MAPLE: Memory-Augmented Planning with Language and Evolution

2026-09-10

AI Summary

为解决动态运营中优化问题的快速适应，MAPLE通过结合自然语言处理、数学编程和进化搜索，并保留先前决策及解，以连续自然语言请求维护优化问题。

Abstract

Domain practitioners understand their business constraints but may lack operations-research expertise or dedicated support. LLM-based optimization agents translate natural-language requirements into models or solver programs that established optimization tools can execute. This progress makes optimization more accessible, but real-world operations are dynamic: changing demand, resources, and priorities require updates to data, constraints, and objectives. Methods centered on isolated requests offer limited support for rapid adaptation that preserves earlier decisions and reuses useful search results. We introduce MAPLE (Memory-Augmented Planning with Language and Evolution), an agent for maintaining optimization problems through successive natural-language requests. MAPLE combines language-based problem construction with mathematical programming and evolutionary search. It retains the optimization program, accepted plans, earlier updates, and candidate solutions for subsequent requests. We introduce NLDO, a benchmark of 15 trajectories and 180 updates spanning selection, scheduling, rostering, routing, and cloud-resource placement. In the main evaluation, MAPLE completes all trajectories and achieves online scalar quality of 0.951 and a Pareto hypervolume ratio of 0.875. Controlled comparisons further show that maintaining executable state improves update validity and can preserve useful search information across substantial revisions.

Problem

• dynamic operations
• natural-language requests
• optimization problem maintenance
• search result reuse

Innovation

• Memory-Augmented Planning
• Natural-Language Requests
• Evolutionary Search
• Optimization Problem Maintenance
• Dynamic Adaptation