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Robust Satisficing

We present a general framework for robust satisficing that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution deviates from the empirical distribution. The satisficing decision maker specifies an acceptable target, or loss of optimality compared with the empirical optimization model, as a trade-off for the model’s ability to withstand greater uncertainty. We axiomatize the decision criterion associated with robust satisficing, termed as the fragility measure, and present its representation theorem. Focusing on Wasserstein distance measure, we present tractable robust satisficing models for risk-based linear optimization, combinatorial optimization, and linear optimization problems with recourse. Serendipitously, the insights to the approximation of the linear optimization problems with recourse also provide a recipe for approximating solutions for hard stochastic optimization problems without relatively complete recourse. We perform numerical studies on a portfolio optimization problem and a network lot-sizing problem. We show that the solutions to the robust satisficing models are more effective in improving the out-of-sample performance evaluated on a variety of metrics, hence alleviating the optimizer’s curse.

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Informasi Detail

Judul Seri

OPERATIONS RESEARCH; Vol.71 No.1 January-February 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 61-82

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

ROBUST OPTIMIZATION
STOCHASTIC OPTIMIZATION
DATA-DRIVEN
DISCRETE OPTIMIZATION
ROBUST SATISFICING
FRAGILIRY MEASURE

Info Detail Spesifik

https://doi.org/10.1287/opre.2021.2238

Pernyataan Tanggungjawab

Daniel Zhuoyu Long, Melvyn Sim, Minglong Zhou

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