JaimeSotomayor94

My company is developing a big model that simulates the arrival of passengers to an airport. It is important to us to control the randomness of the model for two reasons: - We want to be able to replicate the outputs of an experiment if the changes had not been significant. - When running different scenarios where only part of the configuration has changed, the part of the model that has not changed should remain the same. It is possible to control the random generated number of the distributions that Simio offers, but we hadn’t been able to fix the randomness in the next cases: - When using the function RandomRow. Any change in the model that modifies the running time when the function is called changes the row selected. - When selecting a path from its weight. As well as with the RandomRow function, we have seen that only moving one meter the node that is connected to more than one path, the randomness of the path selected varies. Furthermore, because of the dimensions of the simulation, some processes take time to end. That means that in some cases when we add more data to the input tables, data that is independent to a previous part of the simulation, everything changes because the process takes a little more time to end and that changes the result of the random decisions. We would like to know if it is possible to control the randomness on this events, or what other alternative can we use. There is a mini model attached that shows the described behavior. It consist on a source that generates 100 entities, and two possible paths with the same weight. If the node that connects to both path is in the center of the facility, 45 entities go up and 55 down, like in the next picture. If the node is moved to another location, the number of entities that go up changes to 48. Finally, adding a process (with a loop that doesn’t do anything) before selecting the path, the number of entities varies again to 47. Randomness_example.spfx

And also, not only do I want to select one randomly, but taking into account one column that would have the weight. For example, 80% of "Type A" that are "Good" goes to "Server_1" and 20% to Server_2. Basically use RandomRow to a table filtering for more than 1 foreign key

Thanks for the response. I will use nodes instead of stations. But your solution doesn´t cover my problem completely. That list where you are picking nodes randomly is only filtering for types of entity A or B. I want also to take into account other columns, like the quality one. The problem is when you know that your entity is of type A and good, then is when I would like to select randomly only one of the possible destinations.

In the attached model, I want to transfer entities to stations depending on some criteria defined in a table. I have 2 types of entities, "A" and "B", defined in a table as primary keys. Also I have 2 states, "Good" or "Bad", also in a table as primary key. Finally I have 3 stations. I have a table that has 3 columns, “Entity_Type”, “Quality_Type” and “Station”. For example a row could be "A"; "Good"; "Station 1". It is possible for entity "A" with "Good" quality to end up in either "Station 1" or "Station 2", and in that case I want to randomly select one. The problem is that I cannot use a RandomRow based in more than one criteria. Either I use RandomRow for the rows of the table that are type "A" or quality "Good", but I am not finding a way to do more than one combination. This model is a simplification of the real model which is more complex and has hundreds of combinations. Model_RandomRow_FilteredTable.spfx