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Assignment 1
One method of applying self adaptation to our program Emergency Room Flow Manager (ERFM) is by ranking the hospitals based on how busy they are. The program will initially rank the hospitals based on distance, but as data on a hospital’s busyness comes in, the program will re-rank them accordingly. A hospital’s busyness can be identified based on how many people are in queue and the estimated wait time. The re-ranking will take into account how busy the hospitals are relative to each other. Distance is still an important factor for hospital visits, so it will still be taken into account when listing hospitals. This can be implemented by using a weighted ranking. This re-ranking should guide users to less busier hospitals which should mean less people in queue and less time to wait.
Another way to apply software self adaptation to the ERFM is by adjusting the estimated wait time based on how many people are in queue. The amount of time that it takes for the queue to decrease by a certain number of people could be taken on multiple instances and averaged to show a more accurate estimated wait time. The wait time can then be adjusted according to this data. This accuracy is significant for users as hospital visits are important and also meets the requirement better.
Below you can find the high-level architecture we have developed for our application.
Using the Balsamiq Prototyping tool, we have developed 5 Low fidelity prototypes that are displayed below.
For developing a software for the Emergency Room Flow Manager Project, we will be interacting with Django.
Django is a high-level Python web framework, and we will use it to build our website.
