Group 19 Copy 2

AMPL Guides

Guide to Using ChatGPT for AMPL Models and Streamlit Apps

Guide to Using ChatGPT for AMPL Models and Streamlit Apps

Introduction

This guide will help you leverage ChatGPT to generate ideas for AMPL models, write these models, and create simple Streamlit apps for visualization. It is designed for data scientists, OR specialists, and students to save time and streamline their workflow.

1. Generating Ideas for AMPL Models with ChatGPT

Step 1: Define Your Problem

Start by clearly defining the problem you want to solve. This will help ChatGPT generate relevant ideas. For example:

  • Supply Chain Optimization: Minimize the total cost of production and transportation.
  • Portfolio Optimization: Maximize return while minimizing risk.

Step 2: Ask ChatGPT for Ideas

Use ChatGPT to brainstorm potential AMPL models. Here are some example prompts:

  • “Can you suggest an AMPL model for optimizing a supply chain network?”
  • “What are some key constraints and objectives for a portfolio optimization model in AMPL?”

Example Interaction

User: “Can you suggest an AMPL model for optimizing a supply chain network?”

ChatGPT: “Sure! An AMPL model for supply chain optimization can include variables for production quantities, transportation flows, and storage levels. Objectives might include minimizing total costs, which include production, transportation, and storage costs. Constraints can enforce production capacities, demand satisfaction, and transportation limits.”

2. Writing AMPL Models with ChatGPT

Step 1: Define Model Components

Break down the model into components:

    • Parameters: Fixed values like costs, capacities, demands.
    • Variables: Decision variables like production quantities, transportation amounts.
    • Objective Function: What you aim to minimize or maximize.
    • Constraints: Equations or inequalities that restrict the values of variables.

Step 2: Use ChatGPT to Write Model Components

Prompt ChatGPT to help write these components.

Example Interaction

User: “Can you write an AMPL model for minimizing transportation costs with given production capacities and demands?”

ChatGPT:

ampl

param n; # number of factories
param m; # number of warehouses
param capacity {1..n}; # production capacity of each factory
param demand {1..m}; # demand of each warehouse
param cost {1..n, 1..m}; # transportation cost from factory to warehouse

var Trans {1..n, 1..m} >= 0; # transportation amounts

minimize TotalCost:
sum {i in 1..n, j in 1..m} cost[i,j] * Trans[i,j];

subject to Capacity {i in 1..n}:
sum {j in 1..m} Trans[i,j] <= capacity[i];

subject to Demand {j in 1..m}:
sum {i in 1..n} Trans[i,j] >= demand[j];

Caution:

Please remember that ChatGPT – although impressive at writing AMPL models and code in general, could be wrong. Be sure to check your work or verify with the AMPL technical team.

3. Creating Streamlit Apps to Visualize AMPL Models

Step 1: Install Streamlit

Ensure you have Streamlit installed. You can install it using pip:

bash

pip install streamlit

Step 2: Develop the Streamlit App

Create a simple Streamlit app to input model parameters and visualize results.

Example Streamlit App

python

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

st.title(“Supply Chain Optimization Model”)

# Input parameters
n = st.number_input(“Number of factories”, min_value=1, value=3)
m = st.number_input(“Number of warehouses”, min_value=1, value=3)

capacity = [st.number_input(f”Capacity of factory {i+1}”, min_value=0) for i in range(n)]
demand = [st.number_input(f”Demand of warehouse {i+1}”, min_value=0) for i in range(m)]
cost = [[st.number_input(f”Cost from factory {i+1} to warehouse {j+1}”, min_value=0) for j in range(m)] for i in range(n)]

if st.button(“Optimize”):
# Call AMPL model here and get results
# This is a placeholder for the actual optimization call
results = {“Trans”: [[0]*m for _ in range(n)], “TotalCost”: 0}

st.write(“Transportation Matrix:”)
df = pd.DataFrame(results[“Trans”], columns=[f”Warehouse {j+1}” for j in range(m)], index=[f”Factory {i+1}” for i in range(n)])
st.write(df)

st.write(f”Total Cost: {results[‘TotalCost’]}”)

# Visualize results
fig, ax = plt.subplots()
ax.matshow(results[“Trans”], cmap=’coolwarm’)
for i in range(n):
for j in range(m):
ax.text(j, i, results[“Trans”][i][j], va=’center’, ha=’center’)
st.pyplot(fig)

Conclusion

By following this guide, you can leverage ChatGPT to generate and write AMPL models, and use Streamlit to visualize them. This approach helps data scientists, OR specialists, and students save time and improve their workflow.

Additional Resources

Table of Contents

Looking to get started with AMPL?

Our team is here to make sure you install and use AMPL to its full potential. 

More Resources

AMPL offers a wealth of resources to help modelers and developers get the most out of the language.