Suppose we have an English to French translation task in a company, and there are 100s of workers who are proficient in doing this task, but each worker has its own unique attributes which enables them to do certain translations in a better way than the others (e.g., a translator who is a doctor would do better translations of medical documents). Consider 1000s of documents for translation received per day, and each document takes one day to translate. So, there is a two-fold problem:
- Allocating the tasks to available translators using a queuing model or any other efficient allocation mechanism.
- Learning the worker model based on the past performance of each (e.g., correctness scores, static parameters like skill set, qualifications, experience, etc.) using a machine learning system.
Is there a setup/system/model which solves both of the above problems in one cohesive system? From my reading, this problem calls for the application of the assignment problem, user modelling, queuing, and machine learning optimization. I am searching for existing models or frameworks which integrate all of this, can anyone give me some direction for this task of optimum allocation?