Encyclopedia of Operations Research and Management Science

2013 Edition
| Editors: Saul I. Gass, Michael C. Fu

Petroleum Refining

Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-1153-7_1268

Introduction

By many financial and physical measures, the petroleum industry is the world’s largest industry. The industry’s operations comprise a global supply chain that produces, transports, refines, and distributes more than 85 million barrels of oil per day – nearly 5 billion tons per year.

Because of its scale, global scope, and huge capital requirements, the petroleum industry is populated with many large, vertically-integrated companies (many of them national oil companies) with global operations. The industry is highly competitive because it has many participants and because it produces basic commodities (e.g., gasoline, diesel fuel, petrochemical feedstocks, etc.) that are difficult to differentiate by brand. The industry’s huge volume and low margins mean that even small changes in operating costs have important effects on operating results. The petroleum industry is a leader in the development and application of new technology; it develops and applies advanced technologies...

This is a preview of subscription content, log in to check access

References

  1. Baker, T. E. (2000). Petrochemical industry. Encyclopedia of operations research and management science (2nd ed). Kluwer Academic Publishers.Google Scholar
  2. Baker, T. E. (1994). An integrated approach to planning and scheduling. In D. W. T. Rippin (Ed.), Foundations of computer-aided process operations (pp. 237–251). Texas: Austin. CACHE.Google Scholar
  3. Baker, T. E., & Lasdon, L. S. (1985). Successive linear programming at Exxon. Management Science, 31, 264–274.CrossRefGoogle Scholar
  4. Bammi, D. (1990). Northern border pipeline logistics simulation. Interfaces, 20(3), 1–13.CrossRefGoogle Scholar
  5. Beale, E. M. L. (1978). Nonlinear programming using a general mathematical programming system. In H. J. Greenberg (Ed.), Design and implementation of optimization software (pp. 259–279). The Netherlands: Sijthoff and Noordhoff.CrossRefGoogle Scholar
  6. Bodington, C. E. (1995). Planning, scheduling and control integration in the process industries. New York: McGraw-Hill.Google Scholar
  7. Bodington, C. E., & Baker, T. E. (1990). A history of mathematical programming in the petroleum industry. Interfaces, 20(3), 117–127.CrossRefGoogle Scholar
  8. Brown, G. G., et al. (1987). Real-time, wide area dispatch of Mobil tank trucks. Interfaces, 17(1), 107–120.CrossRefGoogle Scholar
  9. Charnes, A., Cooper, W. W., & Mellon, B. (1952). Blending aviation gasoline–a study in programming interdependent activities in an integrated oil company. Econometrica, 20(2), 135–139.CrossRefGoogle Scholar
  10. Council, N. P. (2000). U.S. Petroleum refining: Assuring the adequacy and affordability of cleaner fuels. Washington, DC: National Petroleum Council.Google Scholar
  11. Edgar, T. F., & Himmelblau, D. M. (1988). Optimization of chemical processes. New York: McGraw-Hill.Google Scholar
  12. Findlay, P. L., et al. (1989). Optimization of the daily production rates for an offshore oilfield. Journal of Operational Research Society, 40, 1079–1088.CrossRefGoogle Scholar
  13. Gary, J. H., Handwerk, G. E., & Kaiser, M. J. (2007). Petroleum refining technology and economics. Boca Raton, FL: CRC Press.Google Scholar
  14. Griffith, R. E., & Stewart, R. A. (1961). A nonlinear programming technique for the optimization of continuous processing systems. Management Science, 7, 379–392.CrossRefGoogle Scholar
  15. Guyonnet, P., Grant, F. H., & Bagajewicz, M. J. (2009). Integrated model for refinery planning, oil procuring, and product distribution. Industrial and Engineering Chemistry Research, 48(463–482), 2009.Google Scholar
  16. Hansen, P., et al. (1992). Location and sizing of off-shore platforms for oil exploration. European Journal of Operational Research, 58(2), 202–214.CrossRefGoogle Scholar
  17. Higgins, J. G. (1993). Planning for risk and uncertainty in oil exploration. Long Range Planning, 26(1), 111–122.CrossRefGoogle Scholar
  18. Klingman, D., et al. (1987). The successful deployment of management science throughout citgo petroleum corporation. Interfaces, 17(1), 4–25.CrossRefGoogle Scholar
  19. Lasdon, L. S., & Waren, A. D. (1980). A survey of nonlinear programming applications. Operations Research, 28, 102–1073.CrossRefGoogle Scholar
  20. Main, R. A. (1993). Large recursion models: Practical aspects of recursion techniques. In T. A. Ciriani & R. C. Leachman (Eds.), Optimization in industry. New York: Wiley.Google Scholar
  21. Manne, A. (1958). A linear programming model of the US petroleum refining industry. Econometrica, 26(1), 67–106.CrossRefGoogle Scholar
  22. Maples, R. E. (2000). Petroleum refinery process economics (2nd ed.). Tulsa, Oklahoma: PennWell Corporation.Google Scholar
  23. Miller, D., et al. (1994). A modular system for scheduling chemical plant production. In D. W. T. Rippin (Ed.), Foundations of computer-aided process operations (pp. 355–372). Texas: Austin. CACHE.Google Scholar
  24. Miller, D. (1987). An interactive, computer-aided ship scheduling system. European Journal Operational Research, 32(3), 363–379.CrossRefGoogle Scholar
  25. Palacios-Gomez, F., Lasdon, L., & Enquist, M. (1982). Nonlinear optimization by successive linear programming. Management Science, 28(10), 1106–1120.CrossRefGoogle Scholar
  26. Palmer, K. H., et al. (1984). A model-management framework for mathematical programming. New York: Wiley.Google Scholar
  27. Pawde, M. D., & Singh, S. (2010). “Crude oil cargo selection and time frame of LP optimization,” Petroleum Technology Quarterly, Third Quarter, 2010.Google Scholar
  28. Symonds, G. H. (1955). Linear programming–the solution of refinery problems. New York: Esso Standard Oil Company.Google Scholar
  29. Tucker, M. A. (2001). “LP modeling – past, present, and future,” National Petrochemical and Refiners Association (NPRA) 2001 Computer conference, Paper CC-01-153.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.MathPro Inc.BethesdaUSA