Simulation-based workforce assignment considering position in a social network

Abstract

The goal of this paper is to propose a novel modeling framework to help project managers devise optimal workforce assignments that consider both short- and long-term aspects of projects that must be completed through a multi-organizational social network. The proposed framework is comprised of an evaluation module and an assignment module. Each time a workforce assignment is performed, the Decision Evolution Procedure of the evaluation module first calculates the position value between each pair of currently available workforce members based on various social networking parameters such as trustworthiness, influence, reputation, and proximity. Second, by using these position values, the Extended Regular Equivalence Evaluation algorithm from the evaluation module computes the regular and structural equivalence values between each pair of workforce members. Finally, the assignment module selects an optimal workforce mix that maximizes both the short-term performance (productivity) as well as the long-term performance (workforce training, and robustness) of the project organizations. Agent-based simulation and multi-objective optimization techniques are leveraged for the evaluation module and the assignment module, respectively. The proposed framework is illustrated and successfully demonstrated using the software enhancement request process in Kuali, a multi-organizational alliance-based software development project involving 12 universities.