Abstract
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.
Highlights
Workforce scheduling is one of the key tasks in modern project management
The results demonstrate that the proposed MLPs can precisely approach the original MNLP model but can be calculated in much less time
This research works on multiskilled workforce scheduling problems considering learning effect and project quality (MSWSP-LE&PQ), which constitute the significance and value of this paper
Summary
Workforce scheduling is one of the key tasks in modern project management. Since human resource costs keep rising, project managers have to pay more attention to workforce scheduling in projects. This research works on multiskilled workforce scheduling problems considering learning effect and project quality (MSWSP-LE&PQ), which constitute the significance and value of this paper. The phenomenon of learning effect arises in work activities where the experiences accumulation improves skill performance of individual employee. Heimerl and Kolisch [23] combined the multiskill and the learning curves together in IT-projects and presented a mixed nonlinear programming model for minimizing the scheduling costs. They solved the nonlinear model by a primal-dual interior filter line search algorithm. A nonlinear model for the MSWSPLE&PQ is put forward through investigating the effect of skill experiences accumulation on productivity promoting and quality improving.
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