Bibliography#

AOP20

Douglas Alem, Fabricio Oliveira, and Miguel Carrión Ruiz Peinado. A practical assessment of risk-averse approaches in production lot-sizing problems. International Journal of Production Research, 58(9):2581–2603, 2020.

ADEH99

Philippe Artzner, Freddy Delbaen, Jean-Marc Eber, and David Heath. Coherent measures of risk. Mathematical finance, 9(3):203–228, 1999.

BL11

John R Birge and Francois Louveaux. Introduction to stochastic programming. Springer Science & Business Media, 2011.

COS20

Lucas Condeixa, Fabricio Oliveira, and Afzal S Siddiqui. Wasserstein-distance-based temporal clustering for capacity-expansion planning in power systems. In 2020 International Conference on Smart Energy Systems and Technologies (SEST), 1–6. IEEE, 2020.

DOA17

Mary Dillon, Fabricio Oliveira, and Babak Abbasi. A two-stage stochastic programming model for inventory management in the blood supply chain. International Journal of Production Economics, 187:27–41, 2017.

DMP22

Oscar Dowson, David P Morton, and Bernardo K Pagnoncelli. Incorporating convex risk measures into multistage stochastic programming algorithms. Annals of Operations Research, pages 1–25, 2022.

Dupavcova90

Jitka Dupačová. Stability and sensitivity-analysis for stochastic programming. Annals of operations research, 27:115–142, 1990.

DupavcovaGroweKRomisch03

Jitka Dupačová, Nicole Gröwe-Kuska, and Werner Römisch. Scenario reduction in stochastic programming. Mathematical programming, 95:493–511, 2003.

FernandezPerezOH18

Miguel A Fernández Pérez, Fabricio Oliveira, and Silvio Hamacher. Optimizing workover rig fleet sizing and scheduling using deterministic and stochastic programming models. Industrial & engineering chemistry research, 57(22):7544–7554, 2018.

HRomisch03

Holger Heitsch and Werner Römisch. Scenario reduction algorithms in stochastic programming. Computational optimization and applications, 24:187–206, 2003.

HRomisch07

Holger Heitsch and Werner Römisch. A note on scenario reduction for two-stage stochastic programs. Operations Research Letters, 35(6):731–738, 2007.

HRomisch09

Holger Heitsch and Werner Römisch. Scenario tree modeling for multistage stochastic programs. Mathematical Programming, 118:371–406, 2009.

HRomischS06

Holger Heitsch, Werner Römisch, and Cyrille Strugarek. Stability of multistage stochastic programs. SIAM Journal on Optimization, 17(2):511–525, 2006.

HoylandKW03

Kjetil Høyland, Michal Kaut, and Stein W Wallace. A heuristic for moment-matching scenario generation. Computational optimization and applications, 24:169–185, 2003.

HoylandW01

Kjetil Høyland and Stein W Wallace. Generating scenario trees for multistage decision problems. Management science, 47(2):295–307, 2001.

Kau21

Michal Kaut. Scenario generation by selection from historical data. Computational Management Science, 18(3):411–429, 2021.

Lohndorf16

Nils Löhndorf. An empirical analysis of scenario generation methods for stochastic optimization. European Journal of Operational Research, 255(1):121–132, 2016.

NS07

Arkadi Nemirovski and Alexander Shapiro. Convex approximations of chance constrained programs. SIAM Journal on Optimization, 17(4):969–996, 2007.

ONBH16

Fabricio Oliveira, Paula M Nunes, Rosa Blajberg, and Silvio Hamacher. A framework for crude oil scheduling in an integrated terminal-refinery system under supply uncertainty. European Journal of Operational Research, 252(2):635–645, 2016.

OH12

Fabrício Oliveira and Silvio Hamacher. Optimization of the petroleum product supply chain under uncertainty: a case study in northern brazil. Industrial & Engineering Chemistry Research, 51(11):4279–4287, 2012.

Pfl01

G Ch Pflug. Scenario tree generation for multiperiod financial optimization by optimal discretization. Mathematical programming, 89:251–271, 2001.

Roc07

R Tyrrell Rockafellar. Coherent approaches to risk in optimization under uncertainty. In OR Tools and Applications: Glimpses of Future Technologies, pages 38–61. Informs, 2007.

Romisch03

Werner Römisch. Stability of stochastic programming problems. Handbooks in operations research and management science, 10:483–554, 2003.

Sch00

Rüdiger Schultz. Some aspects of stability in stochastic programming. Annals of Operations Research, 100:55–84, 2000.

Sha11

Alexander Shapiro. Analysis of stochastic dual dynamic programming method. European Journal of Operational Research, 209(1):63–72, 2011. URL: https://www.sciencedirect.com/science/article/pii/S0377221710005448, doi:https://doi.org/10.1016/j.ejor.2010.08.007.

SHdM98

Alexander Shapiro and Tito Homem-de-Mello. A simulation-based approach to two-stage stochastic programming with recourse. Mathematical Programming, 81(3):301–325, 1998.