Search
Close this search box.

ChatGPT for Model Development

Simplify your optimization process and boost efficiency with AI developed AMPL models

Using ChatGPT prompts to write your models makes it easier than ever for professionals to achieve accurate and efficient results, regardless of their expertise level.

Benefits of Using AI with AMPL

How AI Elevates Your Optimization Models

Ease of Use

AI tools, like ChatGPT, guide you through the process of developing optimization models in AMPL’s language. Whether you’re a seasoned expert or a newcomer, you’ll find the process intuitive and straightforward.

Accuracy

AI provides precise suggestions and automates complex tasks, ensuring that your models are robust and reliable.

Efficiency

Save valuable time and resources by leveraging AI to accelerate your model development process, allowing you to focus on strategic decision-making.

Getting started with ChatGPT for Model Development

Tips for Prompting ChatGPT to Write Accurate Models in AMPL's Language

Developing optimization models has never been easier. With AI tools like ChatGPT, you can quickly generate and refine models in AMPL’s language, even if you’re not an expert in writing models. The AI provides step-by-step guidance, helping you create accurate and effective models effortlessly.

Example Use Case: Imagine you’re tasked with optimizing supply chain logistics. With ChatGPT’s assistance, you can develop a comprehensive model in AMPL’s language, addressing various constraints and objectives. The AI’s intuitive suggestions and corrections ensure your model is both accurate and efficient, ready to implement in real-world scenarios.

  1. Start with a Clear Objective
  • Begin by clearly stating the objective of your optimization model. This helps the AI understand the context and desired outcomes.
  • Example Prompt: “I need to optimize the supply chain logistics for a manufacturing company to minimize costs while meeting demand.”

2. Provide Specific Constraints and Parameters

  • List all relevant constraints, parameters, and variables involved in your model. The more detailed you are, the better the AI can assist you.
  • Example Prompt: “The model should include constraints for production capacity, transportation limits, and inventory levels. The parameters include production costs, transportation costs, and demand at each location.”

3. Define the Decision Variables

  • Clearly identify the decision variables in your model, as this will guide the AI in formulating the optimization problem.
  • Example Prompt: “The decision variables are the number of units produced at each factory, the number of units transported between locations, and the inventory levels at each distribution center.”

4. Specify the Objective Function

  • Explain what you want to optimize, whether it’s minimizing costs, maximizing profits, or achieving some other goal.
  • Example Prompt: “The objective function should minimize the total cost, which includes production, transportation, and holding costs.”

5. [Optional] Provide Data and Initial Values

  • If you have any initial data or values for parameters, include them in your prompt to give the AI a complete picture.
  • Example Prompt: “The production cost per unit is $10, the transportation cost per unit per mile is $0.50, and the holding cost per unit per month is $2. The demand at each location is 1000 units.”

6. Use Natural Language Descriptions

  • Describe the problem in natural language before asking for the code. This helps ensure the AI captures all aspects of the problem correctly.
  • Example Prompt: “I need a model that captures the supply chain from production to distribution, ensuring we don’t exceed production capacity or transportation limits, and all demand is met.”

7. Ask for Specific Components

  • Break down the request into smaller parts if necessary, such as asking for the objective function first, then the constraints, and so on.
  • Example Prompt: “Can you write the objective function to minimize the total cost first?”

8. Request Example Models

  • If you’re unsure how to structure your prompt, ask for an example model with similar characteristics.
  • Example Prompt: “Can you provide an example of an AMPL model for optimizing transportation costs in a supply chain?”

9. Iterate and Refine

  • Don’t hesitate to ask for revisions or clarifications. Iterative prompts can help refine the model.
  • Example Prompt: “The initial model looks good, but I need to add a constraint for maximum inventory levels. Can you help with that?”

10. Highlight Common Pitfalls

  • Mention any common mistakes to avoid in the model to guide the AI away from typical errors.
  • Example Prompt: “Please ensure the model doesn’t allow for negative inventory levels or production beyond capacity.”

Example of a Comprehensive Prompt

Here’s how you can combine these tips into a comprehensive prompt for ChatGPT:

Prompt:

“I need to create an optimization model in AMPL for a supply chain logistics problem. The objective is to minimize the total cost, which includes production, transportation, and holding costs.

  • Decision Variables:

    • xijx_{ij}: Units produced at factory ii and shipped to distribution center jj
    • yjy_j: Inventory levels at distribution center jj
  • Constraints:

    • Production capacity at each factory (max 5000 units)
    • Transportation limits between each factory and distribution center
    • Inventory levels at each distribution center (max 2000 units)
    • Demand at each distribution center must be met (1000 units per center)
    • No negative inventory or production levels
  • Parameters:

    • Production cost per unit: $10
    • Transportation cost per unit per mile: $0.50
    • Holding cost per unit per month: $2

Please write the AMPL model for this problem, including the objective function, decision variables, and constraints.”

Expected Output

Based on the prompt above, ChatGPT should generate an AMPL model that includes all the specified elements. Here’s an example of what you might expect:

ampl
Markup
# Define sets
set FACTORIES;
set DISTRIBUTION_CENTERS;

# Parameters
param production_cost;
param transport_cost{FACTORIES, DISTRIBUTION_CENTERS};
param holding_cost;
param demand{DISTRIBUTION_CENTERS};
param max_production;
param max_inventory;

# Decision Variables
var x{FACTORIES, DISTRIBUTION_CENTERS} >= 0; # Units produced and shipped
var y{DISTRIBUTION_CENTERS} >= 0; # Inventory levels

# Objective Function
minimize Total_Cost:
    sum{i in FACTORIES, j in DISTRIBUTION_CENTERS} (production_cost * x[i, j] + transport_cost[i, j] * x[i, j]) +
    sum{j in DISTRIBUTION_CENTERS} (holding_cost * y[j]);

# Constraints
s.t. Production_Limit {i in FACTORIES}:
    sum{j in DISTRIBUTION_CENTERS} x[i, j] <= max_production;

s.t. Demand_Met {j in DISTRIBUTION_CENTERS}:
    sum{i in FACTORIES} x[i, j] + y[j] = demand[j];

s.t. Inventory_Limit {j in DISTRIBUTION_CENTERS}:
    y[j] <= max_inventory;

s.t. Nonnegative_Inventory {j in DISTRIBUTION_CENTERS}:
    y[j] >= 0;
Book a demo - man talking on zoom to a woman

Expert Consultation Services

Take your optimization models to the next level with our expert consultation services.

AMPL developers are available to verify your models, ensuring they meet the highest standards of accuracy and performance. Additionally, our experts can work with you to customize and expand your models, addressing unique challenges and optimizing outcomes.

Get Started Today

Ready to experience the power of AI-enhanced optimization modeling? Sign up for a free trial of AMPL today and explore the benefits firsthand. For personalized assistance, book a consultation with one of our expert developers. Contact us for more information or any inquiries.