A scenario-embedded neural network with feasibility decoder and composite loss learns to proxy solutions for sequential contextual stochastic programs, achieving 2800x speedup and cost improvements in order fulfillment simulations.
A survey of contextual optimization methods for decision-making under uncertainty
4 Pith papers cite this work. Polarity classification is still indexing.
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A multi-objective probabilistic forecast combination framework is introduced that generates Pareto-optimal combinations balancing forecast accuracy and inventory decision performance, outperforming single-objective methods on retail and spare parts data.
Systematic review of 370 publications classifies uncertainty representation in risk management into probabilistic, evidence-based/fuzzy, qualitative, graphical, and hybrid families, noting limited practical integration.
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.
citing papers explorer
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Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application
A scenario-embedded neural network with feasibility decoder and composite loss learns to proxy solutions for sequential contextual stochastic programs, achieving 2800x speedup and cost improvements in order fulfillment simulations.
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Multi-objective probabilistic forecast combination for inventory demand
A multi-objective probabilistic forecast combination framework is introduced that generates Pareto-optimal combinations balancing forecast accuracy and inventory decision performance, outperforming single-objective methods on retail and spare parts data.
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Methods for Uncertainty Representation in Risk Management: A Comparative Review and Decision-Oriented Framework
Systematic review of 370 publications classifies uncertainty representation in risk management into probabilistic, evidence-based/fuzzy, qualitative, graphical, and hybrid families, noting limited practical integration.
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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.