Exploiting Counterfactuals for Scalable Stochastic Optimization

  • Stefan Kuhlemann
  • Meinolf SellmannEmail author
  • Kevin TierneyEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11802)


We propose a new framework for decision making under uncertainty to overcome the main drawbacks of current technology: modeling complexity, scenario generation, and scaling limitations. We consider three NP-hard optimization problems: the Stochastic Knapsack Problem (SKP), the Stochastic Shortest Path Problem (SSPP), and the Resource Constrained Project Scheduling Problem (RCPSP) with uncertain job durations, all with recourse. We illustrate how an integration of constraint optimization and machine learning technology can overcome the main practical shortcomings of the current state of the art.



This work is partially supported by Deutsche Forschungsgemeinschaft (DFG) grant 346183302. We thank the Paderborn Center for Parallel Computation (PC\(^2\)) for the use of the OCuLUS cluster.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Bielefeld UniversityBielefeldGermany
  2. 2.GE ResearchNiskayunaUSA
  3. 3.Paderborn UniversityPaderbornGermany

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