Assistance with conditional non-equals

Hello,

I ask you for help in identifying the error in conditional non-equals. The model works when equation attributes are .fx, but when I turn them into .l, model does not work.
I’ve tried many different variants, and I can’t find which non-equal equation is not set correctly.

I would really appreciate your help, because I spend a lot of time modeling it and I am loosing my hope.

I attached my model in gams and a picture, about the model. Some text in gams is not in English, but it doesn’t make a difference.

Sincere thank you to the person who will take the time to help me.

Best regards
HUANG fixed version.gms (8.85 KB)
gams1.png

I have no idea what you are asking. The attached GAMS model runs though nicely. I don’t find any .fx, I only find some .l in your code. They have no effect (since a MIP solver usually ignores the starting point if not instructed otherwise). If I turn the .l into .fx I still get a good solution but with a worse objective. Provide some details why you think your model does something wrong.

-Michael

My task is to repeat the model from the article in the program gams. If I set the variables to the .fx, the optimal result is correct, because I told the program what are the optimal solutions. So that the most optimal position of the crane is k=3, what are the optimal combinations (as in the Table 11). Therefore, the results are: ST = 12.0665, MMF = 22.5056 AND DST = 16.79, TT=51,36. Then I need to change it so that the program itself shows what are the optimal solutions, so I have to release the .fx extension for variables and change it to .l. When I do this, the program doesn’t find me the right results, so there’s a error in the conditional non-equals which do restrictions.
I do not know how to correct the non-equal equations, so that the program itself will choose optimal solutions correctly, and the TT will be 51,36.
I attached the model with .l ending of binary variables. If you change it to .fx, it works correctly.

Thank you for the reply and help.
Article Huang.pdf (1.6 MB)

I see. Debugging models is hard. What you need to do is to take be “better” solution you get by just providing the .l (or leave this out because it has no effect) and look at this solution. Explain the solution to yourself and detect the flaws in this solution. Next look at the equation that is supposed to prevent this flawed situation and see why it does not do this. This is a creative process that only you can do because you understand the problem and the model. For some external person this would take a long time and I am afraid you won’t find someone here that would do this time investment (for free).

-Michael

Yes, I agree. I am willing to pay a reasonable price for the service, if anyone is willing to help me.