Jose peeterson

Hi Mario, Thanks for your reply. I understand the 1st and 2nd bullet points.  For your 3rd point, somehow the results of MOO is different when I define the objectives individually using m.setObject...

Hi Jaromil, I have now resorted to use del_t = 0.25. I have also chosen to use only 2 piecewise objectives. I am also using presolve commented out. Now, I am able to get near real-time performance....

Hi Jaromil, Unfortunately, It does not solve the problem.  You can try to comment m.params.Presolve = 0 and change del_t from 1 to 0.1 It still takes very long to solve. I am afraid I cannot use ad...

I have modified the indicator constraints using m.addGenConstrIndicator() Now, it is choosing the regions correctly. However, If the number of decision variables are large the convergence is too sl...

Hey Tobias, Thanks for your reply.    Now my code looks like the way you mentioned. I want to makesure that the correct objective is chosen so I have written a small test code. When I run my code ...

Hi Tobias, I have written this code following your instruction above. Could you please check if its right? I am now getting Model is Infeasible error.      b1  = m.addVar(vtype=GRB.BINARY, name="b...

Thanks for your reply Matthias. How do I specify disjoint intervals like I> 960*SOC_avg , 480*SOC_avg < I < 960*SOC_avg , 240*SOC_avg < I < 480*SOC_avg , 120*SOC_avg < I < 240*SOC_avg , an...

Hi Jaromil, Thanks so much for your prompt response. :) I found out that the fit is even better if I create more than 2 piecewise objectives like below. if (I <= 120*SOC_avg) if (I > 120*SOC_avg a...

Thank you Maliheh. It works. :)

Thanks Tobias. I have used lb and ub for all my variables. Now 'exp' and 'log' are working as expected. However, the solutions take some time to compute as shown below. Do you think anything is unu...