I am working on solving and updating an optimization model iteratively via Gurobi-Python. Namely, first I generate an optimization model, and then I repeatedly solve and update the model (e.g., add variables or constraints). Please note that as the optimization model decomposes into several smaller subproblems, the solution of the optimization model in each iteration can be obtained by solving the smaller problems in parallel.
I have seen the solution suggested on this page:
The issue with this solution is that the optimization model needs to be completely regenerated in each iteration which is not time efficient as the model size grows with iterations and regenerating the model consumes time.
Any suggestion or help is appreciated.
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