Joint optimization of production scheduling and machine group preventive maintenance. (February 2016)

• Record Type: Journal Article
• Authors: Xiao, Lei; Song, Sanling; Chen, Xiaohui; Coit, David W.
• Is Part Of: Reliability engineering & system safety. Volume 146(2016:Feb.)
• Page Start: 68
• Page End: 78
• Publication Date: 2016-02
• Subjects: Preventive maintenance -- Scheduling -- Random keys -- Genetic algorithms; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2015.10.013
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: Joint optimization models were developed combining group preventive maintenance of a series system and production scheduling. In this paper, we propose a joint optimization model to minimize the total cost including production cost, preventive maintenance cost, minimal repair cost for unexpected failures and tardiness cost. The total cost depends on both the production process and the machine maintenance plan associated with reliability. For the problems addressed in this research, any machine unavailability leads to system downtime. Therefore, it is important to optimize the preventive maintenance of machines because their performance impacts the collective production processing associated with all machines. Too lengthy preventive maintenance intervals may be associated with low scheduled machine maintenance cost, but may incur expensive costs for unplanned failure due to low machine reliability. Alternatively, too frequent preventive maintenance activities may achieve the desired high reliability machines, but unacceptably high scheduled maintenance cost. Additionally, product scheduling plans affect tardiness and maintenance cost. Two results are obtained when solving the problem; the optimal group preventive maintenance interval for machines, and the assignment of each job, including the corresponding start time and completion time. To solve this non-deterministic polynomial-time problem, random keys genetic algorithms are used, and a numerical example is solved to illustrate the proposed model. Highlights: Group preventive maintenance (PM) planning and production scheduling are jointed. Maintenance interval and assignment of jobs are decided by minimizing total cost. Relationships among system age, PM, job processing time are quantified. Random keys genetic algorithms (GA) are used to solve the NP-hard problem. Random keys GA and Particle Swarm Optimization (PSO) are compared.