Project acceleration using temporary workers with heterogeneous efficiency: optimal and best recruitment policies

ABSTRACT

This paper investigates recruitment decisions of temporary work with several levels of experience, from beginners to experts (heterogeneous efficiency) to accelerate a manufacturing project with the objective of labour cost minimisation. A Mixed Integer Linear Program formulation of the problem is developed to find the optimal pool of temporary work of each experience level so as to minimise the cost subject to a desired deadline. The deadline is decreased decrementally in order to identify any potential patterns of recruitment along the reduction in the project duration. A variety of projects, efficiency levels with respect to years of experience and cost scenarios are considered in an experimental framework. Simulation results show a high sensitivity of the optimal pool of temporary work to the project type, efficiency levels and costs. A worst-case analysis suggests that using solely expert temporary workers might be the best recruiting policy to accelerate a project, whatever the desired deadline.

KEYWORDS:

• Project scheduling
• acceleration
• temporary work
• heterogeneous efficiency
• work experience
• SDG 9: Industry
• innovation and infrastructure

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The author confirms that the data supporting the findings of this study are available within the article. Detailed data are available upon request to the author.

Notes

1 Data are available from the author upon request.

Additional information

Notes on contributors

Jully Jeunet

Jully Jeunet is a Research fellow at the The French National Centre for Scientific Research (CNRS) since 2000 and affiliated to the Laboratory for Analysis and Modeling Systems for Decision Support (LAMSADE), University Paris Dauphine. She received her Ph.D. in Operations Management in 1997 from Louis Pasteur University in Strasbourg where she first held a position of assistant professor in management science. Her main research interests are operational research, discrete optimisation, simulation, metaheuristics, planning and scheduling in manufacturing and services.