Optimization of system reliability in multi-factory production networks by maintenance approach

Maintaining the reliability of a system is one of the most critical and challenging tasks for factories during production. A reliable system is not only significant for improving a system's productivity and products quality, but worth even more in multi-factory production because the failure of one entity may induce a vigorous chain reaction to the others. Maintaining the reliability in an acceptable level requires an optimal maintenance strategy and planning for each entity in the network. The objective of this paper is to propose a double tier genetic algorithm approach for multi-factory production networks, aiming to keep the system's reliability in a defined acceptable level, and minimize the makespan of the jobs. The optimization algorithm simultaneously schedules perfect and imperfect maintenance during the process of distributed scheduling. The optimization reliability of the proposed algorithm is demonstrated through three numerical examples, including its ability to maintain the system's reliability in a defined acceptable level, the relationship of the acceptance level to production scheduling, and that of the machine age reduction factors to production scheduling.