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Jitendra Kumar avatar image
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Jitendra Kumar asked Jitendra Kumar commented

Pull item by matching label of global data table

Hi,

I have a small model, source, queue, processor and exit. Flow items are generated using a schedule and stored at queue, each flow item is having some labels as PART_COL_ID and PART_COL. Now I want to pull the flow items at processor based on a global preference table "GlobalTable1. This table consist of two labels as PART_COL_ID and PART_COL of each flow item.

I will take the first flow item from global table and look for it in queue by matching label PART_COL_ID, if I found match I will send it processor and removed it from GlobalTable1, if not I will look for substitute by matching label PART_COL in queue, even if I not found match, I will keep the flow item as back order and look for next flow item of global table.

Any help, thread will be appreciated. I am attaching my model.

Thank you in advance

Jitendra

pull-item-by-global-table-label-match.fsm

FlexSim 17.0.2
match labelbackorder
5 |100000

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1 Answer

Sam Stubbs avatar image
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Sam Stubbs answered Jitendra Kumar commented

I think if you set up your system with Lists it will work better. If you are unfamiliar with lists I recommend going to the User Manual > Modeling Tools > Lists section and reading up about them. Essentially what you will want to do is push the items to a list when they enter the queue. Also I'd recommend going through the Process Flow tutorials in the manual, there's a chapter there that covers using lists in the Process Flow environment.

Then you'd want to set up some Pull system to pull from the list using query. Your query would look something like this: WHERE PART_COL_ID = Puller.PART_COL_ID.

I tried to modify your example with a simple set up like this. You may need to adjust the model to better suit what you are looking for, but at least it will give you an idea of how to set this up.

pull-item-by-global-table-label-match-1.fsm


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Up to 12 attachments (including images) can be used with a maximum of 23.8 MiB each and 47.7 MiB total.