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Optimization and Persistence

Author

Listed:
  • Gerald G. Brown

    (Operations Research Department, Naval Postgraduate School, Monterey, California 93943-5219)

  • Robert F. Dell

    (Operations Research Department, Naval Postgraduate School, Monterey, California 93943-5219)

  • R. Kevin Wood

    (Operations Research Department, Naval Postgraduate School, Monterey, California 93943-5219)

Abstract

Most optimization-based decision support systems are used repeatedly with only modest changes to input data from scenario to scenario. Unfortunately, optimization (mathematical programming) has a well-deserved reputation for amplifying small input changes into drastically different solutions. A previously optimal solution, or a slight variation of one, may still be nearly optimal in a new scenario and managerially preferable to a dramatically different solution that is mathematically optimal. Mathematical programming models can be stated and solved so that they exhibit varying degrees of persistence with respect to previous values of variables, constraints, or even exogenous considerations. We use case studies to highlight how modeling with persistence has improved managerial acceptance and describe how to incorporate persistence as an intrinsic feature of any optimization model.

Suggested Citation

  • Gerald G. Brown & Robert F. Dell & R. Kevin Wood, 1997. "Optimization and Persistence," Interfaces, INFORMS, vol. 27(5), pages 15-37, October.
    • Handle: RePEc:inm:orinte:v:27:y:1997:i:5:p:15-37
    DOI: 10.1287/inte.27.5.15
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    Citations

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    Cited by:

    1. Javier Salmerón & Jeffrey Kline & Greta Spitz Densham, 2011. "Optimizing Schedules for Maritime Humanitarian Cooperative Engagements from a United States Navy Sea Base," Interfaces, INFORMS, vol. 41(3), pages 238-253, June.
    2. Michael R. Miller & Robert J. Alexander & Vincent A. Arbige & Robert F. Dell & Steven R. Kremer & Brian P. McClune & Jane E. Oppenlander & Joshua P. Tomlin, 2017. "Optimal Allocation of Students to Naval Nuclear-Power Training Units," Interfaces, INFORMS, vol. 47(4), pages 320-335, August.
    3. Thengvall, Benjamin G. & Yu, Gang & Bard, Jonathan F., 2001. "Multiple fleet aircraft schedule recovery following hub closures," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(4), pages 289-308, May.
    4. Zhichao Zheng & Karthik Natarajan & Chung-Piaw Teo, 2016. "Least Squares Approximation to the Distribution of Project Completion Times with Gaussian Uncertainty," Operations Research, INFORMS, vol. 64(6), pages 1406-1421, December.
    5. Gerardo Gonzalez & Christopher Richards & Alexandra Newman, 2018. "Optimal Course Scheduling for United States Air Force Academy Cadets," Interfaces, INFORMS, vol. 48(3), pages 217-234, June.
    6. Ahmet Silav & Orhan Karasakal & Esra Karasakal, 2019. "Bi‐objective missile rescheduling for a naval task group with dynamic disruptions," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(7), pages 596-615, October.
    7. Wei Yang & Itır Karaesmen & Pınar Keskinocak & Sridhar Tayur, 2008. "Aircraft and crew scheduling for fractional ownership programs," Annals of Operations Research, Springer, vol. 159(1), pages 415-431, March.
    8. Thierry Petit & Andrew C. Trapp, 2019. "Enriching Solutions to Combinatorial Problems via Solution Engineering," INFORMS Journal on Computing, INFORMS, vol. 31(3), pages 429-444, July.
    9. Andrew G. Clark & Susan Cholette & Ozgur Ozluk, 2011. "UCSF Increases Consumer Value Through Optimal Vendor-Show Scheduling," Interfaces, INFORMS, vol. 41(4), pages 327-337, August.
    10. Sebastian Ruther & Natashia Boland & Faramroze G. Engineer & Ian Evans, 2017. "Integrated Aircraft Routing, Crew Pairing, and Tail Assignment: Branch-and-Price with Many Pricing Problems," Transportation Science, INFORMS, vol. 51(1), pages 177-195, February.
    11. Martinez, Michael A. & Newman, Alexandra M., 2011. "A solution approach for optimizing long- and short-term production scheduling at LKAB's Kiruna mine," European Journal of Operational Research, Elsevier, vol. 211(1), pages 184-197, May.
    12. Alexandra M. Newman & Martin Weiss, 2013. "A Survey of Linear and Mixed-Integer Optimization Tutorials," INFORMS Transactions on Education, INFORMS, vol. 14(1), pages 26-38, September.
    13. Spyros Kontogiorgis & Suresh Acharya, 1999. "US Airways Automates Its Weekend Fleet Assignment," Interfaces, INFORMS, vol. 29(3), pages 52-62, June.
    14. Gerald G. Brown & Walter C. DeGrange & Wilson L. Price & Anton A. Rowe, 2017. "Scheduling combat logistics force replenishments at sea for the US Navy," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(8), pages 677-693, December.
    15. Gerald G. Brown & Robert F. Dell & Alexandra M. Newman, 2004. "Optimizing Military Capital Planning," Interfaces, INFORMS, vol. 34(6), pages 415-425, December.
    16. Gerald G. Brown & W. Matthew Carlyle & Robert C. Harney & Eric M. Skroch & R. Kevin Wood, 2009. "Interdicting a Nuclear-Weapons Project," Operations Research, INFORMS, vol. 57(4), pages 866-877, August.
    17. Marjorie Cone Saur & Kaleigh Starr & Mark Husted & Alexandra M. Newman, 2012. "Scheduling Softball Series in the Rocky Mountain Athletic Conference," Interfaces, INFORMS, vol. 42(3), pages 296-309, June.
    18. Gerald G. Brown & Richard E. Rosenthal, 2008. "Optimization Tradecraft: Hard-Won Insights from Real-World Decision Support," Interfaces, INFORMS, vol. 38(5), pages 356-366, October.
    19. Gerald G. Brown & Robert F. Dell & Ray L. Davis & Richard H. Duff, 2002. "Optimizing Plant-Line Schedules and an Application at Hidden Valley Manufacturing Company," Interfaces, INFORMS, vol. 32(3), pages 1-14, June.
    20. Gerald Brown & Joseph Keegan & Brian Vigus & Kevin Wood, 2001. "The Kellogg Company Optimizes Production, Inventory, and Distribution," Interfaces, INFORMS, vol. 31(6), pages 1-15, December.

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