why using gurobi solver in loop could cause increase the optimisation time.
回答済みi was solving a optimisation problem based on sequential case. I have run the model for 24 times (24 time steps in my model and I have to obtain the optimal solution for each time step). Previously I just manually did that, e.g., run the model at one timestep and obtain the results, then move to the next stop. Now I believe my model is fine and add a loop to do it automatically.
Everything looks okay, but the solution time seems increased and it gave me something like this:
MIP start from previous solve produced solution with objective -4442.69 (148.07s)
MIP start from previous solve produced solution with objective -6599.13 (189.22s)
Looking forward to your significant answer
-
正式なコメント
This post is more than two years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum, or try Gurobot, our chatbot interface offering instant, expert-level support. -
Could you please provide more information about how exactly you execute the loop and what you did before. A small code example would be best, cf. minimal reproducible example. In particular what do you change in the loop, i.e., how do you modify your model(s) at each iteration. Without the additional information, one could only start guessing what might have gone wrong.
0 -
Many thanks for the reply.
I have went through some documents which mentioned, the reason might be the MIP. By using an automated solve loop, this may cause Gurobi to reuse older heuristic solutions, leading to increased solve time.
So it would be excellent if the model can be reset to the initial status after each running. But I have no idea how to implement this.
0 -
Hi Cass,
Model.reset() sounds like what you are looking for.- Riley
0 -
Hi, Riley, many thanks for this. It indeed solved my trouble. I put it in the wrong location before and that was why I got some issues.
Best
0
投稿コメントは受け付けていません。
コメント
5件のコメント