Operations Research

Operations research (OR) is both a profession and an academic discipline. It involves the application of advanced analytical methods to improve executive and management decisions. This survey highlights the types of OR models and techniques in common use. It explores the roots of OR and its theoretical and professional evolution, and presents the current trends which shape its future.

This chapter was originally published in The New Palgrave Dictionary of Economics, 2nd edition, 2008. Edited by Steven N. Durlauf and Lawrence E. Blume

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Operations Research

INFORMS, Analytics, Research and Challenges

Surveys in operations research

Bibliography

• Arrow, K., S. Karlin, and H. Scarf. 1958. Studies in the mathematical theory of inventory and production. Stanford: Stanford University Press. Google Scholar
• Bellman, R. 1957. Dynamic programming. Princeton: Princeton University Press. Google Scholar
• Bixby, R. 2002. Solving real-world linear programs: a decade and more of progress. Operations Research 50: 3–15. ArticleGoogle Scholar
• Bonder, S. 1979. Changing the future of operations research. Operations Research 27: 209–224. ArticleGoogle Scholar
• Charnes, A., and W. Cooper. 1959. Chance-constrained programming. Management Science 6: 73–79. ArticleGoogle Scholar
• Charnes, A., and Cooper, W. 1961. Management models and industrial applications of linear programming, vols. 1 and 2. New York: Wiley. Google Scholar
• Churchman, C. 1979. Paradise regained: A hope for the future of systems design education. In Education in systems science, ed. B. Bayraktar. London: Taylor and Francis. Google Scholar
• Churchman, C., R. Ackoff, and E. Arnoff. 1957. Introduction to operations research. New York: Wiley. Google Scholar
• Cook, S. 1971. The complexity of theorem-proving procedures. Proceedings of the Association for Computing Machinery Annual Symposium on the Theory of Computing 3: 151–158. Google Scholar
• Cornuéjols, G. 2003. The strong perfect graph theorem. Optima: The Mathematical Programming Society Newsletter 70 (June): 2–6. Google Scholar
• Dando, M.R., and R. Sharp. 1978. Operational research in the UK in 1977: The cases and consequences of a myth? Journal of the Operational Research Society 29: 939–949. ArticleGoogle Scholar
• Dantzig, G. 1963. Linear programming and extensions. Princeton: Princeton University Press. BookGoogle Scholar
• Denizel, M., B. Usdiken, and D. Tuncalp. 2003. Drift or shift? Continuity, change, and international variation in knowledge. Operations Research 51: 711–720. ArticleGoogle Scholar
• Fleischer, L. 2000. Recent progress in submodular function minimization. Optima: The Mathematical Programming Society Newsletter 64 (September): 1–11. Google Scholar
• Ford, L. Jr., and D. Fulkerson. 1962. Flows in networks. Princeton: Princeton University Press. Google Scholar
• Geoffrion, A. 1992. Forces, trends and opportunities in MS/OR. Operations Research 40: 423–445. ArticleGoogle Scholar
• Gomory, R. 1958. Essentials of an algorithm for integer solutions to linear programs. Bulletin of the American Mathematical Society 64: 275–278. ArticleGoogle Scholar
• House, A. 1952. The founding meeting of the society. Journal of the Operations Research Society of America 1: 18–32. ArticleGoogle Scholar
• INFORMS (Institute for Operations Research and Management Science). About operations research. Online. Available at http://www.informs.org/index.php? c = 49&kat = + About + Operations + Research&p = 17| . Accessed 22 Aug 2006. Google Scholar
• Kantorovich, L. 1939. Mathematical methods of organising and planning production. Leningrad University [in Russian]. Trans. R. Campbell and W. Marlow, Management Science 6(1960): 366–422. Google Scholar
• Karmarkar, N. 1984. A new polynomial time algorithm for linear programming. Combinatorica 4: 373–395. ArticleGoogle Scholar
• Karp, R. 1972. Reducibility among combinatorial problems. In Complexity of computer computations, ed. R. Miller and J. Thatcher. New York: Plenum Press. Google Scholar
• Khachian, L. 1979. A polynomial algorithm in linear programming. Doklady Akademii Nauk SSSR 224: 1093–1096. Google Scholar
• Khachian, L. 1980. Polynomial algorithms in linear programming. Zhurnal vychisditel’noi matematiki i matematicheskoi 20: 51–68. Google Scholar
• Kuhn, H., and A. Tucker. 1951. Nonlinear programming. In Proceedings of the second Berkeley symposium on mathematical statistics and probability, ed. J. Neyman. Berkeley: University of California Press. Google Scholar
• Lemke, C. 1954. The dual method of solving the linear programming problem. Naval Research Logistic Quarterly 1: 36–47. ArticleGoogle Scholar
• Lemke, C. 1965. Bimatrix equilibrium points and mathematical programming. Management Science 11: 681–689. ArticleGoogle Scholar
• Little, J. 1986. Research opportunities in the decision and management sciences. Management Science 32 (1): 1–13. ArticleGoogle Scholar
• Miser, H. 1978. The history, nature and use of operations research. In Handbook of operations research, ed. J. Moder and S. Elmaghraby, vol. 1. New York: Van Nostrand Reinhold. Google Scholar
• National Academy of Sciences. 1976. Systems analysis and operations research: A tool for policy and program planning for developing countries. Report of an ad hoc panel. Washington, DC: National Academy of Sciences. Google Scholar
• Pocock, J. 1956. Operations research: A challenge to management. In Operations research. Special report no. 13. New York: American Management Association. Google Scholar
• Quesnay, F. 1759. Tableau économique. In Paris, ed. M. Kuczynski and R. Meek, 3rd ed. London: Macmillan. 1972. Google Scholar
• Raiffa, H. 1968. Decision analysis: Introductory lectures on choices and uncertainty. New York: Addison-Wesley. Google Scholar
• Roundy, R. 1985. Effective integer ratio lot-sizing for one warehouse multi-retailer systems. Management Science 31: 1416–1430. ArticleGoogle Scholar
• Roundy, R. 1986. Effective lot-sizing rule for a multi-product multi stage production/inventory system. Mathematics of Operations Research 11: 699–727. ArticleGoogle Scholar
• The OR Society. What operational research is. Online. Available at http://www.orsoc. org.uk/orshop/(aamlgbmxg1m44xb520nsqw45)/orcontent.aspx?inc = about.htm. Accessed 22 Aug 2006. Google Scholar
• Topkis, D.M. 1998. Supermodularity and complementarity. Princeton: Princeton University Press. Google Scholar
• von Neumann, J. 1937. A model of general economic equilibrium. In Ergebnisse eines mathematischen Kolloquiums 8, ed. K. Menger [in German]. Trans. as ‘A model of general equilibrium’. Review of Economic Studies 13(1945–6): 1–9. Google Scholar
• Walras, L. 1883. Théorie mathématique de la richesse sociale. Lausanne: Corbaz. Google Scholar
• Wolfe, P. 1959. The simplex method for quadratic programming. Econometrica 27: 382–398. ArticleGoogle Scholar

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1. http://link.springer.com/referencework/10.1057/978-1-349-95121-5 Ilan Vertinsky

1. Ilan Vertinsky