A node formulation for multistage stochastic programs with endogenous uncertainty

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A node formulation for multistage stochastic programs with endogenous uncertainty. / Pantuso, Giovanni.

I: Computational Management Science, Bind 18, Nr. 3, 2021, s. 325 - 354.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Pantuso, G 2021, 'A node formulation for multistage stochastic programs with endogenous uncertainty', Computational Management Science, bind 18, nr. 3, s. 325 - 354. https://doi.org/10.1007/s10287-021-00390-z

APA

Pantuso, G. (2021). A node formulation for multistage stochastic programs with endogenous uncertainty. Computational Management Science, 18(3), 325 - 354. https://doi.org/10.1007/s10287-021-00390-z

Vancouver

Pantuso G. A node formulation for multistage stochastic programs with endogenous uncertainty. Computational Management Science. 2021;18(3):325 - 354. https://doi.org/10.1007/s10287-021-00390-z

Author

Pantuso, Giovanni. / A node formulation for multistage stochastic programs with endogenous uncertainty. I: Computational Management Science. 2021 ; Bind 18, Nr. 3. s. 325 - 354.

Bibtex

@article{cc286158f7ea4fbdb018bab541e83af7,
title = "A node formulation for multistage stochastic programs with endogenous uncertainty",
abstract = "This paper introduces a node formulation for multistage stochastic programs with endogenous (i.e., decision-dependent) uncertainty. Problems with such structure arise when the choices of the decision maker determine a change in the likelihood of future random events. The node formulation avoids an explicit statement of non-anticipativity constraints and, as such, keeps the dimension of the model sizeable. An exact solution algorithm for a special case is introduced and tested on a case study. Results show that the algorithm outperforms a commercial solver as the size of the instances increases.",
author = "Giovanni Pantuso",
year = "2021",
doi = "10.1007/s10287-021-00390-z",
language = "English",
volume = "18",
pages = "325 -- 354",
journal = "Computational Management Science",
issn = "1619-697X",
publisher = "Springer",
number = "3",

}

RIS

TY - JOUR

T1 - A node formulation for multistage stochastic programs with endogenous uncertainty

AU - Pantuso, Giovanni

PY - 2021

Y1 - 2021

N2 - This paper introduces a node formulation for multistage stochastic programs with endogenous (i.e., decision-dependent) uncertainty. Problems with such structure arise when the choices of the decision maker determine a change in the likelihood of future random events. The node formulation avoids an explicit statement of non-anticipativity constraints and, as such, keeps the dimension of the model sizeable. An exact solution algorithm for a special case is introduced and tested on a case study. Results show that the algorithm outperforms a commercial solver as the size of the instances increases.

AB - This paper introduces a node formulation for multistage stochastic programs with endogenous (i.e., decision-dependent) uncertainty. Problems with such structure arise when the choices of the decision maker determine a change in the likelihood of future random events. The node formulation avoids an explicit statement of non-anticipativity constraints and, as such, keeps the dimension of the model sizeable. An exact solution algorithm for a special case is introduced and tested on a case study. Results show that the algorithm outperforms a commercial solver as the size of the instances increases.

UR - http://www.scopus.com/inward/record.url?scp=85103563837&partnerID=8YFLogxK

U2 - 10.1007/s10287-021-00390-z

DO - 10.1007/s10287-021-00390-z

M3 - Journal article

AN - SCOPUS:85103563837

VL - 18

SP - 325

EP - 354

JO - Computational Management Science

JF - Computational Management Science

SN - 1619-697X

IS - 3

ER -

ID: 261614502