IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v90y2020ics0305048317306874.html
   My bibliography  Save this article

Robust goal programming for multi-objective optimization of data-driven problems: A use case for the United States transportation command's liner rate setting problem

Author

Listed:
  • Hanks, Robert W.
  • Lunday, Brian J.
  • Weir, Jeffery D.

Abstract

Robust goal programming (RGP) is a recently developed, powerful new optimization modeling technique that conjoins two widely accepted operations research disciplines: robust optimization (RO) and goal programming (GP). In lieu of applying a probability distribution over possible outcomes, an approach considered by stochastic programming, RO utilizes uncertainty sets to account for data uncertainty. This characteristic of RO is an important attribute because identifying such a probability distribution is challenging, at best. Given this RO context, RGP additionally incorporates GP, traditionally a deterministic procedure, to address optimization problems having multiple objectives. As such, RGP has potential to help address a wide array of data-driven applications, ranging from financial management to engineering design.

Suggested Citation

  • Hanks, Robert W. & Lunday, Brian J. & Weir, Jeffery D., 2020. "Robust goal programming for multi-objective optimization of data-driven problems: A use case for the United States transportation command's liner rate setting problem," Omega, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:jomega:v:90:y:2020:i:c:s0305048317306874
    DOI: 10.1016/j.omega.2018.10.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048317306874
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2018.10.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    2. Hanks, Robert W. & Weir, Jeffery D. & Lunday, Brian J., 2017. "Robust goal programming using different robustness echelons via norm-based and ellipsoidal uncertainty sets," European Journal of Operational Research, Elsevier, vol. 262(2), pages 636-646.
    3. Forkenbrock, David J., 2001. "Comparison of external costs of rail and truck freight transportation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(4), pages 321-337, May.
    4. Ghahtarani, Alireza & Najafi, Amir Abbas, 2013. "Robust goal programming for multi-objective portfolio selection problem," Economic Modelling, Elsevier, vol. 33(C), pages 588-592.
    5. Tsai, Wen-Hsien & Yang, Chih-Hao & Chang, Jui-Chu & Lee, Hsiu-Li, 2014. "An Activity-Based Costing decision model for life cycle assessment in green building projects," European Journal of Operational Research, Elsevier, vol. 238(2), pages 607-619.
    6. Dorota Kuchta, 2011. "A concept of a robust solution of a multicriterial linear programming problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 19(4), pages 605-613, December.
    7. Erick Delage & Yinyu Ye, 2010. "Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems," Operations Research, INFORMS, vol. 58(3), pages 595-612, June.
    8. A. L. Soyster, 1973. "Technical Note—Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming," Operations Research, INFORMS, vol. 21(5), pages 1154-1157, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammadi, Reza & He, Qing & Karwan, Mark, 2021. "Data-driven robust strategies for joint optimization of rail renewal and maintenance planning," Omega, Elsevier, vol. 103(C).
    2. Gilani Larimi, Niloofar & Azhdari, Abolghasem & Ghousi, Rouzbeh & Du, Bo, 2022. "Integrating GIS in reorganizing blood supply network in a robust-stochastic approach by combating disruption damages," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    3. Hosseini-Motlagh, Seyyed-Mahdi & Samani, Mohammad Reza Ghatreh & Shahbazbegian, Vahid, 2020. "Innovative strategy to design a mixed resilient-sustainable electricity supply chain network under uncertainty," Applied Energy, Elsevier, vol. 280(C).
    4. Jones, D.F. & Treloar, R. & Ouelhadj, D. & Glampedakis, A. & Bartmeyer, P., 2024. "Incorporation of poverty principles into goal programming," Omega, Elsevier, vol. 127(C).
    5. Sehgal, Ruchika & Sharma, Amita & Mansini, Renata, 2023. "Worst-case analysis of Omega-VaR ratio optimization model," Omega, Elsevier, vol. 114(C).
    6. Xiang, Xi & Liu, Changchun, 2021. "An expanded robust optimisation approach for the berth allocation problem considering uncertain operation time," Omega, Elsevier, vol. 103(C).
    7. Xiang, Xi & Liu, Changchun, 2021. "An almost robust optimization model for integrated berth allocation and quay crane assignment problem," Omega, Elsevier, vol. 104(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2021. "Robust Portfolio Selection Problems: A Comprehensive Review," Papers 2103.13806, arXiv.org, revised Jan 2022.
    2. Antonio G. Martín & Manuel Díaz-Madroñero & Josefa Mula, 2020. "Master production schedule using robust optimization approaches in an automobile second-tier supplier," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 143-166, March.
    3. Antonio J. Conejo & Nicholas G. Hall & Daniel Zhuoyu Long & Runhao Zhang, 2021. "Robust Capacity Planning for Project Management," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1533-1550, October.
    4. Wang, Fan & Zhang, Chao & Zhang, Hui & Xu, Liang, 2021. "Short-term physician rescheduling model with feature-driven demand for mental disorders outpatients," Omega, Elsevier, vol. 105(C).
    5. Zhi Chen & Melvyn Sim & Peng Xiong, 2020. "Robust Stochastic Optimization Made Easy with RSOME," Management Science, INFORMS, vol. 66(8), pages 3329-3339, August.
    6. Joel Goh & Melvyn Sim, 2011. "Robust Optimization Made Easy with ROME," Operations Research, INFORMS, vol. 59(4), pages 973-985, August.
    7. Erfan Babaee Tirkolaee & Zahra Dashtian & Gerhard-Wilhelm Weber & Hana Tomaskova & Mehdi Soltani & Nasim Sadat Mousavi, 2021. "An Integrated Decision-Making Approach for Green Supplier Selection in an Agri-Food Supply Chain: Threshold of Robustness Worthiness," Mathematics, MDPI, vol. 9(11), pages 1-30, June.
    8. Mardani Najafabadi, Mostafa & Magazzino, Cosimo & Valente, Donatella & Mirzaei, Abbas & Petrosillo, Irene, 2023. "A new interval meta-goal programming for sustainable planning of agricultural water-land use nexus," Ecological Modelling, Elsevier, vol. 484(C).
    9. Panos Xidonas & Ralph Steuer & Christis Hassapis, 2020. "Robust portfolio optimization: a categorized bibliographic review," Annals of Operations Research, Springer, vol. 292(1), pages 533-552, September.
    10. Gabrielli, Paolo & Fürer, Florian & Mavromatidis, Georgios & Mazzotti, Marco, 2019. "Robust and optimal design of multi-energy systems with seasonal storage through uncertainty analysis," Applied Energy, Elsevier, vol. 238(C), pages 1192-1210.
    11. Khodamoradi, T. & Salahi, M. & Najafi, A.R., 2020. "Robust CCMV model with short selling and risk-neutral interest rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    12. Zhi Chen & Melvyn Sim & Huan Xu, 2019. "Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets," Operations Research, INFORMS, vol. 67(5), pages 1328-1344, September.
    13. Hanks, Robert W. & Weir, Jeffery D. & Lunday, Brian J., 2017. "Robust goal programming using different robustness echelons via norm-based and ellipsoidal uncertainty sets," European Journal of Operational Research, Elsevier, vol. 262(2), pages 636-646.
    14. Steffen Rebennack, 2022. "Data-driven stochastic optimization for distributional ambiguity with integrated confidence region," Journal of Global Optimization, Springer, vol. 84(2), pages 255-293, October.
    15. Andrew J. Keith & Darryl K. Ahner, 2021. "A survey of decision making and optimization under uncertainty," Annals of Operations Research, Springer, vol. 300(2), pages 319-353, May.
    16. Zhi Chen & Peng Xiong, 2023. "RSOME in Python: An Open-Source Package for Robust Stochastic Optimization Made Easy," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 717-724, July.
    17. Jang Ho Kim & Woo Chang Kim & Frank J. Fabozzi, 2014. "Recent Developments in Robust Portfolios with a Worst-Case Approach," Journal of Optimization Theory and Applications, Springer, vol. 161(1), pages 103-121, April.
    18. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2022. "Robust portfolio selection problems: a comprehensive review," Operational Research, Springer, vol. 22(4), pages 3203-3264, September.
    19. Huan Xu & Constantine Caramanis & Shie Mannor, 2012. "A Distributional Interpretation of Robust Optimization," Mathematics of Operations Research, INFORMS, vol. 37(1), pages 95-110, February.
    20. Dominic J. Breuer & Shashank Kapadia & Nadia Lahrichi & James C. Benneyan, 2022. "Joint robust optimization of bed capacity, nurse staffing, and care access under uncertainty," Annals of Operations Research, Springer, vol. 312(2), pages 673-689, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:90:y:2020:i:c:s0305048317306874. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.