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operations-research

topic4 events
papersTODAY 04:00 UTC

Compact Policies for Submodular MDPs via LP-Based Submodular Orienteering

A new arXiv paper introduces an approach for deriving strong yet compact action-selection policies in Markov Decision Processes whose value functions are submodular. The method builds on a linear-programming formulation of submodular orienteering, a problem where an agent must reach a set of targets under a budget. The authors argue this yields policies that are both effective and compact, relevant to reinforcement learning and operations research settings where repeated action choice is required.

mdps/reinforcement-learning/optimization/arxiv
papersTODAY 04:00 UTC

Surrogate-Assisted Genetic Programming with Phenotypic Characterisation for Dynamic Scheduling

A new arXiv paper applies genetic programming to dynamic multi-mode resource-constrained project scheduling, where tasks face precedence rules, limited resources, several execution modes, and uncertain durations. The authors add surrogate assistance and phenotypic characterisation to guide the evolutionary search toward promising schedules. The work sits at the intersection of evolutionary computation and operations research rather than commercial AI products.

genetic-programming/scheduling/optimization/research
papersSEP 12 04:00 UTC

MAPLE pairs LLM planning with evolutionary search for optimization modeling

A new arXiv paper introduces MAPLE, a memory-augmented planning method that combines language models with an evolutionary search process. The work targets optimization tasks, where business users often understand their constraints but lack operations-research expertise or dedicated support. It builds on the idea of LLM-based optimization agents that convert natural-language requirements into models or solver programs.

agents/llm/optimization/planning
papersSEP 12 04:00 UTC

arXiv Paper Proposes Solver-Informed Self-Distillation for Operations Research LLMs

A new arXiv preprint introduces a post-training method that uses solver feedback to guide self-distillation, aiming to improve how language models turn natural-language problem descriptions into operations research formulations. The approach is positioned as a way to go beyond training on verified answers alone when bootstrapping such models.

arxiv/self-distillation/operations-research/llm-training