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Jay J avatar image
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Jay J asked Jordan Johnson edited

Variable Lower Bound - Upper Bound Discontinuous

In model
I use pull strategy specific label in processor by pulling item from source. I want to find out the minimum time working machinery. The limitations of the machine some product can not do some machines. EX Product1 can use machine 1, 2, 3, 6, 7, 9, 10 Product2 can use machine 1, 2, 5, 6, 8, 10
I did find that problem OptQuest doesn't allow to specify a not equal to constraint.

example.fsm

Regards

FlexSim 17.2.3
optquestconstraints
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example.fsm (70.0 KiB)
5 |100000

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Jordan Johnson avatar image
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Jordan Johnson answered

It is true that OptQuest does not allow you to make that kind of constraint. However, you can put that kind of constraint directly in the model. I have attached a demo model (machinepartmatching.fsm):

In this model, there are 4 part types, and 3 machine types. Certain machine types can only handle certain part types. I put that information in a global table. There is one row per part type, and one column per machine type. The value indicates whether the machine for that column can handle the product for that row:

According to this table, Machine type 1 can only handle part type 1. Machine 2 can handle part types 1, 2, and 3, and Machine 3 can handle part types 2 and 4.

Each Item has a Type label, as does each processor. The queue pushes items to a list, and the processors pull items from that list. The processors pull with a query, however:

WHERE CanPull

This query references the CanPull field, which I added to the list. Its value is determined according to the table:

This field uses the part type to get the correct row, and the machine type to get the correct column. This allows machines to test each part, essentially asking, "can I do this part?"

As you watch the model run, watch where items go. You can verify that products are only flowing through the correct kind of processors.

In summary, FlexSim is fully equipped to allow you to create complicated rules. In fact, by putting these rules in the model, rather than in the optimization, you actually reduce the search space, decreasing the time the optimizer will spend evaluating infeasible solutions.


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