# \[AMPL 24618\] AMPL Inquiries

**URL:** <https://discuss.ampl.com/t/ampl-24618-ampl-inquiries/508>\
**Category:** Google Group Mirror\
**Created:** [April 19, 2023, 4:41am UTC](https://discuss.ampl.com/t/ampl-24618-ampl-inquiries/508 "2023-04-19T04:41:15Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![18845789723](https://avatars.discourse-cdn.com/v4/letter/1/858c86/32.png) [@18845789723](https://discuss.ampl.com/u/18845789723)\
**Post date:** [April 19, 2023, 4:41am UTC](https://discuss.ampl.com/t/ampl-24618-ampl-inquiries/508/1 "2023-04-19T04:41:15Z")

</div>

Dear AMPL customer servicer：

Hello!I have some questions about AMPL I would like to ask you.My research problem is the problem of assembly shop scheduling.But I had trouble building models with AMPL.Here are my questions：  
The first one，as shown in Figure 1, I want to ask for the maximum value in parentheses, whether there are any functions in AMPL that can directly find the maximum value, or if it needs to be linearized like Figure 2, and whether my linearization is correct …  
Figure 1:

![](data:image/png;base64,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)

Figure 2:

![](data:image/png;base64,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)

Secondly, as shown in Figure 3, how these two constraints should be written in AMPL, should they also be linearized？

Figure 3:  
 ![](data:image/png;base64,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)

The lastly, as shown in Figure 4, my problem is the assembly shop scheduling problem, the final target value is to minimize the maximum completion time, this is calculated in the constraint, how to write this goal in AMPL?

Figure 4:  
 ![](data:image/png;base64,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)

Looking forward to your response！

Thank you！
