Abstract Operations Research Modeling Using Natural Language Inputs

Abstract: Operations research (OR) uses mathematical models to enhance decision-making, but developing these models requires expert knowledge and can be time-consuming. Automated mathematical programming (AMP) has emerged to simplify this process, but existing systems have limitations. This paper introduces a novel methodology that uses recent advances in LLM to create and edit OR solutions from non-expert user queries expressed using Natural Language. This reduces the need for domain expertise and the time to formulate a problem. The paper presents an end-to-end pipeline, named NL2OR, that generates solutions to OR problems from natural language input, and shares experimental results on several important OR problems.

• The paper introduces NL2OR, an end-to-end pipeline that converts natural language inputs into formal operations research models.
• The methodology integrates a DSL generator, FORA Builder, automatic solver triage, and report generation to streamline model formulation and execution.
• The experimental evaluation over 30 OR problems demonstrated high model accuracy and efficient editing, promising greater accessibility for non-experts.

Overview of "NL2OR: Solve Complex Operations Research Problems Using Natural Language Inputs"

"NL2OR: Solve Complex Operations Research Problems Using Natural Language Inputs," authored by Junxuan Li et al., introduces a novel methodology addressing the challenges associated with modeling and solving operations research (OR) problems through natural language (NL) inputs. The research demonstrates a significant reduction in the need for domain expertise and time required for formulating complex OR problems, through leveraging recent advancements in LLMs. This paper presents an end-to-end pipeline, NL2OR, which allows non-experts to create and edit OR solutions using natural language queries, demonstrating the potential of LLMs in broadening the accessibility of OR methodologies.

Methodology and Components

The NL2OR pipeline consists of four primary components:

1. Domain Specific Language (DSL) Generator: This component translates natural language queries into a formal representation of OR problems in a Domain Specific Language (DSL). The prompts used to generate the DSL are carefully engineered to improve quality outputs from the LLMs, and the process includes validation, error correction, and syntax verification phases to ensure DSL soundness.
2. Framework for OR Analytics (FORA) Builder: The FORA Builder converts the validated DSL into an executable format. This process primarily involves instantiating entities within the internal FORA library as outlined in the DSL, creating an abstract model that can be used across various solver interfaces.
3. FORA Executor: Once the abstract model and necessary data inputs are provided, the FORA Executor instantiates and solves a concrete model. It selects an appropriate solver based on the problem characteristics, ensuring optimization is achieved efficiently.
4. Report Generator: The Report Generator prepares the output from the FORA Executor into a user-friendly format. It constructs database schemas to facilitate data storage and retrieval, ensuring the generated solution can be easily understood and utilized by the user.

Key Contributions

The paper highlights the following significant contributions:

1. Abstract OR Model Creation and Editing: NL2OR handles abstract model creation and editing using NL queries, significantly simplifying the modeling process for non-experts. This abstraction allows for operations on a model contract that can later be instantiated into multiple concrete models, which is crucial for industrial applications.
2. Automatic Solver Triage: NL2OR includes an automatic solver triage mechanism, a feature that distinguishes it from existing AMP systems. This takes the responsibility of selecting the appropriate solver out of the hands of the user, streamlining the problem-solving process.

Experimental Evaluation

The evaluation of NL2OR involved a rigorous assessment over 30 distinct OR problem instances. Key metrics used in the evaluation include Valid@k, the average number of successful generation attempts to produce valid OR models, and Latency, the average time taken for model generation and execution. Results demonstrate:

• High Model Generation Accuracy: NL2OR consistently generated valid OR models across various problem scenarios, with more sophisticated models such as gpt-4-32k displaying higher accuracy metrics compared to gpt-35-turbo-16k.
• Efficient Model Editing: The system exhibited robust performance in editing existing OR models, facilitating what-if analyses crucial for agile decision-making in dynamic environments.

Implications and Future Prospects

The practical implications of this research are considerable. By lowering the entry barrier to developing and deploying OR models, NL2OR holds the potential to democratize access to OR methodologies, particularly benefiting small and medium-sized enterprises that may lack extensive domain expertise. The theoretical implications suggest a growing intersection between NLP and OR, highlighting opportunities for further exploration.

Future development of NL2OR could involve integrating reinforcement learning techniques to enhance model generation accuracy further and evaluating the system's performance on a broader spectrum of OR problems. Additionally, exploring other LLMs/SLMs may yield insights into optimizing NL2OR’s functionality.

In summary, this paper presents a significant advancement in the field of automated OR problem-solving by leveraging natural language inputs. The NL2OR pipeline shows promising results in reducing the complexity and expertise required for OR model formulation and solution, paving the way for more accessible and efficient decision-making tools.