Date of Award

3-10-2019

Publication Type

Master Thesis

Degree Name

M.C.Sc.

Department

Computer Science

First Advisor

Ziad Kobti

Second Advisor

Mehdi Kargar

Keywords

Bi-objective maximization, Cluster Hire, Data Mining, Greedy Algorithm, Productivity, Team Formation

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Abstract

Discovering a group of experts to complete a set of tasks that require various skills is known as Cluster Hire Problem. Each expert has a set of skills which he/she can offer and charges a monetary cost to offer their expertise. We are given a set of projects that need to be completed and on completion of each project, the organization gets a Profit. For performing a subset of given projects, we are given a predetermined budget. This budget is spent on hiring experts. We extend this problem by introducing the productivity and capacity of experts. We want to hire experts that are more productive, and this factor is determined on the basis of their past experience. We also want to make sure that no expert is overworked as it is not possible for a single expert to provide his/her expertise for unlimited times. Our goal is to hire as many experts as possible in which the sum of their hiring costs (i.e., salary) is under the given budget as we are interested to maximize the profit and also maximize the productivity of the group of experts, our problem is a bi-objective optimization problem. To achieve this, we propose two different approaches that maximize our Profit and Productivity.

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