Abstract
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.
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This is in contrast to some theoretical results on the SKP that assume we can decide in what order we wish to consider the items [6]. We consider having this freedom less realistic.
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Acknowledgements
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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Kuhlemann, S., Sellmann, M., Tierney, K. (2019). Exploiting Counterfactuals for Scalable Stochastic Optimization. In: Schiex, T., de Givry, S. (eds) Principles and Practice of Constraint Programming. CP 2019. Lecture Notes in Computer Science(), vol 11802. Springer, Cham. https://doi.org/10.1007/978-3-030-30048-7_40
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