MS-E2191 - Graduate Seminar on Operations Research (V) D, Lecture, 9.9.2022-2.12.2022

Topic outline

• Background material
  References (with links)

  1. Alem et al. (2020) - A practical assessment of risk-averse approaches in production lot-sizing problems
     
  2. Ben-Tal et al. (2004) - Adjustable robust solutions of uncertain linear programs
     
  3. Ben-Tal et al. (2009) - Robust optimization
     
  4. Bertsimas and Sim (2004) - The price of robustness
     
  5. Birge and Louveaux (1988) - A multicut algorithm for two-stage stochastic linear programs
     
  6. Birge and Louveaux (1997) - Introduction to Stochastic Programming (Springer series in Operations Research)
  7. Boyd et al. (2011) - Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
     
  8. Carøe and Schultz (1999) - Dual decomposition in stochastic integer programming
  9. Charnes and Coopers (1959) - Chance constrained programming
     
  10. Carpentier et al. (2013) - Long-term management of a hydroelectric multireservoir system under uncertainty using progressive hedging algorithm
      
  11. Conejo et al. (2010) - Decision making under uncertainty in electricity markets 
  12. Dowson (2020) - The policy graph decomposition of multistage stochastic programming problems
      
  13. Dowson and Kapelevich (2021) - SDDP.jl - A Julia Package for Stochastic Dual Dynamic Programming
      
  14. Haneveld et al. (2010) - An ALM model for pension funds using integrated chance constraints
      
  15. Higle (2005) - Stochastic programming - optimization when uncertainty matters
      
  16. Homem-de-Mello and Pagnoncelli (2016) - Risk aversion in multistage stochastic programming -  A modeling and algorithmic perspective
      
  17. Kaut (2021) - Scenario generation by selection from historical data
      
  18. Kaut and Wallace (2003) - Evaluation of scenario-generation methods for stochastic programming
      
  19. Kleywegt et al. (2001) - The sample average approximation method for stochastic discrete optimization
      
  20. Linderoth et al. (2006) - The empirical behavior of sampling methods for stochastic programming
      
  21. Luedtke and Ahmed (2008) - A sample approximation approach for optimization with probabilistic constraints
      
  22. Luedtke et al. (2010) - An integer programming approach for linear programs with probabilistic constraints
      
  23. Lubin et al. (2013) - On parallelizing dual decomposition in stochastic integer programming
      
  24. Mulvey et al. (1994) - Robust optimization of large-scale systems
      
  25. Oliveira and Hamacher (2012) - Optimization of the Petroleum Product Supply Chain under Uncertainty - A Case Study in Northern Brazil
      
  26. Oliveira et al. (2016) - A framework for crude oil scheduling in an integrated terminal-refinery system under supply uncertainty
      
  27. Pereira and Pinto (1991) - Multi-stage stochastic optimization applied to energy planning
      
  28. Rockafellar and Wets (1991) - Scenarios and policy aggregation in optimization under uncertainty
      
  29. Ruszczynski and Shapiro (2003) - Stochastic Programming Models (Ch 1 - ORMS Handbook)
      
  30. Prekopa (2003) - Probabilistic programming (Ch 5 - ORMS Handbook)
      
  31. Santoso. et al. (2005) - A stochastic programming approach for supply chain network design under uncertainty
      
  32. Uryasev and Rockafellar (2003) - Conditional value-at-risk - optimization approach
      
  33. Van Slyke and Wets (1969) - L-shaped linear programs with applications to optimal control and stochastic programming
      
  34. Wai-Kei Mak et al. (1999) - Monte Carlo bounding techniques for determining solution quality in stochastic programs
      
  35. Watson and Woodruff (2011) - Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems
      
  36. Zeng and Zhao (2013) - Solving two-stage robust optimization problems using a column-and-constraint generation method
      

  
  

  
  

  
  

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