Two-machine flow shop scheduling integrated with preventive maintenance planning

Abstract

This paper investigates an integrated optimisation problem of production scheduling and preventive maintenance (PM) in a two-machine flow shop with time to failure of each machine subject to a Weibull probability distribution. The objective is to find the optimal job sequence and the optimal PM decisions before each job such that the expected makespan is minimised. To investigate the value of integrated scheduling solution, computational experiments on small-scale problems with different configurations are conducted with total enumeration method, and the results are compared with those of scheduling without maintenance but with machine degradation, and individual job scheduling combined with independent PM planning. Then, for large-scale problems, four genetic algorithm (GA) based heuristics are proposed. The numerical results with several large problem sizes and different configurations indicate the potential benefits of integrated scheduling solution and the results also show that proposed GA-based heuristics are efficient for the integrated problem.

Keywords:

• preventive maintenance
• two-machine flow shop
• production scheduling
• genetic algorithm
• Weibull distribution

Acknowledgements

The authors thank the editor and the anonymous referees for their constructive comments and suggestions that have greatly improved the first version of the paper.

Notes

1. Resumable means that if a job cannot be finished before a maintenance activity, it can continue the processing once the maintenance activity is finished. Non-resumable means a job must be reprocessed fully after the maintenance if its processing is interrupted by the maintenance activity on a machine. Semi-resumable means that if a job is not processed to completion before the machine is stopped for maintenance, an additional set-up time is necessary when the processing is resumed.

Additional information

Funding

This work was supported by the National Science Foundation of China (NSFC) [grant number 71101106], [grant number 71171149], [grant number 71272045]; NSFC major programme [grant number 71090404/71090400]. The first author is also supported by the Fundamental Research Funds for the Central Universities.

Notes on contributors

Shijin Wang

Dr Shijin Wang is currently an associate professor at the Department of Management Science and Engineering, School of Economics and Management, Tongji University, Shanghai, China. His research interests include production planning, scheduling, maintenance and control optimisation. He has published more than 20 papers in refereed journals. He received a BS degree in Industrial Engineering (IE) from Zhejiang University of Technology in China (2002). He received his PhD degree in IE from Shanghai Jiao Tong University in China (2009). He worked at the University of Cincinnati, OH, USA, as a visiting scholar from April 2007 to April 2008.

Ming Liu

Dr Ming Liu is an assistant professor at the Department of Management Science and Engineering, School of Economics and Management, Tongji University, Shanghai, China. His research areas are in scheduling and logistics management. He has published more than 30 papers in refereed journals. He received a BS degree in Management Science & Engineering from Xi'an Jiaotong University in China (2005). He received a PhD degree in IE from Ecole Centrale Paris in France (2009), also a PhD degree in Management Science & Engineering from Xi'an Jiaotong University in China (2010).