Salvato in:
| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2303.02024 |
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Sommario:
- We consider solving stochastic programs over an infinite horizon. By leveraging the stationarity of problem, we develop a novel continually-exploring infinite-horizon explorative dual dynamic programming (CE-Inf-EDDP) algorithm that matches state-of-the-art complexity while providing encouraging numerical performance on the newsvendor and hydrothermal planning problem. CE-Inf-EDDP conceptually differs from previous dual dynamic programming approaches by exploring the feasible region longer and updating the cutting-plane model more frequently. In addition, our algorithm can handle both simple linear to more complex nonlinear costs. To demonstrate this, we extend our algorithm to handle the so-called hierarchical stationary stochastic program, where the cost function is a parametric multi-stage stochastic program. The hierarchical program can model problems with a hierarchy of decision-making, e.g., how long-term decisions influence day-to-day operations. As a concrete example, we introduce a newsvendor problem that includes a second-stage multi-product assembly serving as a secondary market.