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.
Start by clearly defining the problem you want to solve. This will help ChatGPT generate relevant ideas. For example:
Use ChatGPT to brainstorm potential AMPL models. Here are some example prompts:
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.”
Break down the model into components:
Prompt ChatGPT to help write these components.
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.
Ensure you have Streamlit installed. You can install it using pip:
bash
Create a simple Streamlit app to input model parameters and visualize results.
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)
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.
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