Anylogic为模拟实验的每次迭代导入新的Excel文件-anylogic帮助代码不起作用

Question

In my anylogic model my agents receive their parameters from a database table, which is based on an Excel file. In the Excel file, each cell has its own code stored, so each time I open the file, the cell values change.

I would like to have that with each automatic run of my model the Excel file is read in again as the database table (ie the parameter values of the agents change). In the " Parameter Variation Experiment " I entered this code under " after iteration ":

String tempString = excelFile_DatabasisLinks.getCellStringValue(1, rowCounter, 1);
ModelDatabase modelDB = getEngine().getModelDatabase();
Database myNewFile = new Database(this, "rohdaten2", tempString);
modelDB.importFromExternalDB(myNewFile.getConnection(), "Rohdaten", "rohdaten", true, false);
rowCounter += 1;

I have the code form this anylogic help page. Using a variable to be able to change the path of the file (ie the file) seems to work (anylogic doesn't throw an error).

In the currently used dummy model, the agents receive their parameters at the source.
At the sink, the parameter values are written via collections into another excel (results) file.

I put obvious pattern into my data files, to see if the data changes, but I always receive the same excel file in my results file.
I read that anylogic copies the excel tables to its temporary files to make simulation runs faster. I hoped the code above would be a workaround, but it is not.
I'm grateful for any suggestions how to make this work!

Answer 1

I could not find out what is wrong with the above code or how to get it work.
However, I found a workaround using the "excelFile "-Block.
In the main agent (the agent where all other agents live) in the agent actions in "on startup":

excelFile.readFile();

ensures that the excel file is updated before each run. The parameters are added via

agent.set_<parametername>(excelFile.getCellBooleanValue( sheet number, row number, column number)

"on exit" in the source.
I hope this helps everyone with a similar problem.