The discrete time break scheduling problem under fatigue and no preemption: solution methods and impact of work regulations

Abstract

We address the discrete time break scheduling problem with no preemption when workers' fatigue impacts their productivity. We propose a Mixed Integer Linear Programming model to solve the one break problem to optimality, using a lexicographic approach where the production amount is maximised first, and then the break length over a discrete time horizon. We develop a Variable Neighbourhood Search algorithm to solve the multiple break problem. In addition to proposing efficient solution methods to the problem, our incentive is to assess the impact on the production amount and on workers' welfare of rest break regulations laid down in legislation or collective agreements. We conducted an extensive simulation study to represent a wide range of workers' profiles defined in terms of fatigability and recovery speed. Simulation results show that regulations slightly affect the production amount whereas they allow for large improvements of workers' welfare as long as breaks are optimised as a second objective. The production amount is also shown to be quite sensitive to the break timing. Finally, multiple breaks can improve the production amount and workers' welfare in many situations, which questions the widespread belief that endowing workers with a single short break would optimise the production amount.

Keywords:

• Optimal production amount and breaks
• fatigue
• no preemption
• rest break regulations

Disclosure statement

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

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article. Additional informations about experiments, code and more detailed data are available upon request to the corresponding author [JJ].

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.

Fabio Salassa

Fabio Salassa graduated as an Industrial Engineer from Politecnico di Torino (Italy) in 2005. In 2011, he received a Ph.D. in Production Systems and Industrial Design again from Politecnico di Torino. From 2012 to 2016, he worked as a research fellow and then, from 2016 to 2021 as an assistant professor at Politecnico di Torino. From 2021 he works as an associate professor of Operations Research at Politecnico di Torino where he also participates in national and European research projects. His main research interests include operations research and in particular combinatorial optimisation. He applies exact and heuristic solution approaches mainly to scheduling and timetabling problems.