SMARTAPS: Tool-augmented LLMs for Operations Management
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Large language models (LLMs) present intriguing opportunities to enhance user interaction with traditional algorithms and tools in real-world applications. An advanced planning system (APS) is a sophisticated software that leverages optimization to help operations planners create, interpret, and modify an operational plan. While highly beneficial, many customers are priced out of using an APS due to the ongoing costs of consultants responsible for customization and maintenance. To address the need for a more accessible APS expressed by supply chain planners, we present SmartAPS, a conversational system built on a tool-augmented LLM. Our system provides operations planners with an intuitive natural language chat interface, allowing them to query information, perform counterfactual reasoning, receive recommendations, and execute scenario analysis to better manage their operation. A short video demonstrating the system has been released: https://youtu.be/KtIrJjlDbyw
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 6 of its 16 references that are in the map. First posted on arXiv 23 Jul 2025.
Builds on
6 of 16 references.
References · 6
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
- 2023NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptionscited by 135
- 2022Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptionscited by 54
- 2019Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehensioncited by 51
- 2021Artificial Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challengescited by 5
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- 2025Large Language Models for Planning: A Comprehensive and Systematic Survey
- 2025Ask, Clarify, Optimize: Human-LLM Agent Collaboration for Smarter Inventory Control
- 2025SMaRT: Select, Mix, and ReinvenT -- A Strategy Fusion Framework for LLM-Driven Reasoning and Planning
- 2025Toward PDDL Planning Copilot
- 2024Smart Language Agents in Real-World Planning
- 2025Language Model as Planner and Formalizer under Constraints
- 2025AIM-Bench: Evaluating Decision-making Biases of Agentic LLM as Inventory Manager
- 2025Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant