Recommendation system for workers and tasks Recommending the optimal assignment of workers to tasks

Examensarbete för masterexamen

Please use this identifier to cite or link to this item:
Download file(s):
File Description SizeFormat 
251316.pdfFulltext610.02 kBAdobe PDFView/Open
Type: Examensarbete för masterexamen
Master Thesis
Title: Recommendation system for workers and tasks Recommending the optimal assignment of workers to tasks
Authors: Bellevik, Sebastian
Ekman, Philip
Abstract: This thesis tries to solve the problem of matching workers with tasks when unknown parameters are involved. Looking at the trend where outsourcing tasks to previously unknown parties is becoming more common, a need is definitely there to solve this problem in an efficient way. The problem can be described as a list of workers, each with an unknown list of skills, and a list of tasks, each with a known list of requirements. Any method assigning all tasks to workers, while maximizing the reward given for doing so, must be able to accurately estimate the skills of every worker to provide good results. To solve this problem when each worker only has a single skill has been shown to be possible with an algorithm called Bounded Epsilon First. This algorithm is used as a starting point for testing data with single-skill workers and single-requirement tasks, before moving on to multi-skill workers and multi-requirement tasks. No real world data was available for multi-skill matching, which is why all experimentation is done on synthetic data, generated uniformly at random. After the first phase, different matching algorithms and methods of rating worker performance were implemented and tested, producing varying results. Testing all implemented methods on real world data would surely produce interesting results, but overall, the results presented in this thesis show good promise. Our best solution, given time to estimate each worker’s skills, give results approaching 85% of the result produces by matching with all parameters known.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2017
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
Collection:Examensarbeten för masterexamen // Master Theses

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.