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.
In: Computational Management Science, Vol. 18, No. 3, 2021, p. 325 - 354.Research output: Contribution to journal › Journal article › Research › peer-review
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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