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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

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

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    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).
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    6. Xiang, Xi & Liu, Changchun, 2021. "An almost robust optimization model for integrated berth allocation and quay crane assignment problem," Omega, Elsevier, vol. 104(C).

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