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

Robert W. Hanks, Brian J. Lunday and Jeffery D. Weir

Omega, 2020, vol. 90, issue C

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.

Keywords: Robust optimization; Goal programming; Robust goal programming; Decision making under risk (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.omega.2018.10.013

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