In the attached model, you'll find a Utilization vs Time chart driven by a Statistics Collector and a Process Flow. This article is an alternative approach to the chart shown here:
https://answers.flexsim.com/content/kbentry/158947/utilization-vs-time-statistics-collector.html
Thanks @Felix Möhlmann for putting that article together. The concept in this model is very similar, but this is a different approach. There are pros and cons to each method. This method offers some performance improvements at the cost of writing more FlexScript code. This method also doesn't handle Warmup time, where the other method does.
The general idea in this model is to keep a history of state changes for the previous hour (or other time interval). The history adds new info as states continue to change and drops old info as it "expires" by being older that the time interval.
I chose to use a Bundle (stored on a token label) for the history. Bundles are optimized to add data to the end. If a bundle is paged (they are by default), then they are also optimized to remove data from the beginning.
[Begin Technical Discussion - TLDR; Bundles are fast at removing the first row and adding new rows]
A paged bundle keeps blocks of memory (pages) for a certain number of rows. If a page fills up, it allocates another page. It keeps track of the location in the page for the next entry. Similarly, it keeps track of the location of the first entry on the start page. If you remove the first row, that start location is moved forward on the page. If the start location gets to the end of a page, that page is dropped and the start location moves to the next page.
[End Technical Discussion]
The Process Flow maintains the history table. Whenever the object's state changes, the Process Flow adds a new entry to the history table. This part is straightforward. The tricky part is properly "expiring" the data. To do this, a second set of tokens wait for the last of the oldest data to expire. When the oldest set of data should expire, the tokens remove any rows that are too old and then wait for the next oldest row. If there are no expired rows, the tokens wait for one "time interval" and then check again. This is because if there is no expired data, data can't expire for at least one time interval. In this way, the history table is always kept up to date.
The Statistics Collector is configured to post-process the history table. It calculates the total utilization, including the state that the object is currently in, as well as the state being "phased out" from the history table. The Statistics Collector can do this calculation at any point in the model. So the sampling interval (called Resolution in the model) is independent from the time window.
