Replies posted by Jean
@Marcel Hunting Dear Marcel; many thanks for your time and your advise. I will follow your advice step-by-step and will use the Tuning tool. I have used “Piecewise linear approximation” within an MIP. When I remove this part the model perform much better and the larger instance is solved to optimality (exactly same solution obtained by heuristic). Anyhow thanks again for your support and helps on this issue. Regards, Jean
Hi Marcel, I is possible that the discrepancy between the results obtained from CPLEX and my heuristic for the problem that I am investigating be related to my formulation. Here is my reason: As mentioned above after examining the CPLEX solution (for a larger instance) I realise that for a binary variable AIMMS is not picking up the right value that result in lower objective function value. I reformulate that part of the model. The new formulation become harder to solve even for small instances. For larger instance (I tested earlier), solver terminated after 44948 seconds and reported the best LP (1623792848) and best solution (2789389756). The CPLEX best solution cost is reasonable now and it is in line with and close to what heuristic has found (2779810667). The optimality Gap is quite large (41%) and I need to get the optimal solution for this problem instance and compare it to heuristic result to confirm correctness of the formulation . Question (1): Is there any way performance
Hi Marcel, The solution provided by the heuristic need to be transformed into a solution similar to that generate by AIMMS I guess. So I manually calculated the values of the key decision variables (the binary ones) and emailed it to support. Hope this would be enough to build the “heuristic solution”. Many thanks.
Further information… I examined the solution returned by the CPLEX for a larger instance. The model I am trying to solve, among many other decision variables, has a binary variable, let say Xr where sum Xr=1 for r=1,2,3,4. In the CPLEX final solution (supposedly optimal solution), X3=1. However if X4=1 then a less expensive solution is obtained. Don’t understand why CPLEX is unable to find and use X4=1. Other factors may also contribute to this problem I am sharing with you here.
Thanks for your reply. “Did you observe that CPLEX returned a sub-optimal solution for your MIP model (for a larger instance)?” This is exactly what I meant. I checked the solution given by CPLEX and compared it with what the solution obtained (for the same large problem instance) by a heuristic. The value of the obj function (returned) by CPLEX is larger than that from the heuristic!.
Thank you both again for your input. I figure it out. The problem was about updating the perimeters' values. Because the two parameters used in the model have "three" indices of let say i,j and k then for each k we have a matrix so all the values in all the matrices for different value of k (k=1 to n) must be updated. I just updated the first matrix for k=1 that's why I got the same results no matter the value of the parameters in the new set; silly me!.
Thank you for your reply. Let me give you a little bit more information. Based on what I observed from running the AIMMS and heuristic for different parameter sets,it seems that at least FOR TWO specific parameters, AIMMS is using something like, [b]default values[/b] saved from past runs and the new values for these two parameters (in a new case or changed within the same case) are completely ignored/not accessible by/to AIMMS. I tested this by using the solution found by AIMMS (REPEATEDLY) as an input into the heuristic algorithm. With "specific" values for these two parameters, algorithm returns the SAME opt solution returned (repeatedly) by AIMMS-no problem. But when I use another set of values for these two parameters in the heuristic, heuristic algorithm returns another solution (which make more sense given the new values for the two parameters); for AIMMS, changing the value of these two parameters make no difference as explained above. Thanks,
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