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Mathematical Problems in Engineering
Volume 2016, Article ID 3728934, 11 pages
Research Article

Piecewise Linear Model for Multiskilled Workforce Scheduling Problems considering Learning Effect and Project Quality

College of Information Science and Engineering, Northeastern University, No. 3-11, Wenhua Road, Heping District, Shenyang 110819, China

Received 27 October 2015; Revised 11 January 2016; Accepted 27 January 2016

Academic Editor: Nazrul Islam

Copyright © 2016 Shujin Qin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Workforce scheduling is an important and common task for projects with high labour intensities. It becomes particularly complex when employees have multiple skills and the employees’ productivity changes along with their learning of knowledge according to the tasks they are assigned to. Till now, in this context, only little work has considered the minimum quality limit of tasks and the quality learning effect. In this research, the workforce scheduling model is developed for assigning tasks to multiskilled workforce by considering learning of knowledge and requirements of project quality. By using piecewise linearization to learning curve, the mixed 0-1 nonlinear programming model (MNLP) is transformed into a mixed 0-1 linear programming model (MLP). After that, the MLP model is further improved by taking account of the upper bound of employees’ experiences accumulation, and the stable performance of mature employees. Computational experiments are provided using randomly generated instances based on the investigation of a software company. The results demonstrate that the proposed MLPs can precisely approach the original MNLP model but can be calculated in much less time.