Hi Zhiwei,

Thanks for sharing your models.

I can see some minor differences between the two CPO files.

I suppose these ".cpo" files are export of models created using the docplex python library.

In the ".cpo" files, the line numbers in the source python files are also provided. It would be interesting to understand the cause of these minor differences.

Unless there is an issue in docplex, the exported ".cpo" files should be identical.

However, in the model building script, if some python data structures are used that do not maintain order when iterating over them, this may explain these minor differences.

If decision variables or constraints are not declared in the same order, this may impact search (decision variables will not be explored in the same order, for instance) and may explain the difference in behavior.

I also did some experiments with your models using the "runseeds" command available in the cpoptimizer interactive tool. This tool executes the search several times using different random seeds and computes some statistics.

I set a maximum timelimit of 120 seconds (as solve could last much longer for some values of the random seed) and I did notice that CPO can spend a long time to prove optimality from time to time (about 1 out of 10 runs).

Both versions of your model behaved similarly. Here is the output of "runseeds" for one model:

```
CP-Optimizer> tools runseeds 10
Benchmarking current problem on 10 runs...
Run Soln Proof Branches Time (s) Objective
--------------------------------------------------------------------
1 1 1 147705 6.63 12398
2 1 1 145786 3.04 12398
3 1 0 5000098 122.61 12398
4 1 1 130861 2.87 12398
5 1 1 144804 3.31 12398
6 1 1 153022 3.37 12398
7 1 1 291866 6.67 12398
8 1 1 315254 14.16 12398
9 1 1 161426 8.85 12398
10 1 1 269966 14.58 12398
--------------------------------------------------------------------
Statistics on runs with proof (9 out of 10):
Mean 1.00 1.00 195632 7.05 12398
Std dev 73860 4.64 0
Geomean 184718 5.99
Min 130861 2.87 12398
Max 315254 14.58 12398
--------------------------------------------------------------------
Statistics on runs stopped by limit (1 out of 10):
Mean 1.00 0.00 5000098 122.61 12398
Std dev 0 0.00 0
Geomean 5000098 122.61
Min 5000098 122.61 12398
Max 5000098 122.61 12398
```

It might be useful to investigate some parameter settings to mitigate this behavior. May be by selecting a specific SearchType... I haven't tested that.

Best regards,

------------------------------

Hugues Juille

------------------------------

Original Message:

Sent: Tue October 10, 2023 10:12 AM

From: Zhiwei Feng

Subject: performance tuning for cplex cp optimizer to get faster convergency

please ignore above attached. use the simplified files instead

------------------------------

Zhiwei Feng

Original Message:

Sent: Mon October 09, 2023 04:46 AM

From: Hugues Juille

Subject: performance tuning for cplex cp optimizer to get faster convergency

Zhiwei,

If the random seed is identical, one should get the same result (assuming the number of workers is identical).

Would it be possible to share a model that illustrates the behavior that you are observing ?

Thanks,

------------------------------

Hugues Juille

Original Message:

Sent: Sat October 07, 2023 09:07 PM

From: Zhiwei Feng

Subject: performance tuning for cplex cp optimizer to get faster convergency

I also observed the randomness on the solution. After get the current solution(actually optimal), sometimes it quickly prove it is optimal sometimes it takes very long time to prove(best bound = current bound)

------------------------------

Zhiwei Feng

Original Message:

Sent: Fri October 06, 2023 10:25 PM

From: Zhiwei Feng

Subject: performance tuning for cplex cp optimizer to get faster convergency

I observed the cp optimizer can get i feasible solution very faste(most time with high quality). However, it takes much more time to prove its optimality. For example, it tries to converge the best bound until it meets the current bound. Sometimes, it seems it keeps branching to prove optimality until meets time limits even though it actually got the optimal solution. Any tips to help improve this? Any parameter like failure-direct-search or search-phase can help? Thanks in advance!

------------------------------

Zhiwei Feng

------------------------------