Joint parallel-machine scheduling and maintenance planning optimisation with deterioration, unexpected breakdowns, and condition-based maintenance

Abstract

The joint optimisation of production scheduling and maintenance planning can significantly decrease production interruptions (or stoppages) and, simultaneously, improve production stability and enhance the reliability and availability of equipment and machines. This paper studies the joint optimisation of production schedules and CBM plans in a parallel-machine production setting. The machines are subject to deterioration, unexpected breakdowns, and deterioration-based failures. The reliability of the machines is modelled as a multi-state system in which two deterioration thresholds are introduced to initiate maintenance and prevent deterioration-based failures. An integrated optimisation model is proposed to solve this new problem. The proposed model employs Markov chains to formulate machines’ reliability and a matrix-based approach to estimate the expected processing times, energy consumption, and maintenance costs. Then, a mixed-integer programming model is proposed that jointly optimises production schedules and maintenance plans by minimising a weighted sum objective function that includes expected lateness, maintenance, and energy consumption costs. A genetic algorithm (GA) is used to solve the new problem, and extensive computational experiments are performed to test the performance of the proposed GA. The results show the superiority of the proposed GA for all the test problems compared to two well-known metaheuristics.

KEYWORDS:

• Parallel machines
• deterioration
• condition-based maintenance
• unexpected breakdowns
• imperfect repair
• perfect repair
• Markov chains
• genetic algorithm

Disclosure statement

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

Credit authorship contribution statement

Mani Sharifi: Conceptualisation, Methodology, Software, Validation, Formal analysis, Investigation, Visualization, Writing – original draft.

Mageed Ghaleb: Software, Validation, Visualization, Formal analysis, Writing – original draft.

Sharareh Taghipour: Supervision, Project administration, Funding acquisition, Writing – review & editing.

Data availability

The data used to support the findings of this study are included in the article.

Additional information

Funding

This research was undertaken, in part, thanks to funding from the Canada Research Chairs Program.

Notes on contributors

Mani Sharifi

Mani Sharifi is a post-doctoral research fellow at the Department of computer science. He holds a B.Sc. degree from Qazvin Islamic Azad University, an M.Sc. degree from south Tehran branch Islamic Azad University, and a Ph.D. degree from Tehran Research and Science Islamic Azad University in industrial engineering. He was the Managerial editor of the Journal of Optimization in Industrial Engineering. He joined Ryerson University's Department of Mechanical & Industrial Engineering (MIE) in November 2018. His area of interest includes Reliability Engineering, Combinatorial Optimization, Statistical Optimization, as well as Production Scheduling.

Mageed Ghaleb

Dr. Mageed Ghaleb is a post-doctoral research fellow at the Department of Mechanical & Industrial Engineering (MIE). He joined the Reliability, Risk, and Maintenance Research (RRMR) Laboratory in January 2021 as a post-doctoral research fellow. He obtained his Ph.D. in Industrial Engineering from Ryerson University. His Ph.D. research was focused on developing real-time optimization algorithms for production scheduling and maintenance planning problems in advanced manufacturing systems. He has worked on various projects and has obtained broad knowledge in real-time decision-making, production scheduling, reliability engineering, maintenance planning, hybrid optimization algorithms, sustainability management, and machine learning applications in production planning and control.

Sharareh Taghipour

Dr. Sharareh Taghipour is an Associate Professor and the RRMR Lab Director at the Department of Mechanical and Industrial Engineering at Ryerson University. She is a Tier 2 Canada Research Chair in Physical Asset Management. She is an IEEE, IIE, SRE, and ASQ member. She obtained her Ph.D. in Industrial Engineering from the University of Toronto. She received her B.Sc. in Mathematics and Computer Science and her M.Sc. in Industrial Engineering, both from the Sharif University of Technology, Iran.