factors that act negatively on the sold portion of the market, and a random effect that can go either way. The model and some extensions have been empirically tested and are widely used in the marketing literature.

DNSS points

DNSS points arise in optimal control problems that exhibit multiple optimal solutions. A DNSS point is an indifference point in an optimal control problem such that starting from such a point, the problem has more than one different optimal solutions. Suresh P. Sethi identified such indifference points for the first time in 1977.

Hierarchical manufacturing systems

Most manufacturing systems are large, complex, and subject to uncertainty. The problem of the efficient management of such systems is of critical importance to a nation's economic competitiveness. However, obtaining optimal feedback policies to run these systems is usually impossible. Hierarchical feedback control policies, on the other hand, offer the promise of being able to handle realistically complex manufacturing systems in a tractable fashion to make their management more efficient. Suresh Sethi and his co-authors have articulated a profound theory that shows that hierarchical decision making in the context of a goal-seeking manufacturing system can lead to near optimization of its objective. They consider manufacturing systems in which events occur at different time scales. In such systems, longer term decisions such as those dealing with capital expansion can be based on the average existing production capacity and can be expected to be nearly optimal even though the shorter term capacity fluctuations are ignored. Having the long-term decisions in hand, one can then solve the simpler problem of obtaining production rates. Multilevel decisions constructed in this manner are shown to be asymptotically optimal as the average time between successive short-term events becomes much smaller than that between successive long-term events. Much attention is given to establish that the order of deviation of the cost of the hierarchical solution from the optimal cost is small. The striking novelty of their approach is that this is done without the insurmountable task of solving for the optimal solution. The approach represents a new paradigm in convex production planning and a new research direction in control theory. The research presented cuts across the disciplines of Operations Management, Operations Research, System and Control Theory, Industrial Engineering, Probability and Statistic, and Applied Mathematics. The research is detailed in two books.[12][13]

Inventory problems with incomplete information

For the first time since the beginning of the inventory theory nearly hundred years ago, Sethi and co-authors have extended the filtering theory in the electrical engineering literature to treat inventory models with incomplete information.[14][15][16] It is shown that ignoring this realistic feature comes at a significant cost. Also, the existence of optimal feedback ordering policies is proved and these policies are partially characterized.

Inventory problems with Markovian demand and forecast updates

Sethi and co-authors have made sustained contributions to the study of inventory problems with Markovian demands with discounted as well as average-cost criteria. Also, they have generalized the standard assumptions to include unbounded demands and cost functions having polynomial growth. Their work is detailed in a book titled Markovian Demand Inventory Models.[17]

Sethi and co-authors have studied the optimality of base stock and type policies in case of forecast updates and multiple delivery modes. They introduce a general forecast updating scheme, termed peeling layers of an onion, and show the optimality of forecast-dependent base stock and policies with two delivery modes. They show further that the base stock policy is no longer optimal for other than the first two consecutive modes. These results are collected in a 2005 book by Sethi, Yan, and Zhang titled Inventory and Supply Chain Management with Forecast Updates.[18]

Forecast and decision horizons

In 1978, Sethi began to look into the fundamental problem of how long-term planning influences immediate decisions. His work (with C. Bes)[19] on decision and forecast horizons has provided a logical framework for the practice of finite horizon assumptions and the choice of horizon. This framework has been widely adopted by researchers in the area.

Supply chain contracts with risk-averse agents

Much of the research in operations management assumes that the agents in supply chain are expected profit maximizers. However, it is well known in the finance literature that individuals are usually risk averse. Gan and Sethi[20] generalized the existing supply contracts to allow for the agents to be risk averse. They developed a definition of coordination in this case, based on the Nash Bargaining Solution, and obtain coordinating contracts in a variety of supply chains with agents observing different risk-averse objectives.

Flexibility in manufacturing

Developed the widely used framework of “Flexibility in Manufacturing”.[21]

Scheduling in robotic cells

Optimum operations in robotic cells: scheduling of parts and re-sequencing of robot moves.[22][23]

Investment/consumption problems with bankruptcy

The problem of optimal consumption and investment is concerned with the decisions of a single agent endowed with some initial wealth who seeks to maximize total expected discounted utility of consumption. The decisions are the rate of consumption and the allocation of their wealth directed to risky and risk-free investments over time. The problem was first studied by Paul Samuelson and Robert Merton in 1969 and 1971; however none of their formulations took into account the possibility that an agent might go bankrupt in the process. In a set of articles published during the period from 1979 to 1996, Suresh Sethi and various co-authors explicitly introduced a bankruptcy value/penalty in the consumption/investment model. They also introduced a nonzero subsistence consumption level, which makes the consideration of bankruptcy even more important. This provided the ability to deal mathematically with the problems of bankruptcy in the study of consumption and investment. This research provides a useful frame for deepening our understanding of the consumption and portfolio selection behavior of individuals and households, and it is collected in a book titled Optimal Consumption and Investment with Bankruptcy.[24]

Optimal control formulations and solutions of a variety of dynamic operations management and economics problems

Suresh Sethi is the key figure in the development and use of optimal control theory to address dynamic and stochastic problems in management science. Sethi wrote his 1972 doctoral thesis on optimal control and its applications. Sethi extended the theory to deal with the special characteristics of management problems, such as the nonnegativity constraints and time lags. His thesis and the subsequent work eventually led to the classic 1981 Sethi-Thompson book[25] that brought the theory of optimal control to management schools. The second edition (505 pages)[4] of this classic text became available in Fall 2000. Central to the book is its extraordinarily wide range of optimal control theory applications. These cover finance, production and inventory problems, marketing, machine maintenance and replacement, optimal consumption of natural resources (renewable or exhaustible), and a number of applications to economics.

Causality detection in multivariate stochastic processes

Caines, Keng and Sethi[26] developed the theory of multivariable causality in the time series analysis, and also applied it to study the determinants of the sales in the Toronto supermarkets. Prior to this, causality studies were used to detect whether a variable such as advertising causes sales over time or whether advertising expenditures are in practice determined as some percentage of sales. The authors extended the concept of causality when more than two variables are involved.

Optimal Economic Growth with Value of Population and Genuine Savings

Arrow et al.[27] study a model of economic growth with population growing in an arbitrary manner. This requires population as well as capital as state variables. In a later paper,[28] they consider population dynamics to be endogenously determined and derive the expression of genuine savings and evaluate the sustainability of the economic system.

References
  1. Homepage of Suresh P. Sethi
  2. "Suresh Sethi," Production and Operations Management, 20(6), 2011, xi-xii.
  3. Google Citations.
  4. Sethi, S.P. and Thompson, G.L., Optimal Control Theory: Applications to Management Science and Economics, Second Edition, Springer, 2000. ISBN 0-387-28092-8 and ISBN 0-7923-8608-6. Slides are available at http://www.utdallas.edu/~sethi/OPRE7320presentation.html
  5. Optimal Control and Dynamic Games: Workshop in Honor of Suresh Sethi, Aix en Provence, France, June 2–6, 2005.
  6. International Conference on Management Sciences: Optimization Models and Applications in Honor of Professor Suresh Sethi, University of Texas at Dallas, Richardson, TX, May 20–22, 2006.
  7. M1 Stochastic Processes, Optimization, and Control Theory: Applications in Financial Engineering, Queueing Networks, and Manufacturing Systems: A Volume in Honor of Suresh Sethi; Series: International Series in Operations Research & Management Science, Vol. 94, H. Yan, G. Yin, and Q. Zhang (Eds.), Springer, 2006. (360 pages – ISBN 978-0-387-33770-8)
  8. Optimal Control and Dynamic Games, Applications in Finance, Management Science and Economics, Series: Advances in Computational Management Science, Vol. 7, C. Deissenberg and R.F. Hartl (Eds.), Springer, Netherlands, 2005. (344 pages – ISBN 978-0-387-25804-1)
  9. Dantzig, G. B.; Sethi, S. P. Linear optimal control problems and generalized linear programs J. Oper. Res. Soc. 32 (1981), no. 6, 467--476
  10. Dantzig, G. B.; Hoffman, A. J.; Hu, T. C. Triangulations (tilings) and certain block triangular matrices Math. Programming 31 (1985), no. 1, 1--14
  11. Erdős, Paul; Fajtlowicz, Siemion; Hoffman, Alan J. Maximum degree in graphs of diameter $2$. Networks 10 (1980), no. 1, 87--90
  12. Sethi, S.P. and Zhang, Q., Hierarchical Decision Making in Stochastic Manufacturing Systems, in series Systems and Control: Foundations and Applications, Birkhäuser Boston, Cambridge, MA, 1994 (419 pages - ISBN 0-8176-3735-4)
  13. Sethi, S.P., Zhang, H., and Zhang, Q., Average-Cost Control of Stochastic Manufacturing Systems, in series Stochastic Modelling and Applied Probability, Springer, New York, NY, 2005 (325 pages - ISBN 0-387-21947-1)
  14. Sethi, S.P., "i3: Incomplete Information Inventory Models," Decision Line, October 2010, 16-19.
  15. Bensoussan, A., Cakanyildirim, M., and Sethi, S.P., "Optimal Ordering Policies for Inventory Problems with Dynamic Information Delays," Production and Operations Management, 16(2) March–April 2007, 241-256.
  16. Bensoussan, A., Cakanyildirim, M., and Sethi, S.P., "Filtering for Discrete-Time Markov Processes and Applications to Inventory Control with Incomplete Information," in Handbook on Nonlinear Filtering, D. Crisan and B. Rozovsky (Eds.), Oxford University Press, 2010, 500-525.
  17. Beyer, D., Cheng, F., Sethi, S.P., and Taksar, M.I., Markovian Demand Inventory Models, Springer, New York, NY, 2010. (253 pages-ISBN 978-0-387-71603-9) .
  18. Sethi, S.P., Yan, H., and Zhang, H., Inventory and Supply Chain Management with Forecast Updates, in series International Series in Operations Research & Management Science, Springer, NY, NY, 2005. (310 pages – ISBN 1-4020-8123-5)
  19. Bes, C. and Sethi, S.P., "Concepts of Forecast and Decision Horizons: Applications to Dynamic Stochastic Optimization Problems," Mathematics of Operations Research, 13(2), May 1988, 295-310.
  20. Gan, X., Sethi, S.P., and Yan, H., "Coordination of a Supply Chain with Risk-Averse Agents," Production and Operations Management, 13(2), 2004, 135-149.
  21. Sethi, A. and Sethi, S.P., "Flexibility in Manufacturing: A Survey," International Journal of Flexible Manufacturing Systems, 2, 1990, 289-328.
  22. Sethi, S.P., Sriskandarajah, C., Sorger, G., Blazewicz, J., and Kubiak, W., "Sequencing of Parts and Robot Moves in a Robotic Cell," International Journal of Flexible Manufacturing Systems, 3/4, 4, 1992, 331-358.
  23. Dawande, M.W., Geismar, H.N., Sethi, S.P., and Sriskandarajah, C., Throughput Optimization in Robotic Cells, in series: International Series in Operations Research & Management Science, Springer, New York, NY, 2007. (430 pages – ISBN 978-0-387-70987-1)
  24. Sethi, S.P., Optimal Consumption and Investment with Bankruptcy, (with a Foreword by Harry M. Markowitz, 1990 Nobel Laureate in Economics) Kluwer Academic Publishers, Norwell, MA, 1997 (428 pages - ISBN 0-7923-9755-X).
  25. Sethi, S.P. and Thompson, G.L., Optimal Control Theory: Applications to Management Science, Martinus Nijhoff, Boston, 1981 (481 pages)
  26. Caines, P., Keng, C.W. and Sethi, S.P., "Causality Analysis and Multivariate Autoregressive Modelling with an Application to Supermarket Sales Analysis," Journal of Economic Dynamics and Control, 3, 1981, 267-298.
  27. Arrow K. J., Bensoussan, A., Feng, Q. and Sethi, S.P., "Optimal savings and the value of population," Proceedings of the National Academy of Sciences (PNAS), 104(47), 2007, 18421-18426.
  28. Arrow, K. J., Bensoussan, A., Feng, Q. and Sethi, S.P., " The Genuine Savings Criterion and the Value of Population in an Economy with Endogenous Population Changes," in Optimal Control of Age-structured Population in Economy, Demography, and the Environment, Series: Environmental Economics, R. Boucekkine, N. Hritonenko, and Y. Yatsenko (Eds.), Routledge, New York, 2011, 20-44. (ISBN 978-0-415-77651-6 and ISBN 978-0-203-84455-7)
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