Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application
arXiv:2606.25362v1 Announce Type: cross Abstract: Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose consequences propagate into future decisions. In many practical contexts, these programs require obtaining solutions rapidly as new information becomes available. These problems can be represented through scenario approximations to be solved by off-the-shelf optimization solvers, which achieve high decision quality offline but typically run in seconds to minutes per instance, falling short of the sub-s
The increasing complexity of real-time decision systems and the demand for rapid, autonomous action are driving the need for more efficient optimization methods beyond traditional solvers.
This research addresses a critical bottleneck in the deployment of AI in dynamic, high-stakes environments, enabling faster and more effective decision-making in systems like supply chain and logistics.
The ability to generate near-instantaneous, high-quality decisions in complex stochastic environments, reducing reliance on long-running optimization processes and increasing the autonomy of AI systems.
- · Logistics and supply chain companies
- · Autonomous system developers
- · AI software providers
- · E-commerce platforms
- · Traditional optimization software vendors
- · Companies with slow decision-making processes
Companies will be able to optimize complex operations in real-time, leading to significant efficiency gains.
This acceleration of decision-making could enable more robust and responsive AI agents in various industrial settings.
The integration of such proxies might underpin fully autonomous, self-optimizing industrial ecosystems, further collapsing human oversight needs.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG