Hey there! Let's dig into troubleshooting your optimization model issue. First, let's organize the task time data you've provided clearly to make sure we're on the same page:

• Alpha:
  • Milling: 12 minutes
  • Inspecting: 5 minutes
  • Drilling: 10 minutes
• Bravo:
  • Milling: 10 minutes
  • Inspecting: 4 minutes
  • Drilling: 8 minutes
• Charlie:
  • Milling: 8 minutes
  • Inspecting: 4 minutes
  • Drilling: 16 minutes

Since you mentioned the model is throwing exceptions but didn't share specific details (like error messages, unexpected results, or solver failures), here are some targeted troubleshooting steps to start with:

1. Validate Constraint Logic #

• Double-check if your constraints align with your actual requirements:
  • Are you enforcing that each enterprise must complete all three tasks? Or can tasks be split/assigned to different enterprises?
  • Do you have any conflicting constraints (e.g., forcing a total time limit that's mathematically impossible to meet with the given task times)?
• Even small mismatches here can lead to infeasible models or nonsensical outputs.

2. Confirm Your Objective Function #

• Make sure your optimization goal is clearly and correctly defined:
  • Are you trying to minimize total combined time across all enterprises? Minimize the longest individual enterprise's completion time? Or optimize for something else (like resource load balance)?
  • For example, if your goal is minimal total time (with task assignment allowed), the optimal split would be Milling to Charlie, Inspecting to Bravo/Charlie, and Drilling to Bravo—totaling 20 minutes. But if your model requires each enterprise to do all three tasks, the logic shifts entirely.

3. Check Data & Tool Setup #

• Verify that you've input the task times correctly: no swapped values, unit mismatches (e.g., accidentally using hours instead of minutes), or missing data points.
• If you're using a solver (like Gurobi, PuLP, or Pyomo), confirm that your model type matches the solver's capabilities (e.g., linear vs. nonlinear models). Nonlinear constraints/objectives will break linear solvers.

4. Share More Details for Precise Help #

To narrow this down further, it'd be super helpful if you could add:

• The specific error message or unexpected behavior you're seeing (e.g., solver returns "infeasible", results are way off from expected)
• A clear breakdown of your model's constraints and objective
• The tool/solver you're using to run the model

That way we can pinpoint exactly where things are going wrong!

内容的提问来源于stack exchange，提问作者nick turner