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| Operations researchers | |
|---|---|
| Name | Operations researchers |
| Occupation | "Analysts, scientists, consultants" |
| Known for | "Optimization, decision analysis, modeling" |
Operations researchers Operations researchers are practitioners who apply mathematical modeling, statistical analysis, and computational methods to decision problems in organizations such as RAND Corporation, Bell Labs, Boeing, NASA and General Electric. They draw on techniques from John von Neumann-era game theory, George Dantzig-invented linear programming, Claude Shannon-inspired information theory and Alan Turing-rooted computation to advise leaders in contexts like D-Day, Operation Desert Storm, Apollo program and modern COVID-19 response. Their work intersects with institutions such as Massachusetts Institute of Technology, Stanford University, London School of Economics, INSEAD and companies including Amazon (company), Google, McKinsey & Company.
Operations researchers define problems in organizational settings using models tied to stakeholders such as United States Department of Defense, World Health Organization, International Monetary Fund, European Commission and World Bank. They formulate objectives, constraints and performance metrics influenced by frameworks from Leonid Kantorovich, Vilfredo Pareto-inspired efficiency analysis, Frank Ramsey decision theory, Herbert Simon-bounded rationality and Kenneth Arrow social choice. Typical scopes include supply chains for Walmart, scheduling for Deutsche Bahn, network design for Cisco Systems and forecasting for Goldman Sachs.
The field emerged during World War II with contributions at Bletchley Park, Cambridge University, Harvard University and Columbia University where cryptanalysis, logistics and resource allocation problems were critical. Early milestones include Kálmán filter-related control theory development at MIT and Bell Labs advances in queuing theory by Agner Krarup Erlang and A. K. Erlang-linked work that later influenced teletraffic engineering at AT&T. Postwar diffusion occurred through entities like RAND Corporation, Brookings Institution and Royal Air Force, and through textbooks by George Dantzig, John von Neumann, Harold Hotelling and Richard Bellman.
Practitioners use optimization methods such as linear programming, integer programming, nonlinear programming and dynamic programming originating from George Dantzig, Richard Bellman and John Nash game-theoretic ideas. They apply stochastic models including Markov chains, Poisson process, Brownian motion and Queuing theory tied to work by Agner Krarup Erlang and David G. Kendall. Simulation techniques draw on tools from Monte Carlo method developers like Stanislaw Ulam and Nicolaas Govert de Bruijn, while statistical learning uses methods advanced by Ronald Fisher, Jerzy Neyman, Karl Pearson and modern algorithms from Geoffrey Hinton, Yann LeCun, Yoshua Bengio-inspired deep learning when data-rich problems require predictive modeling.
Operations research methods are applied across sectors such as transportation with FedEx, UPS, Delta Air Lines, and Siemens; healthcare with Mayo Clinic, Johns Hopkins Hospital, Centers for Disease Control and Prevention and National Health Service; finance with J.P. Morgan, Citigroup, BlackRock and Nasdaq; energy with ExxonMobil, Shell plc, National Grid (Great Britain) and BP; and technology with Microsoft, Apple Inc., Facebook, Alibaba Group and IBM. Public sector applications involve agencies like Federal Aviation Administration, United Nations, European Central Bank and Department for Transport (UK).
Training pathways include degree programs at Massachusetts Institute of Technology, Stanford University, University of California, Berkeley, Princeton University and University of Cambridge, with curricula influenced by texts from George Dantzig, Richard Bellman, Donald Knuth and C. R. Rao. Professional certification and practice occur in firms such as McKinsey & Company, Boston Consulting Group, Accenture and through societies like The Institute for Operations Research and the Management Sciences and International Federation of Operational Research Societies. Career roles span titles at Procter & Gamble, Siemens AG, Boeing and Rolls-Royce Holdings.
Leading organizations include The Institute for Operations Research and the Management Sciences, International Federation of Operational Research Societies, Operational Research Society (UK), INFORMS chapters, and university centers at MIT Operations Research Center, Stanford Graduate School of Business and London Business School. Major journals comprise Operations Research (journal), Management Science (journal), European Journal of Operational Research, Journal of the Royal Statistical Society, and Transportation Science.
Prominent contributors include George Dantzig (linear programming simplex), Richard Bellman (dynamic programming), John von Neumann (game theory), Claude Shannon (information theory), Herbert A. Simon (decision theory), Agner Krarup Erlang (queuing theory), Leonid Kantorovich (resource allocation), George Box (experimental design), Ronald A. Fisher (statistics), John Nash (equilibrium theory), Donald Knuth (algorithms), Stanislaw Ulam (Monte Carlo), Harry Markowitz (portfolio theory), Frank Ramsey (decision), Kenneth Arrow (social choice), Harold Hotelling (multivariate analysis), Edward Lorenz (chaos theory), Michael Porter (competitive strategy), C. R. Rao (statistical inference), Jerzy Neyman (hypothesis testing), David Blackwell (probability), David Cox (survival analysis), Edsger Dijkstra (algorithms), David G. Kendall (stochastic processes), Martin Shubik (game theory), Michael J. North (simulation), Peter Drucker (management), W. Edwards Deming (quality), Eliyahu M. Goldratt (theory of constraints), Paul Samuelson (economics), Kenneth Arrow (welfare economics), John Little (queueing), Ralph Gomory (integer programming), Harrison White (network analysis), Thomas Saaty (analytic hierarchy process), Vaughan Pratt (formal methods), Uriel Rothblum (mathematical programming), Margaret Wright (optimization), Ada Lovelace (computing precursor), Alan Turing (computability), Leslie Fox (numerical analysis), Robert Solow (growth theory), Herbert Robbins (stochastic approximation), G. M. Adelson-Velsky (tree algorithms), Stephen Cook (complexity theory), Michael Jordan (computer scientist) (machine learning).