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Successive Linear Programming at Exxon

Author

Listed:
  • Thomas E. Baker

    (Chesapeake Decision Sciences, Inc., 54 Center Street, Chatham, New Jersey 07928)

  • Leon S. Lasdon

    (Graduate School of Business, University of Texas, Austin, Texas 78712)

Abstract

Successive Linear Programming (SLP) has been used extensively in the refining and petrochemical industries for over 20 years. This paper concentrates on some recent work at Exxon to unify the treatment of nonlinear terms in "mostly linear" models. We first discuss the source of nonlinearities in refining and petrochemical problems and propose a multiplicative formulation for the linearized subproblems to be solved by SLP. We then describe a SLP algorithm which is shown to be related to the concept of trust regions. Finally, we present an example formulation and computational results for a series of large industrial applications.

Suggested Citation

  • Thomas E. Baker & Leon S. Lasdon, 1985. "Successive Linear Programming at Exxon," Management Science, INFORMS, vol. 31(3), pages 264-274, March.
    • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:3:p:264-274
    DOI: 10.1287/mnsc.31.3.264
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    Citations

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    Cited by:

    1. Natashia Boland & Thomas Kalinowski & Fabian Rigterink, 2016. "New multi-commodity flow formulations for the pooling problem," Journal of Global Optimization, Springer, vol. 66(4), pages 669-710, December.
    2. Akshay Gupte & Shabbir Ahmed & Santanu S. Dey & Myun Seok Cheon, 2017. "Relaxations and discretizations for the pooling problem," Journal of Global Optimization, Springer, vol. 67(3), pages 631-669, March.
    3. Lars Hellemo & Asgeir Tomasgard, 2016. "A generalized global optimization formulation of the pooling problem with processing facilities and composite quality constraints," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(2), pages 409-444, July.
    4. Yifu Chen & Christos T. Maravelias, 2020. "Preprocessing algorithm and tightening constraints for multiperiod blend scheduling: cost minimization," Journal of Global Optimization, Springer, vol. 77(3), pages 603-625, July.
    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. Michelle L. Blom & Christina N. Burt & Adrian R. Pearce & Peter J. Stuckey, 2014. "A Decomposition-Based Heuristic for Collaborative Scheduling in a Network of Open-Pit Mines," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 658-676, November.
    7. Chen, Ruoran & Deng, Tianhu & Huang, Simin & Qin, Ruwen, 2015. "Optimal crude oil procurement under fluctuating price in an oil refinery," European Journal of Operational Research, Elsevier, vol. 245(2), pages 438-445.
    8. Charles Audet & Jack Brimberg & Pierre Hansen & Sébastien Le Digabel & Nenad Mladenovi'{c}, 2004. "Pooling Problem: Alternate Formulations and Solution Methods," Management Science, INFORMS, vol. 50(6), pages 761-776, June.
    9. Cheng, Xianliang & Feng, Suzhen & Zheng, Hao & Wang, Jinwen & Liu, Shuangquan, 2022. "A hierarchical model in short-term hydro scheduling with unit commitment and head-dependency," Energy, Elsevier, vol. 251(C).
    10. 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.
    11. Jianzhong Xiao & Xiaolin Wang & Ran Wang, 2016. "Research on Factors Affecting the Optimal Exploitation of Natural Gas Resources in China," Sustainability, MDPI, vol. 8(5), pages 1-13, May.
    12. Foo, Nam & Bloch, Harry & Salim, Ruhul, 2018. "The optimisation rule for investment in mining projects," Resources Policy, Elsevier, vol. 55(C), pages 123-132.
    13. Draman, Murat & Kuban Altinel, I & Bajgoric, Nijaz & Tamer Unal, Ali & Birgoren, Burak, 2002. "A clone-based graphical modeler and mathematical model generator for optimal production planning in process industries," European Journal of Operational Research, Elsevier, vol. 137(3), pages 483-496, March.
    14. Hagspiel, S. & Jägemann, C. & Lindenberger, D. & Brown, T. & Cherevatskiy, S. & Tröster, E., 2014. "Cost-optimal power system extension under flow-based market coupling," Energy, Elsevier, vol. 66(C), pages 654-666.
    15. 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.
    16. Hagspiel, Simeon & Jägemann, Cosima & Lindenberger, Dietmar & Brown, Tom & Cherevatskiy, Stanislav & Tröster, Eckehard, 2013. "Cost-Optimal Power System Extension under Flow-Based Market Coupling," EWI Working Papers 2013-9, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    17. René Caldentey & Susana Mondschein, 2003. "Policy Model for Pollution Control in the Copper Industry, Including a Model for the Sulfuric Acid Market," Operations Research, INFORMS, vol. 51(1), pages 1-16, February.
    18. Santanu S. Dey & Akshay Gupte, 2015. "Analysis of MILP Techniques for the Pooling Problem," Operations Research, INFORMS, vol. 63(2), pages 412-427, April.
    19. Sarker, Ruhul A. & Gunn, Eldon A., 1997. "A simple SLP algorithm for solving a class of nonlinear programs," European Journal of Operational Research, Elsevier, vol. 101(1), pages 140-154, August.
    20. Dag Haugland & Eligius M. T. Hendrix, 2016. "Pooling Problems with Polynomial-Time Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 170(2), pages 591-615, August.

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