Which uncertainty is important in multistage stochastic programmes? A case from maritime transportation

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Which uncertainty is important in multistage stochastic programmes? A case from maritime transportation. / Pantuso, Giovanni; Fagerholt, Kjetil; Wallace, Stein W.

In: IMA Journal of Management Mathematics, Vol. 28, No. 1, 2017, p. 5-17.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Pantuso, G, Fagerholt, K & Wallace, SW 2017, 'Which uncertainty is important in multistage stochastic programmes? A case from maritime transportation', IMA Journal of Management Mathematics, vol. 28, no. 1, pp. 5-17. https://doi.org/10.1093/imaman/dpu026

APA

Pantuso, G., Fagerholt, K., & Wallace, S. W. (2017). Which uncertainty is important in multistage stochastic programmes? A case from maritime transportation. IMA Journal of Management Mathematics, 28(1), 5-17. https://doi.org/10.1093/imaman/dpu026

Vancouver

Pantuso G, Fagerholt K, Wallace SW. Which uncertainty is important in multistage stochastic programmes? A case from maritime transportation. IMA Journal of Management Mathematics. 2017;28(1):5-17. https://doi.org/10.1093/imaman/dpu026

Author

Pantuso, Giovanni ; Fagerholt, Kjetil ; Wallace, Stein W. / Which uncertainty is important in multistage stochastic programmes? A case from maritime transportation. In: IMA Journal of Management Mathematics. 2017 ; Vol. 28, No. 1. pp. 5-17.

Bibtex

@article{bf8ddf96316149dd803288ce96549b03,
title = "Which uncertainty is important in multistage stochastic programmes?: A case from maritime transportation",
abstract = "Given that the scope of stochastic programming is to suggest good decisions and not to estimate probability distributions, we demonstrate in this paper how to numerically evaluate which properties of random variables are more important to capture in a stochastic programming model. Such analysis, performed before data collection, can indicate which information should be primarily sought, and which is not critical for the final decision. We apply the analysis to a real-life instance of the maritime fleet renewal. Results show that some properties of the stochastic phenomena, such as the correlation between random variables, have very little influence on the final decision.",
keywords = "Fleet planning, Modelling uncertainty, Stochastic programming",
author = "Giovanni Pantuso and Kjetil Fagerholt and Wallace, {Stein W.}",
year = "2017",
doi = "10.1093/imaman/dpu026",
language = "English",
volume = "28",
pages = "5--17",
journal = "IMA Journal of Management Mathematics",
issn = "1471-678X",
publisher = "Oxford University Press",
number = "1",

}

RIS

TY - JOUR

T1 - Which uncertainty is important in multistage stochastic programmes?

T2 - A case from maritime transportation

AU - Pantuso, Giovanni

AU - Fagerholt, Kjetil

AU - Wallace, Stein W.

PY - 2017

Y1 - 2017

N2 - Given that the scope of stochastic programming is to suggest good decisions and not to estimate probability distributions, we demonstrate in this paper how to numerically evaluate which properties of random variables are more important to capture in a stochastic programming model. Such analysis, performed before data collection, can indicate which information should be primarily sought, and which is not critical for the final decision. We apply the analysis to a real-life instance of the maritime fleet renewal. Results show that some properties of the stochastic phenomena, such as the correlation between random variables, have very little influence on the final decision.

AB - Given that the scope of stochastic programming is to suggest good decisions and not to estimate probability distributions, we demonstrate in this paper how to numerically evaluate which properties of random variables are more important to capture in a stochastic programming model. Such analysis, performed before data collection, can indicate which information should be primarily sought, and which is not critical for the final decision. We apply the analysis to a real-life instance of the maritime fleet renewal. Results show that some properties of the stochastic phenomena, such as the correlation between random variables, have very little influence on the final decision.

KW - Fleet planning

KW - Modelling uncertainty

KW - Stochastic programming

U2 - 10.1093/imaman/dpu026

DO - 10.1093/imaman/dpu026

M3 - Journal article

VL - 28

SP - 5

EP - 17

JO - IMA Journal of Management Mathematics

JF - IMA Journal of Management Mathematics

SN - 1471-678X

IS - 1

ER -

ID: 189768273