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The Evolution of Texaco’s Blending Systems: From OMEGA to StarBlend

Author

Listed:
  • Billy Rigby

    (Texaco Refining and Marketing, Inc., Division of Information Technology Manufacturing, 6464 Savoy Drive, Houston, Texas 77036)

  • Leon S. Lasdon

    (Department of Management Science and Information Systems, College of Business Administration, The University of Texas at Austin, Austin, Texas 78712-1175)

  • Allan D. Waren

    (Computer and Information Science Department, James J. Nance College of Business Administration, Cleveland State University, Cleveland, Ohio 44115)

Abstract

Gasoline blending is a critical refinery operation. In the 1980s, Texaco developed OMEGA, an optimization-based decision support system, to assist in planning and scheduling its blending operations. OMEGA ran primarily on minicomputers and on a mainframe computer system. In 1990, Texaco began replacing OMEGA with StarBlend, a Microsoft Windows application, running on personal computers and client-server networks. StarBlend uses a multi-period extension of OMEGA’s blending model written in the GAMS modeling language and extracts model data from relational databases. Its improved features make it easier to use and it enables blenders and blend planners to more readily incorporate future requirements into current blending decisions. Currently, StarBlend is operational in all Texaco refineries. As federal and state regulations on auto emissions become more complex, StarBlend’s importance will continue to increase.

Suggested Citation

  • Billy Rigby & Leon S. Lasdon & Allan D. Waren, 1995. "The Evolution of Texaco’s Blending Systems: From OMEGA to StarBlend," Interfaces, INFORMS, vol. 25(5), pages 64-83, October.
    • Handle: RePEc:inm:orinte:v:25:y:1995:i:5:p:64-83
    DOI: 10.1287/inte.25.5.64
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    Citations

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    Cited by:

    1. S Eom & E Kim, 2006. "A survey of decision support system applications (1995–2001)," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(11), pages 1264-1278, November.
    2. Uday S. Karmarkar & Kumar Rajaram, 2001. "Grade Selection and Blending to Optimize Cost and Quality," Operations Research, INFORMS, vol. 49(2), pages 271-280, April.
    3. Radu Baltean-Lugojan & Ruth Misener, 2018. "Piecewise parametric structure in the pooling problem: from sparse strongly-polynomial solutions to NP-hardness," Journal of Global Optimization, Springer, vol. 71(4), pages 655-690, August.
    4. Dimitri J. Papageorgiou & Alejandro Toriello & George L. Nemhauser & Martin W. P. Savelsbergh, 2012. "Fixed-Charge Transportation with Product Blending," Transportation Science, INFORMS, vol. 46(2), pages 281-295, May.
    5. Mohammed Alfaki & Dag Haugland, 2014. "A cost minimization heuristic for the pooling problem," Annals of Operations Research, Springer, vol. 222(1), pages 73-87, November.
    6. Natashia Boland & Thomas Kalinowski & Fabian Rigterink, 2017. "A polynomially solvable case of the pooling problem," Journal of Global Optimization, Springer, vol. 67(3), pages 621-630, March.
    7. Ted Kutz & Mark Davis & Robert Creek & Nick Kenaston & Craig Stenstrom & Margery Connor, 2014. "Optimizing Chevron’s Refineries," Interfaces, INFORMS, vol. 44(1), pages 39-54, February.

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